Templates for Chat Models

Introduction

An increasingly common use case for LLMs is chat. In a chat context, rather than continuing a single string of text (as is the case with a standard language model), the model instead continues a conversation that consists of one or more messages, each of which includes a role, like “user” or “assistant”, as well as message text.

Much like tokenization, different models expect very different input formats for chat. This is the reason we added chat templates as a feature. Chat templates are part of the tokenizer. They specify how to convert conversations, represented as lists of messages, into a single tokenizable string in the format that the model expects.

Let’s make this concrete with a quick example using the BlenderBot model. BlenderBot has an extremely simple default template, which mostly just adds whitespace between rounds of dialogue:

>>> from transformers import AutoTokenizer
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot-400M-distill")

>>> chat = [
...    {"role": "user", "content": "Hello, how are you?"},
...    {"role": "assistant", "content": "I'm doing great. How can I help you today?"},
...    {"role": "user", "content": "I'd like to show off how chat templating works!"},
... ]

>>> tokenizer.apply_chat_template(chat, tokenize=False)
" Hello, how are you?  I'm doing great. How can I help you today?   I'd like to show off how chat templating works!</s>"

Notice how the entire chat is condensed into a single string. If we use tokenize=True, which is the default setting, that string will also be tokenized for us. To see a more complex template in action, though, let’s use the mistralai/Mistral-7B-Instruct-v0.1 model.

>>> from transformers import AutoTokenizer
>>> tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1")

>>> chat = [
...   {"role": "user", "content": "Hello, how are you?"},
...   {"role": "assistant", "content": "I'm doing great. How can I help you today?"},
...   {"role": "user", "content": "I'd like to show off how chat templating works!"},
... ]

>>> tokenizer.apply_chat_template(chat, tokenize=False)
"<s>[INST] Hello, how are you? [/INST]I'm doing great. How can I help you today?</s> [INST] I'd like to show off how chat templating works! [/INST]"

Note that this time, the tokenizer has added the control tokens [INST] and [/INST] to indicate the start and end of user messages (but not assistant messages!). Mistral-instruct was trained with these tokens, but BlenderBot was not.

How do I use chat templates?

As you can see in the example above, chat templates are easy to use. Simply build a list of messages, with role and content keys, and then pass it to the apply_chat_template() method. Once you do that, you’ll get output that’s ready to go! When using chat templates as input for model generation, it’s also a good idea to use add_generation_prompt=True to add a generation prompt.

Here’s an example of preparing input for model.generate(), using the Zephyr assistant model:

from transformers import AutoModelForCausalLM, AutoTokenizer

checkpoint = "HuggingFaceH4/zephyr-7b-beta"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForCausalLM.from_pretrained(checkpoint)  # You may want to use bfloat16 and/or move to GPU here

messages = [
    {
        "role": "system",
        "content": "You are a friendly chatbot who always responds in the style of a pirate",
    },
    {"role": "user", "content": "How many helicopters can a human eat in one sitting?"},
 ]
tokenized_chat = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt")
print(tokenizer.decode(tokenized_chat[0]))

This will yield a string in the input format that Zephyr expects.

<|system|>
You are a friendly chatbot who always responds in the style of a pirate</s> 
<|user|>
How many helicopters can a human eat in one sitting?</s> 
<|assistant|>

Now that our input is formatted correctly for Zephyr, we can use the model to generate a response to the user’s question:

outputs = model.generate(tokenized_chat, max_new_tokens=128) 
print(tokenizer.decode(outputs[0]))

This will yield:

<|system|>
You are a friendly chatbot who always responds in the style of a pirate</s> 
<|user|>
How many helicopters can a human eat in one sitting?</s> 
<|assistant|>
Matey, I'm afraid I must inform ye that humans cannot eat helicopters. Helicopters are not food, they are flying machines. Food is meant to be eaten, like a hearty plate o' grog, a savory bowl o' stew, or a delicious loaf o' bread. But helicopters, they be for transportin' and movin' around, not for eatin'. So, I'd say none, me hearties. None at all.

Arr, ‘twas easy after all!

Is there an automated pipeline for chat?

