Models

Smolagents is an experimental API which is subject to change at any time. Results returned by the agents can vary as the APIs or underlying models are prone to change.

To learn more about agents and tools make sure to read the introductory guide. This page contains the API docs for the underlying classes.

Models

You’re free to create and use your own models to power your agent.

You could use any model callable for your agent, as long as:

It follows the messages format (List[Dict[str, str]]) for its input messages, and it returns a str.
It stops generating outputs before the sequences passed in the argument stop_sequences

For defining your LLM, you can make a custom_model method which accepts a list of messages and returns an object with a .content attribute containing the text. This callable also needs to accept a stop_sequences argument that indicates when to stop generating.

from huggingface_hub import login, InferenceClient

login("<YOUR_HUGGINGFACEHUB_API_TOKEN>")

model_id = "meta-llama/Llama-3.3-70B-Instruct"

client = InferenceClient(model=model_id)

def custom_model(messages, stop_sequences=["Task"]):
    response = client.chat_completion(messages, stop=stop_sequences, max_tokens=1000)
    answer = response.choices[0].message
    return answer

Additionally, custom_model can also take a grammar argument. In the case where you specify a grammar upon agent initialization, this argument will be passed to the calls to model, with the grammar that you defined upon initialization, to allow constrained generation in order to force properly-formatted agent outputs.

TransformersModel

For convenience, we have added a TransformersModel that implements the points above by building a local transformers pipeline for the model_id given at initialization.

from smolagents import TransformersModel

model = TransformersModel(model_id="HuggingFaceTB/SmolLM-135M-Instruct")

print(model([{"role": "user", "content": "Ok!"}], stop_sequences=["great"]))

>>> What a

You must have transformers and torch installed on your machine. Please run pip install smolagents[transformers] if it’s not the case.

class smolagents.TransformersModel

< source >

( model_id: typing.Optional[str] = None device_map: typing.Optional[str] = None torch_dtype: typing.Optional[str] = None trust_remote_code: bool = False **kwargs )

Parameters

model_id (str, optional, defaults to "Qwen/Qwen2.5-Coder-32B-Instruct") — The Hugging Face model ID to be used for inference. This can be a path or model identifier from the Hugging Face model hub.
device_map (str, optional) — The device_map to initialize your model with.
torch_dtype (str, optional) — The torch_dtype to initialize your model with.
trust_remote_code (bool, default False) — Some models on the Hub require running remote code: for this model, you would have to set this flag to True.
kwargs (dict, optional) — Any additional keyword arguments that you want to use in model.generate(), for instance max_new_tokens or device.
**kwargs — Additional keyword arguments to pass to model.generate(), for instance max_new_tokens or device.

Raises

ValueError

ValueError — If the model name is not provided.

A class to interact with Hugging Face’s Inference API for language model interaction.

This model allows you to communicate with Hugging Face’s models using the Inference API. It can be used in both serverless mode or with a dedicated endpoint, supporting features like stop sequences and grammar customization.

You must have transformers and torch installed on your machine. Please run pip install smolagents[transformers] if it’s not the case.

Example:

>>> engine = TransformersModel(
...     model_id="Qwen/Qwen2.5-Coder-32B-Instruct",
...     device="cuda",
...     max_new_tokens=5000,
... )
>>> messages = [{"role": "user", "content": "Explain quantum mechanics in simple terms."}]
>>> response = engine(messages, stop_sequences=["END"])
>>> print(response)
"Quantum mechanics is the branch of physics that studies..."

HfApiModel

The HfApiModel wraps an HF Inference API client for the execution of the LLM.

from smolagents import HfApiModel

messages = [
  {"role": "user", "content": "Hello, how are you?"},
  {"role": "assistant", "content": "I'm doing great. How can I help you today?"},
  {"role": "user", "content": "No need to help, take it easy."},
]

model = HfApiModel()
print(model(messages))

>>> Of course! If you change your mind, feel free to reach out. Take care!

class smolagents.HfApiModel

< source >

( model_id: str = 'Qwen/Qwen2.5-Coder-32B-Instruct' token: typing.Optional[str] = None timeout: typing.Optional[int] = 120 **kwargs )

Parameters

model_id (str, optional, defaults to "Qwen/Qwen2.5-Coder-32B-Instruct") — The Hugging Face model ID to be used for inference. This can be a path or model identifier from the Hugging Face model hub.
token (str, optional) — Token used by the Hugging Face API for authentication. This token need to be authorized ‘Make calls to the serverless Inference API’. If the model is gated (like Llama-3 models), the token also needs ‘Read access to contents of all public gated repos you can access’. If not provided, the class will try to use environment variable ‘HF_TOKEN’, else use the token stored in the Hugging Face CLI configuration.
timeout (int, optional, defaults to 120) — Timeout for the API request, in seconds.
**kwargs — Additional keyword arguments to pass to the Hugging Face API.

Raises

ValueError

ValueError — If the model name is not provided.

