llm-huggingface-plugin 0.1

LLM plugin to access Hugging Face models

llm-huggingface

Access Hugging Face models via the Inference API

Installation

Install this plugin in the same environment as LLM.

llm install llm-huggingface

Configuration

Configure the plugin by setting your Hugging Face API token:

llm keys set huggingface

<paste key here>

You can also set the API key by assigning it to the environment variable HUGGINGFACE_TOKEN.

Usage

The plugin automatically discovers and registers all available text-generation models from Hugging Face. Run a model using the hf/ prefix:

llm -m hf/meta-llama/Llama-3.2-3B-Instruct "Write a haiku about coding"

You can list all available Hugging Face models:

llm models | grep "^hf/"

Set a default model to avoid the -m option:

llm models default hf/mistralai/Mistral-7B-Instruct-v0.3
llm "Explain quantum computing in simple terms"

Features

Streaming Responses

The plugin supports streaming for real-time output:

llm -m hf/meta-llama/Llama-3.2-3B-Instruct "Tell me a story" --stream

JSON Schema Output

Force structured JSON output using schemas:

llm -m hf/meta-llama/Llama-3.2-3B-Instruct \
  --schema '{"type": "object", "properties": {"name": {"type": "string"}, "age": {"type": "integer"}}}' \
  "Generate a person's profile"

Or use the simpler DSL format:

llm -m hf/meta-llama/Llama-3.2-3B-Instruct \
  --schema 'name: str, age: int, hobbies: list[str]' \
  "Generate a person's profile with hobbies"

Note: JSON schema support varies by model. Some models may not fully support structured output or may produce inconsistent results. Models specifically fine-tuned for instruction following and structured output generation tend to perform better with schemas.

Function Calling / Tools

The plugin supports function calling for models that have this capability:

llm -m hf/meta-llama/Llama-3.2-3B-Instruct \
  --tool calculate 'def calculate(expression: str) -> float: """Evaluate a mathematical expression"""' \
  "What is 15% of 240?"

Generation Parameters

Control generation with various parameters:

• Temperature (0.0-2.0): Controls randomness

  llm -m hf/meta-llama/Llama-3.2-3B-Instruct -o temperature 0.7 "Write creatively"
  

• Top-p (0.0-1.0): Nucleus sampling threshold

  llm -m hf/meta-llama/Llama-3.2-3B-Instruct -o top_p 0.9 "Generate text"
  

• Max tokens: Limit response length

  llm -m hf/meta-llama/Llama-3.2-3B-Instruct -o max_tokens 100 "Explain AI"
  

• Stop sequences: Halt generation at specific strings

  llm -m hf/meta-llama/Llama-3.2-3B-Instruct -o stop '[".", "!"]' "Generate until punctuation"
  

Interactive Chat

Start an interactive chat session:

llm chat -m hf/meta-llama/Llama-3.2-3B-Instruct

Conversation History

Continue previous conversations:

llm -m hf/meta-llama/Llama-3.2-3B-Instruct "What is Python?" -c
llm -c "What are its main uses?"

Advanced Options

Token Limits

The plugin supports both max_tokens (chat-completions style) and max_new_tokens (text-generation style) parameters:

# Using max_tokens (preferred)
llm -m hf/meta-llama/Llama-3.2-3B-Instruct -o max_tokens 500 "Tell a story"

# Using max_new_tokens (for compatibility)
llm -m hf/meta-llama/Llama-3.2-3B-Instruct -o max_new_tokens 500 "Tell a story"

Note: You cannot set both max_tokens and max_new_tokens simultaneously.

Response Metadata

Access detailed response metadata using the Python API:

import llm

model = llm.get_model("hf/meta-llama/Llama-3.2-3B-Instruct")
response = model.prompt("Hello")

# Access metadata
print(response.response_json)
# {'usage': {...}, 'model': '...', 'finish_reason': 'stop', ...}

Python API

Use the plugin programmatically:

import llm

# Get a model
model = llm.get_model("hf/meta-llama/Llama-3.2-3B-Instruct")

# Simple prompt
response = model.prompt("Explain machine learning")
print(response.text())

# With options
response = model.prompt(
    "Write a poem",
    temperature=0.9,
    max_tokens=200
)

# Streaming
for chunk in model.prompt("Tell me a story", stream=True):
    print(chunk, end="", flush=True)

# With system prompt
response = model.prompt(
    "Translate to French: Hello",
    system="You are a helpful translation assistant."
)

Model Discovery

The plugin automatically discovers models that support the text-generation task from Hugging Face. The discovery is cached to improve performance. Models are registered with the hf/ prefix to distinguish them from other LLM providers.

Limitations

• Attachments: Image, audio, and file attachments are not currently supported
• Model availability: Not all Hugging Face models may be accessible via the Inference API
• Rate limits: Subject to Hugging Face API rate limits based on your account type

Development

To set up this plugin locally, first checkout the code. Then create a new virtual environment:

cd llm-huggingface
python3 -m venv venv
source venv/bin/activate

Now install the dependencies and test dependencies:

pip install -e '.[test]'

To run the tests:

pytest

License

Apache 2.0