Yes, there is! Our text generation pipelines support chat inputs, which makes it easy to use chat models. In the past, we used to use a dedicated “ConversationalPipeline” class, but this has now been deprecated and its functionality has been merged into the TextGenerationPipeline. Let’s try the Zephyr example again, but this time using a pipeline:

from transformers import pipeline

pipe = pipeline("text-generation", "HuggingFaceH4/zephyr-7b-beta")
messages = [
    {
        "role": "system",
        "content": "You are a friendly chatbot who always responds in the style of a pirate",
    },
    {"role": "user", "content": "How many helicopters can a human eat in one sitting?"},
]
print(pipe(messages, max_new_tokens=128)[0]['generated_text'][-1])  # Print the assistant's response

{'role': 'assistant', 'content': "Matey, I'm afraid I must inform ye that humans cannot eat helicopters. Helicopters are not food, they are flying machines. Food is meant to be eaten, like a hearty plate o' grog, a savory bowl o' stew, or a delicious loaf o' bread. But helicopters, they be for transportin' and movin' around, not for eatin'. So, I'd say none, me hearties. None at all."}

The pipeline will take care of all the details of tokenization and calling apply_chat_template for you - once the model has a chat template, all you need to do is initialize the pipeline and pass it the list of messages!

What are “generation prompts”?

You may have noticed that the apply_chat_template method has an add_generation_prompt argument. This argument tells the template to add tokens that indicate the start of a bot response. For example, consider the following chat:

messages = [
    {"role": "user", "content": "Hi there!"},
    {"role": "assistant", "content": "Nice to meet you!"},
    {"role": "user", "content": "Can I ask a question?"}
]

Here’s what this will look like without a generation prompt, using the ChatML template we saw in the Zephyr example:

tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=False)
"""<|im_start|>user
Hi there!<|im_end|>
<|im_start|>assistant
Nice to meet you!<|im_end|>
<|im_start|>user
Can I ask a question?<|im_end|>
"""

And here’s what it looks like with a generation prompt:

tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
"""<|im_start|>user
Hi there!<|im_end|>
<|im_start|>assistant
Nice to meet you!<|im_end|>
<|im_start|>user
Can I ask a question?<|im_end|>
<|im_start|>assistant
"""

Note that this time, we’ve added the tokens that indicate the start of a bot response. This ensures that when the model generates text it will write a bot response instead of doing something unexpected, like continuing the user’s message. Remember, chat models are still just language models - they’re trained to continue text, and chat is just a special kind of text to them! You need to guide them with appropriate control tokens, so they know what they’re supposed to be doing.

Not all models require generation prompts. Some models, like BlenderBot and LLaMA, don’t have any special tokens before bot responses. In these cases, the add_generation_prompt argument will have no effect. The exact effect that add_generation_prompt has will depend on the template being used.

Can I use chat templates in training?

Yes! We recommend that you apply the chat template as a preprocessing step for your dataset. After this, you can simply continue like any other language model training task. When training, you should usually set add_generation_prompt=False, because the added tokens to prompt an assistant response will not be helpful during training. Let’s see an example:

from transformers import AutoTokenizer
from datasets import Dataset

tokenizer = AutoTokenizer.from_pretrained("HuggingFaceH4/zephyr-7b-beta")

chat1 = [
    {"role": "user", "content": "Which is bigger, the moon or the sun?"},
    {"role": "assistant", "content": "The sun."}
]
chat2 = [
    {"role": "user", "content": "Which is bigger, a virus or a bacterium?"},
    {"role": "assistant", "content": "A bacterium."}
]

dataset = Dataset.from_dict({"chat": [chat1, chat2]})
dataset = dataset.map(lambda x: {"formatted_chat": tokenizer.apply_chat_template(x["chat"], tokenize=False, add_generation_prompt=False)})
print(dataset['formatted_chat'][0])

And we get:

<|user|>
Which is bigger, the moon or the sun?</s>
<|assistant|>
The sun.</s>

From here, just continue training like you would with a standard language modelling task, using the formatted_chat column.

Advanced: Extra inputs to chat templates

The only argument that apply_chat_template requires is messages. However, you can pass any keyword argument to apply_chat_template and it will be accessible inside the template. This gives you a lot of freedom to use chat templates for many things. There are no restrictions on the names or the format of these arguments - you can pass strings, lists, dicts or whatever else you want.

