docs/swarms/models/index.md

Swarm Models

$ pip3 install -U swarm-models

Welcome to the documentation for the llm section of the swarms package, designed to facilitate seamless integration with various AI language models and APIs. This package empowers developers, end-users, and system administrators to interact with AI models from different providers, such as OpenAI, Hugging Face, Google PaLM, and Anthropic.

Table of Contents

1. OpenAI
2. HuggingFace
3. Anthropic

1. OpenAI (swarm_models.OpenAI)

The OpenAI class provides an interface to interact with OpenAI's language models. It allows both synchronous and asynchronous interactions.

Constructor:

OpenAI(api_key: str, system: str = None, console: bool = True, model: str = None, params: dict = None, save_messages: bool = True)

Attributes:

• api_key (str): Your OpenAI API key.

• system (str, optional): A system message to be used in conversations.

• console (bool, default=True): Display console logs.

• model (str, optional): Name of the language model to use.

• params (dict, optional): Additional parameters for model interactions.

• save_messages (bool, default=True): Save conversation messages.

Methods:

• run(message: str, **kwargs) -> str: Generate a response using the OpenAI model.

• generate_async(message: str, **kwargs) -> str: Generate a response asynchronously.

• ask_multiple(ids: List[str], question_template: str) -> List[str]: Query multiple IDs simultaneously.

• stream_multiple(ids: List[str], question_template: str) -> List[str]: Stream multiple responses.

Usage Example:

import asyncio

from swarm_models import OpenAI

chat = OpenAI(api_key="YOUR_OPENAI_API_KEY")

response = chat.run("Hello, how can I assist you?")
print(response)

ids = ["id1", "id2", "id3"]
async_responses = asyncio.run(chat.ask_multiple(ids, "How is {id}?"))
print(async_responses)

2. HuggingFace (swarm_models.HuggingFaceLLM)

The HuggingFaceLLM class allows interaction with language models from Hugging Face.

Constructor:

HuggingFaceLLM(model_id: str, device: str = None, max_length: int = 20, quantize: bool = False, quantization_config: dict = None)

Attributes:

• model_id (str): ID or name of the Hugging Face model.

• device (str, optional): Device to run the model on (e.g., 'cuda', 'cpu').

• max_length (int, default=20): Maximum length of generated text.

• quantize (bool, default=False): Apply model quantization.

• quantization_config (dict, optional): Configuration for quantization.

Methods:

• run(prompt_text: str, max_length: int = None) -> str: Generate text based on a prompt.

Usage Example:

from swarm_models import HuggingFaceLLM

model_id = "gpt2"
hugging_face_model = HuggingFaceLLM(model_id=model_id)

prompt = "Once upon a time"
generated_text = hugging_face_model.run(prompt)
print(generated_text)

3. Anthropic (swarm_models.Anthropic)

The Anthropic class enables interaction with Anthropic's large language models.

Constructor:

Anthropic(model: str = "claude-2", max_tokens_to_sample: int = 256, temperature: float = None, top_k: int = None, top_p: float = None, streaming: bool = False, default_request_timeout: int = None)

Attributes:

• model (str): Name of the Anthropic model.

• max_tokens_to_sample (int, default=256): Maximum tokens to sample.

• temperature (float, optional): Temperature for text generation.

• top_k (int, optional): Top-k sampling value.

• top_p (float, optional): Top-p sampling value.

• streaming (bool, default=False): Enable streaming mode.

• default_request_timeout (int, optional): Default request timeout.

Methods:

• run(prompt: str, stop: List[str] = None) -> str: Generate text based on a prompt.

Usage Example:

from swarm_models import Anthropic

anthropic = Anthropic()
prompt = "Once upon a time"
generated_text = anthropic.run(prompt)
print(generated_text)

This concludes the documentation for the "models" folder, providing you with tools to seamlessly integrate with various language models and APIs. Happy coding!