openchat-3.6-8b-20240522
8.9K
155
llama
by
openchat
Language Model
OTHER
8B params
New
9K downloads
Early-stage
Edge AI:
Mobile
Laptop
Server
18GB+ RAM
Mobile
Laptop
Server
Quick Summary
Advancing Open-source Language Models with Mixed-Quality Data 🏆 The Overall Best Performing Open-source 8B Model 🏆 🚀 Outperforms Llama-3-8B-Instruct and ope...
Device Compatibility
Mobile
4-6GB RAM
Laptop
16GB RAM
Server
GPU
Minimum Recommended
8GB+ RAM
Code Examples
python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Inference using Transformerspythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "openchat/openchat-3.6-8b-20240522"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids,
do_sample=True,
temperature=0.5,
max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))Deploy This Model
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