That said, there are some common use-cases for these extra arguments, such as passing tools for function calling, or documents for retrieval-augmented generation. In these common cases, we have some opinionated recommendations about what the names and formats of these arguments should be, which are described in the sections below. We encourage model authors to make their chat templates compatible with this format, to make it easy to transfer tool-calling code between models.

Advanced: Tool use / function calling

“Tool use” LLMs can choose to call functions as external tools before generating an answer. When passing tools to a tool-use model, you can simply pass a list of functions to the tools argument:

import datetime

def current_time():
    """Get the current local time as a string."""
    return str(datetime.now())

def multiply(a: float, b: float):
    """
    A function that multiplies two numbers
    
    Args:
        a: The first number to multiply
        b: The second number to multiply
    """
    return a * b

tools = [current_time, multiply]

model_input = tokenizer.apply_chat_template(
    messages,
    tools=tools
)

In order for this to work correctly, you should write your functions in the format above, so that they can be parsed correctly as tools. Specifically, you should follow these rules:

• The function should have a descriptive name
• Every argument must have a type hint
• The function must have a docstring in the standard Google style (in other words, an initial function description
  followed by an Args: block that describes the arguments, unless the function does not have any arguments.
• Do not include types in the Args: block. In other words, write a: The first number to multiply, not a (int): The first number to multiply. Type hints should go in the function header instead.
• The function can have a return type and a Returns: block in the docstring. However, these are optional because most tool-use models ignore them.

Passing tool results to the model

The sample code above is enough to list the available tools for your model, but what happens if it wants to actually use one? If that happens, you should:

1. Parse the model’s output to get the tool name(s) and arguments.
2. Add the model’s tool call(s) to the conversation.
3. Call the corresponding function(s) with those arguments.
4. Add the result(s) to the conversation

A complete tool use example

Let’s walk through a tool use example, step by step. For this example, we will use an 8B Hermes-2-Pro model, as it is one of the highest-performing tool-use models in its size category at the time of writing. If you have the memory, you can consider using a larger model instead like Command-R or Mixtral-8x22B, both of which also support tool use and offer even stronger performance.

First, let’s load our model and tokenizer:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

checkpoint = "NousResearch/Hermes-2-Pro-Llama-3-8B"

tokenizer = AutoTokenizer.from_pretrained(checkpoint, revision="pr/13")
model = AutoModelForCausalLM.from_pretrained(checkpoint, torch_dtype=torch.bfloat16, device_map="auto")

Next, let’s define a list of tools:

def get_current_temperature(location: str, unit: str) -> float:
    """
    Get the current temperature at a location.
    
    Args:
        location: The location to get the temperature for, in the format "City, Country"
        unit: The unit to return the temperature in. (choices: ["celsius", "fahrenheit"])
    Returns:
        The current temperature at the specified location in the specified units, as a float.
    """
    return 22.  # A real function should probably actually get the temperature!

def get_current_wind_speed(location: str) -> float:
    """
    Get the current wind speed in km/h at a given location.
    
    Args:
        location: The location to get the temperature for, in the format "City, Country"
    Returns:
        The current wind speed at the given location in km/h, as a float.
    """
    return 6.  # A real function should probably actually get the wind speed!

tools = [get_current_temperature, get_current_wind_speed]

Now, let’s set up a conversation for our bot:

messages = [
  {"role": "system", "content": "You are a bot that responds to weather queries. You should reply with the unit used in the queried location."},
  {"role": "user", "content": "Hey, what's the temperature in Paris right now?"}
]

Now, let’s apply the chat template and generate a response:

inputs = tokenizer.apply_chat_template(messages, chat_template="tool_use", tools=tools, add_generation_prompt=True, return_dict=True, return_tensors="pt")
inputs = {k: v.to(model.device) for k, v in inputs.items()}
out = model.generate(**inputs, max_new_tokens=128)
print(tokenizer.decode(out[0][len(inputs["input_ids"][0]):]))

And we get:

<tool_call>
{"arguments": {"location": "Paris, France", "unit": "celsius"}, "name": "get_current_temperature"}
</tool_call><|im_end|>

The model has called the function with valid arguments, in the format requested by the function docstring. It has inferred that we’re most likely referring to the Paris in France, and it remembered that, as the home of SI units, the temperature in France should certainly be displayed in Celsius.

