> ## Documentation Index
> Fetch the complete documentation index at: https://docs.liquid.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Chat Template

> The chat template defines how conversations are structured using special tokens and roles. LFM2 uses a ChatML-like chat template to structure conversations as follows:

```
<|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|>
```

Conversations are formatted using special tokens:

* **`<|startoftext|>`** — Start of the conversation.
* **`<|im_start|>`** — Start of the message. Always followed by the role name (`system`, `user`, `assistant`, or `tool`) and a line break.
* **`<|im_end|>`** — End end of the message.

LFM2 supports four conversation roles:

* **`system`** — (Optional) Defines who the assistant is and how it should respond.
* **`user`** — Messages from the user containing questions and instructions.
* **`assistant`** — Responses from the model.
* **`tool`** — Results from tool/function execution. Used for [tool use](/lfm/key-concepts/tool-use) workflows.

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

## Text Models

We recommend storing your conversation as a list of dictionaries as follows:

```python theme={"theme":{"light":"github-light","dark":"github-dark"}}
messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "What is machine learning?"}
]
```

You can then apply LFM2's chat template using the [`.apply_chat_template()`](https://huggingface.co/docs/transformers/main/en/chat_templating#using-applychattemplate) method from Transformers:

```python theme={"theme":{"light":"github-light","dark":"github-dark"}}
from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("LiquidAI/LFM2.5-1.2B-Instruct")

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

Output:

```
<|startoftext|><|im_start|>system
You are a helpful assistant.<|im_end|>
<|im_start|>user
What is machine learning?<|im_end|>
<|im_start|>assistant
```

## Vision Models

LFM2-VL models follow the same chat template with additional support for images. When formatted, images are represented with a sentinel token (`<image>`), which is automatically replaced with image tokens by the processor.

When creating conversations for vision models, use a structured format with `content` as a list containing image and text entries:

```python theme={"theme":{"light":"github-light","dark":"github-dark"}}
image = load_image("path/to/image.jpg")  # or use PIL Image

messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "image": image},  # PIL Image or loaded image
            {"type": "text", "text": "What is in this image?"},
        ],
    },
]
```

You can then apply the chat template using the processor's `.apply_chat_template()` method:

```python theme={"theme":{"light":"github-light","dark":"github-dark"}}
from transformers import AutoProcessor
from transformers.image_utils import load_image

processor = AutoProcessor.from_pretrained("LiquidAI/LFM2.5-VL-1.6B")

# Apply chat template
prompt = processor.apply_chat_template(
    conversation,
    tokenize=False,
    add_generation_prompt=True
)
print(prompt)
```

Output:

```
<|startoftext|><|im_start|>system
You are a helpful multimodal assistant by Liquid AI.<|im_end|>
<|im_start|>user
<image>What is in this image?<|im_end|>
<|im_start|>assistant
```