A class to interact with Hugging Face’s Inference API for language model interaction.

Example:

>>> engine = HfApiModel(
...     model_id="Qwen/Qwen2.5-Coder-32B-Instruct",
...     token="your_hf_token_here",
...     max_tokens=5000,
... )
>>> messages = [{"role": "user", "content": "Explain quantum mechanics in simple terms."}]
>>> response = engine(messages, stop_sequences=["END"])
>>> print(response)
"Quantum mechanics is the branch of physics that studies..."

LiteLLMModel

The LiteLLMModel leverages LiteLLM to support 100+ LLMs from various providers. You can pass kwargs upon model initialization that will then be used whenever using the model, for instance below we pass temperature.

from smolagents import LiteLLMModel

messages = [
  {"role": "user", "content": "Hello, how are you?"},
  {"role": "assistant", "content": "I'm doing great. How can I help you today?"},
  {"role": "user", "content": "No need to help, take it easy."},
]

model = LiteLLMModel("anthropic/claude-3-5-sonnet-latest", temperature=0.2, max_tokens=10)
print(model(messages))

class smolagents.LiteLLMModel

< source >

( model_id = 'anthropic/claude-3-5-sonnet-20240620' api_base = None api_key = None **kwargs )

Parameters

model_id (str) — The model identifier to use on the server (e.g. “gpt-3.5-turbo”).
api_base (str, optional) — The base URL of the OpenAI-compatible API server.
api_key (str, optional) — The API key to use for authentication.
**kwargs — Additional keyword arguments to pass to the OpenAI API.

This model connects to LiteLLM as a gateway to hundreds of LLMs.

OpenAIServerModel

This class lets you call any OpenAIServer compatible model. Here’s how you can set it (you can customise the api_base url to point to another server):

from smolagents import OpenAIServerModel

model = OpenAIServerModel(
    model_id="gpt-4o",
    api_base="https://api.openai.com/v1",
    api_key=os.environ["OPENAI_API_KEY"],
)

class smolagents.OpenAIServerModel

< source >

( model_id: str api_base: typing.Optional[str] = None api_key: typing.Optional[str] = None organization: typing.Optional[str] = None project: typing.Optional[str] = None custom_role_conversions: typing.Optional[typing.Dict[str, str]] = None **kwargs )

Parameters

model_id (str) — The model identifier to use on the server (e.g. “gpt-3.5-turbo”).
api_base (str, optional) — The base URL of the OpenAI-compatible API server.
api_key (str, optional) — The API key to use for authentication.
organization (str, optional) — The organization to use for the API request.
project (str, optional) — The project to use for the API request.
custom_role_conversions (dict[str, str], optional) — Custom role conversion mapping to convert message roles in others. Useful for specific models that do not support specific message roles like “system”.
**kwargs — Additional keyword arguments to pass to the OpenAI API.

This model connects to an OpenAI-compatible API server.

AzureOpenAIServerModel

AzureOpenAIServerModel allows you to connect to any Azure OpenAI deployment.

Below you can find an example of how to set it up, note that you can omit the azure_endpoint, api_key, and api_version arguments, provided you’ve set the corresponding environment variables — AZURE_OPENAI_ENDPOINT, AZURE_OPENAI_API_KEY, and OPENAI_API_VERSION.

Pay attention to the lack of an AZURE_ prefix for OPENAI_API_VERSION, this is due to the way the underlying openai package is designed.

import os

from smolagents import AzureOpenAIServerModel

model = AzureOpenAIServerModel(
    model_id = os.environ.get("AZURE_OPENAI_MODEL"),
    azure_endpoint=os.environ.get("AZURE_OPENAI_ENDPOINT"),
    api_key=os.environ.get("AZURE_OPENAI_API_KEY"),
    api_version=os.environ.get("OPENAI_API_VERSION")    
)

class smolagents.AzureOpenAIServerModel

< source >

( model_id: str azure_endpoint: typing.Optional[str] = None api_key: typing.Optional[str] = None api_version: typing.Optional[str] = None custom_role_conversions: typing.Optional[typing.Dict[str, str]] = None **kwargs )

Parameters

model_id (str) — The model deployment name to use when connecting (e.g. “gpt-4o-mini”).
azure_endpoint (str, optional) — The Azure endpoint, including the resource, e.g. https://example-resource.azure.openai.com/. If not provided, it will be inferred from the AZURE_OPENAI_ENDPOINT environment variable.
api_key (str, optional) — The API key to use for authentication. If not provided, it will be inferred from the AZURE_OPENAI_API_KEY environment variable.
api_version (str, optional) — The API version to use. If not provided, it will be inferred from the OPENAI_API_VERSION environment variable.
custom_role_conversions (dict[str, str], optional) — Custom role conversion mapping to convert message roles in others. Useful for specific models that do not support specific message roles like “system”.
**kwargs — Additional keyword arguments to pass to the Azure OpenAI API.

This model connects to an Azure OpenAI deployment.

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