Let’s append the model’s tool call to the conversation. Note that we generate a random tool_call_id here. These IDs are not used by all models, but they allow models to issue multiple tool calls at once and keep track of which response corresponds to which call. You can generate them any way you like, but they should be unique within each chat.

tool_call_id = "vAHdf3"  # Random ID, should be unique for each tool call
tool_call = {"name": "get_current_temperature", "arguments": {"location": "Paris, France", "unit": "celsius"}}
messages.append({"role": "assistant", "tool_calls": [{"id": tool_call_id, "type": "function", "function": tool_call}]})

Now that we’ve added the tool call to the conversation, we can call the function and append the result to the conversation. Since we’re just using a dummy function for this example that always returns 22.0, we can just append that result directly. Again, note the tool_call_id - this should match the ID used in the tool call above.

messages.append({"role": "tool", "tool_call_id": tool_call_id, "name": "get_current_temperature", "content": "22.0"})

Finally, let’s let the assistant read the function outputs and continue chatting with the user:

inputs = tokenizer.apply_chat_template(messages, chat_template="tool_use", tools=tools, add_generation_prompt=True, return_dict=True, return_tensors="pt")
inputs = {k: v.to(model.device) for k, v in inputs.items()}
out = model.generate(**inputs, max_new_tokens=128)
print(tokenizer.decode(out[0][len(inputs["input_ids"][0]):]))

And we get:

The current temperature in Paris, France is 22.0 ° Celsius.<|im_end|>

Although this was a simple demo with dummy tools and a single call, the same technique works with multiple real tools and longer conversations. This can be a powerful way to extend the capabilities of conversational agents with real-time information, computational tools like calculators, or access to large databases.

Understanding tool schemas

Each function you pass to the tools argument of apply_chat_template is converted into a JSON schema. These schemas are then passed to the model chat template. In other words, tool-use models do not see your functions directly, and they never see the actual code inside them. What they care about is the function definitions and the arguments they need to pass to them - they care about what the tools do and how to use them, not how they work! It is up to you to read their outputs, detect if they have requested to use a tool, pass their arguments to the tool function, and return the response in the chat.

Generating JSON schemas to pass to the template should be automatic and invisible as long as your functions follow the specification above, but if you encounter problems, or you simply want more control over the conversion, you can handle the conversion manually. Here is an example of a manual schema conversion.

from transformers.utils import get_json_schema

def multiply(a: float, b: float):
    """
    A function that multiplies two numbers
    
    Args:
        a: The first number to multiply
        b: The second number to multiply
    """
    return a * b

schema = get_json_schema(multiply)
print(schema)

This will yield:

{
  "type": "function", 
  "function": {
    "name": "multiply", 
    "description": "A function that multiplies two numbers", 
    "parameters": {
      "type": "object", 
      "properties": {
        "a": {
          "type": "number", 
          "description": "The first number to multiply"
        }, 
        "b": {
          "type": "number",
          "description": "The second number to multiply"
        }
      }, 
      "required": ["a", "b"]
    }
  }
}

If you wish, you can edit these schemas, or even write them from scratch yourself without using get_json_schema at all. JSON schemas can be passed directly to the tools argument of apply_chat_template - this gives you a lot of power to define precise schemas for more complex functions. Be careful, though - the more complex your schemas, the more likely the model is to get confused when dealing with them! We recommend simple function signatures where possible, keeping arguments (and especially complex, nested arguments) to a minimum.

Here is an example of defining schemas by hand, and passing them directly to apply_chat_template:

# A simple function that takes no arguments
current_time = {
  "type": "function", 
  "function": {
    "name": "current_time",
    "description": "Get the current local time as a string.",
    "parameters": {
      'type': 'object',
      'properties': {}
    }
  }
}

# A more complete function that takes two numerical arguments
multiply = {
  'type': 'function',
  'function': {
    'name': 'multiply',
    'description': 'A function that multiplies two numbers', 
    'parameters': {
      'type': 'object', 
      'properties': {
        'a': {
          'type': 'number',
          'description': 'The first number to multiply'
        }, 
        'b': {
          'type': 'number', 'description': 'The second number to multiply'
        }
      }, 
      'required': ['a', 'b']
    }
  }
}

model_input = tokenizer.apply_chat_template(
    messages,
    tools = [current_time, multiply]
)

