LFM2-8B-A1B-GGUF

Name: LFM2-8B-A1B-GGUF
Author: unsloth

16.9K

8.0B

9 languages

BF16

—

unsloth

Language Model

OTHER

8B params

Fair

17K downloads

Community-tested

Try on Hugging Face Add to Compare

Edge AI:

Mobile

Laptop

Server

18GB+ RAM

Mobile

Laptop

Server

Quick Summary

> [!NOTE] > Includes Unsloth chat template fixes! For `llama.cpp`, use `--jinja` > Unsloth Dynamic 2.0 achieves superior accuracy & outperforms other leading q...

Device Compatibility

Mobile

4-6GB RAM

Laptop

16GB RAM

Server

GPU

Minimum Recommended

8GB+ RAM

Code Examples

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

🏃 How to run LFM2bash

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

2. vLLMbashvllm

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

pythonvllm

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

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