Chat Template

LFM2 uses a ChatML-like chat template to structure conversations. Here's an example conversation:

<|startoftext|><|im_start|>system
You are a helpful assistant trained by Liquid AI.<|im_end|>
<|im_start|>user
What is C. elegans?<|im_end|>
<|im_start|>assistant
It's a tiny nematode that lives in temperate soil environments.<|im_end|>

Special Tokens

Conversations are formatted using special tokens:

• <|im_start|> — Marks the beginning of a message. Always followed by the role name (system, user, assistant, or tool) and a line break.
• <|im_end|> — Marks the end of a message.
• <|startoftext|> — Optional token at the beginning of conversations (typically handled automatically).

Roles

LFM2 supports four conversation roles:

• system — (Optional) Sets the assistant's behavior and context. Defines who the assistant is and how it should respond.
• user — Messages from the user containing questions, instructions, or requests.
• assistant — Responses from the model.
• tool — Results from tool/function execution. Used for tool use workflows.

Using the Chat Template

You can automatically apply the chat template using the .apply_chat_template() method from Hugging Face Transformers:

from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("LiquidAI/LFM2-1.2B")

messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "What is machine learning?"}
]

# Apply chat template
prompt = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
print(prompt)

The complete chat template definition can be found in the chat_template.jinja file in each model's Hugging Face repository.

Coming soon...