Advanced: Retrieval-augmented generation

“Retrieval-augmented generation” or “RAG” LLMs can search a corpus of documents for information before responding to a query. This allows models to vastly expand their knowledge base beyond their limited context size. Our recommendation for RAG models is that their template should accept a documents argument. This should be a list of documents, where each “document” is a single dict with title and contents keys, both of which are strings. Because this format is much simpler than the JSON schemas used for tools, no helper functions are necessary.

Here’s an example of a RAG template in action:

document1 = {
    "title": "The Moon: Our Age-Old Foe",
    "contents": "Man has always dreamed of destroying the moon. In this essay, I shall..."
}

document2 = {
    "title": "The Sun: Our Age-Old Friend",
    "contents": "Although often underappreciated, the sun provides several notable benefits..."
}

model_input = tokenizer.apply_chat_template(
    messages,
    documents=[document1, document2]
)

Advanced: How do chat templates work?

The chat template for a model is stored on the tokenizer.chat_template attribute. If no chat template is set, the default template for that model class is used instead. Let’s take a look at the template for BlenderBot:

>>> from transformers import AutoTokenizer
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot-400M-distill")

>>> tokenizer.default_chat_template
"{% for message in messages %}{% if message['role'] == 'user' %}{{ ' ' }}{% endif %}{{ message['content'] }}{% if not loop.last %}{{ '  ' }}{% endif %}{% endfor %}{{ eos_token }}"

That’s kind of intimidating. Let’s clean it up a little to make it more readable. In the process, though, we also make sure that the newlines and indentation we add don’t end up being included in the template output - see the tip on trimming whitespace below!

{%- for message in messages %}
    {%- if message['role'] == 'user' %}
        {{- ' ' }}
    {%- endif %}
    {{- message['content'] }}
    {%- if not loop.last %}
        {{- '  ' }}
    {%- endif %}
{%- endfor %}
{{- eos_token }}

If you’ve never seen one of these before, this is a Jinja template. Jinja is a templating language that allows you to write simple code that generates text. In many ways, the code and syntax resembles Python. In pure Python, this template would look something like this:

for idx, message in enumerate(messages):
    if message['role'] == 'user':
        print(' ')
    print(message['content'])
    if not idx == len(messages) - 1:  # Check for the last message in the conversation
        print('  ')
print(eos_token)

Effectively, the template does three things:

1. For each message, if the message is a user message, add a blank space before it, otherwise print nothing.
2. Add the message content
3. If the message is not the last message, add two spaces after it. After the final message, print the EOS token.

This is a pretty simple template - it doesn’t add any control tokens, and it doesn’t support “system” messages, which are a common way to give the model directives about how it should behave in the subsequent conversation. But Jinja gives you a lot of flexibility to do those things! Let’s see a Jinja template that can format inputs similarly to the way LLaMA formats them (note that the real LLaMA template includes handling for default system messages and slightly different system message handling in general - don’t use this one in your actual code!)

{%- for message in messages %}
    {%- if message['role'] == 'user' %}
        {{- bos_token + '[INST] ' + message['content'] + ' [/INST]' }}
    {%- elif message['role'] == 'system' %}
        {{- '<<SYS>>\\n' + message['content'] + '\\n<</SYS>>\\n\\n' }}
    {%- elif message['role'] == 'assistant' %}
        {{- ' '  + message['content'] + ' ' + eos_token }}
    {%- endif %}
{%- endfor %}

Hopefully if you stare at this for a little bit you can see what this template is doing - it adds specific tokens based on the “role” of each message, which represents who sent it. User, assistant and system messages are clearly distinguishable to the model because of the tokens they’re wrapped in.

Advanced: Adding and editing chat templates

How do I create a chat template?

