RenCoder-Ministral-8B-Instruct-2410

pip install --upgrade vllm

pip install --upgrade mistral_common

from vllm import LLM
from vllm.sampling_params import SamplingParams

model_name = "mistralai/Ministral-8B-Instruct-2410"

sampling_params = SamplingParams(max_tokens=8192)

# note that running Ministral 8B on a single GPU requires 24 GB of GPU RAM
# If you want to divide the GPU requirement over multiple devices, please add *e.g.* `tensor_parallel=2`
llm = LLM(model=model_name, tokenizer_mode="mistral", config_format="mistral", load_format="mistral")

prompt = "Do we need to think for 10 seconds to find the answer of 1 + 1?"

messages = [
    {
        "role": "user",
        "content": prompt
    },
]

outputs = llm.chat(messages, sampling_params=sampling_params)

print(outputs[0].outputs[0].text)
# You don't need to think for 10 seconds to find the answer to 1 + 1. The answer is 2,
# and you can easily add these two numbers in your mind very quickly without any delay.

pip install mistral_inference --upgrade

from huggingface_hub import snapshot_download
from pathlib import Path

mistral_models_path = Path.home().joinpath('mistral_models', '8B-Instruct')
mistral_models_path.mkdir(parents=True, exist_ok=True)

snapshot_download(repo_id="mistralai/Ministral-8B-Instruct-2410", allow_patterns=["params.json", "consolidated.safetensors", "tekken.json"], local_dir=mistral_models_path)

mistral-chat $HOME/mistral_models/8B-Instruct --instruct --max_tokens 256