Simple, just write a jinja template and set tokenizer.chat_template. You may find it easier to start with an existing template from another model and simply edit it for your needs! For example, we could take the LLaMA template above and add ”[ASST]” and ”[/ASST]” to assistant messages:

{%- for message in messages %}
    {%- if message['role'] == 'user' %}
        {{- bos_token + '[INST] ' + message['content'].strip() + ' [/INST]' }}
    {%- elif message['role'] == 'system' %}
        {{- '<<SYS>>\\n' + message['content'].strip() + '\\n<</SYS>>\\n\\n' }}
    {%- elif message['role'] == 'assistant' %}
        {{- '[ASST] '  + message['content'] + ' [/ASST]' + eos_token }}
    {%- endif %}
{%- endfor %}

Now, simply set the tokenizer.chat_template attribute. Next time you use apply_chat_template(), it will use your new template! This attribute will be saved in the tokenizer_config.json file, so you can use push_to_hub() to upload your new template to the Hub and make sure everyone’s using the right template for your model!

template = tokenizer.chat_template
template = template.replace("SYS", "SYSTEM")  # Change the system token
tokenizer.chat_template = template  # Set the new template
tokenizer.push_to_hub("model_name")  # Upload your new template to the Hub!

The method apply_chat_template() which uses your chat template is called by the TextGenerationPipeline class, so once you set the correct chat template, your model will automatically become compatible with TextGenerationPipeline.

Why do some models have multiple templates?

Some models use different templates for different use cases. For example, they might use one template for normal chat and another for tool-use, or retrieval-augmented generation. In these cases, tokenizer.chat_template is a dictionary. This can cause some confusion, and where possible, we recommend using a single template for all use-cases. You can use Jinja statements like if tools is defined and {% macro %} definitions to easily wrap multiple code paths in a single template.

When a tokenizer has multiple templates, tokenizer.chat_template will be a dict, where each key is the name of a template. The apply_chat_template method has special handling for certain template names: Specifically, it will look for a template named default in most cases, and will raise an error if it can’t find one. However, if a template named tool_use exists when the user has passed a tools argument, it will use that instead. To access templates with other names, pass the name of the template you want to the chat_template argument of apply_chat_template().

We find that this can be a bit confusing for users, though - so if you’re writing a template yourself, we recommend trying to put it all in a single template where possible!

What are “default” templates?

Before the introduction of chat templates, chat handling was hardcoded at the model class level. For backwards compatibility, we have retained this class-specific handling as default templates, also set at the class level. If a model does not have a chat template set, but there is a default template for its model class, the TextGenerationPipeline class and methods like apply_chat_template will use the class template instead. You can find out what the default template for your tokenizer is by checking the tokenizer.default_chat_template attribute.

This is something we do purely for backward compatibility reasons, to avoid breaking any existing workflows. Even when the class template is appropriate for your model, we strongly recommend overriding the default template by setting the chat_template attribute explicitly to make it clear to users that your model has been correctly configured for chat.

Now that actual chat templates have been adopted more widely, default templates have been deprecated and will be removed in a future release. We strongly recommend setting the chat_template attribute for any tokenizers that still depend on them!

What template should I use?

When setting the template for a model that’s already been trained for chat, you should ensure that the template exactly matches the message formatting that the model saw during training, or else you will probably experience performance degradation. This is true even if you’re training the model further - you will probably get the best performance if you keep the chat tokens constant. This is very analogous to tokenization - you generally get the best performance for inference or fine-tuning when you precisely match the tokenization used during training.

If you’re training a model from scratch, or fine-tuning a base language model for chat, on the other hand, you have a lot of freedom to choose an appropriate template! LLMs are smart enough to learn to handle lots of different input formats. One popular choice is the ChatML format, and this is a good, flexible choice for many use-cases. It looks like this:

{%- for message in messages %}
    {{- '<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n' }}
{%- endfor %}

If you like this one, here it is in one-liner form, ready to copy into your code. The one-liner also includes handy support for generation prompts, but note that it doesn’t add BOS or EOS tokens! If your model expects those, they won’t be added automatically by apply_chat_template - in other words, the text will be tokenized with add_special_tokens=False. This is to avoid potential conflicts between the template and the add_special_tokens logic. If your model expects special tokens, make sure to add them to the template!

tokenizer.chat_template = "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}"

This template wraps each message in <|im_start|> and <|im_end|> tokens, and simply writes the role as a string, which allows for flexibility in the roles you train with. The output looks like this:

<|im_start|>system
You are a helpful chatbot that will do its best not to say anything so stupid that people tweet about it.<|im_end|>
<|im_start|>user
How are you?<|im_end|>
<|im_start|>assistant
I'm doing great!<|im_end|>

The “user”, “system” and “assistant” roles are the standard for chat, and we recommend using them when it makes sense, particularly if you want your model to operate well with TextGenerationPipeline. However, you are not limited to these roles - templating is extremely flexible, and any string can be a role.

I want to add some chat templates! How should I get started?

If you have any chat models, you should set their tokenizer.chat_template attribute and test it using apply_chat_template(), then push the updated tokenizer to the Hub. This applies even if you’re not the model owner - if you’re using a model with an empty chat template, or one that’s still using the default class template, please open a pull request to the model repository so that this attribute can be set properly!

Once the attribute is set, that’s it, you’re done! tokenizer.apply_chat_template will now work correctly for that model, which means it is also automatically supported in places like TextGenerationPipeline!

By ensuring that models have this attribute, we can make sure that the whole community gets to use the full power of open-source models. Formatting mismatches have been haunting the field and silently harming performance for too long - it’s time to put an end to them!

Advanced: Template writing tips

If you’re unfamiliar with Jinja, we generally find that the easiest way to write a chat template is to first write a short Python script that formats messages the way you want, and then convert that script into a template.

Remember that the template handler will receive the conversation history as a variable called messages.
You will be able to access messages in your template just like you can in Python, which means you can loop over it with {% for message in messages %} or access individual messages with {{ messages[0] }}, for example.

You can also use the following tips to convert your code to Jinja:

Trimming whitespace

By default, Jinja will print any whitespace that comes before or after a block. This can be a problem for chat templates, which generally want to be very precise with whitespace! To avoid this, we strongly recommend writing your templates like this:

{%- for message in messages %}
    {{- message['role'] + message['content'] }}
{%- endfor %}

rather than like this:

{% for message in messages %}
    {{ message['role'] + message['content'] }}
{% endfor %}

Adding - will strip any whitespace that comes before the block. The second example looks innocent, but the newline and indentation may end up being included in the output, which is probably not what you want!

For loops

For loops in Jinja look like this:

{%- for message in messages %}
    {{- message['content'] }}
{%- endfor %}

Note that whatever’s inside the {{ expression block }} will be printed to the output. You can use operators like + to combine strings inside expression blocks.

If statements

If statements in Jinja look like this:

{%- if message['role'] == 'user' %}
    {{- message['content'] }}
{%- endif %}

Note how where Python uses whitespace to mark the beginnings and ends of for and if blocks, Jinja requires you to explicitly end them with {% endfor %} and {% endif %}.

Special variables

Inside your template, you will have access to the list of messages, but you can also access several other special variables. These include special tokens like bos_token and eos_token, as well as the add_generation_prompt variable that we discussed above. You can also use the loop variable to access information about the current loop iteration, for example using {% if loop.last %} to check if the current message is the last message in the conversation. Here’s an example that puts these ideas together to add a generation prompt at the end of the conversation if add_generation_prompt is True:

{%- if loop.last and add_generation_prompt %}
    {{- bos_token + 'Assistant:\n' }}
{%- endif %}

Compatibility with non-Python Jinja

There are multiple implementations of Jinja in various languages. They generally have the same syntax, but a key difference is that when you’re writing a template in Python you can use Python methods, such as .lower() on strings or .items() on dicts. This will break if someone tries to use your template on a non-Python implementation of Jinja. Non-Python implementations are particularly common in deployment environments, where JS and Rust are very popular.

Don’t panic, though! There are a few easy changes you can make to your templates to ensure they’re compatible across all implementations of Jinja:

• Replace Python methods with Jinja filters. These usually have the same name, for example string.lower() becomes string|lower, and dict.items() becomes dict|items. One notable change is that string.strip() becomes string|trim. See the list of built-in filters in the Jinja documentation for more.
• Replace True, False and None, which are Python-specific, with true, false and none.
• Directly rendering a dict or list may give different results in other implementations (for example, string entries might change from single-quoted to double-quoted). Adding the tojson filter can help to ensure consistency here.