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Get Started

Installation

uv add fusellm

With training support:

uv add "fusellm[training]"

Run without installing

uvx fusellm extract "Sarah Chen is a 34-year-old architect at Stripe" \
  --model bartowski/Llama-3.2-1B-Instruct-GGUF \
  --fields "name:str,age:int,job_title:str"

With pip

pip install fusellm
pip install "fusellm[training]"

Your first extraction

1. Pull a model and extract

import fuse

# Auto-downloads the best Q4 GGUF from HuggingFace Hub
backend = fuse.LlamaCppBackend(model_name="bartowski/Llama-3.2-1B-Instruct-GGUF")
extractor = fuse.Extractor(backend)

# Zero-shot structured extraction — no Pydantic model needed
result = extractor.extract_from_fields(
    "Sarah Chen is a 34-year-old software architect at Stripe.",
    {"name": str, "age": int, "job_title": str, "company": str}
)
print(result)
# {'name': 'Sarah Chen', 'age': 34, 'job_title': 'software architect', 'company': 'Stripe'}

2. Use a local GGUF model

backend = fuse.LlamaCppBackend(model_path="./models/llama-3.2-1b-q4.gguf")
extractor = fuse.Extractor(backend)

result = extractor.extract_from_fields(
    "John is 30 years old and knows Python and Rust",
    {"name": str, "age": int, "skills": list[str]}
)
# {'name': 'John', 'age': 30, 'skills': ['Python', 'Rust']}

3. Extract from a JSON schema

schema = fuse.SchemaBuilder.from_json_schema({
    "type": "object",
    "properties": {
        "name": {"type": "string"},
        "age": {"type": "integer"},
        "skills": {"type": "array", "items": {"type": "string"}},
    },
    "required": ["name", "age"],
})
result = extractor.extract("John is 30 and knows Rust", schema)

4. Let the LLM infer the schema

result = extractor.extract_from_description(
    "The Series A raised $15M from Sequoia, following a $2.5M seed from YC.",
    "Extract monetary amounts, funding round type, and investor names"
)

CLI quickstart

Extract with inline flags

fuse extract "SpaceX was founded in 2002" \
  --model bartowski/Phi-4-mini-instruct-GGUF \
  --fields "company:str,year:int,industry:str"

Extract with a config file

fuse extract "Sarah Chen is a 34-year-old architect at Stripe" \
  --config extract_person.yaml

Extract with evidence spans

See where each value was found in the source text:

fuse extract "Sarah Chen is a 34-year-old architect at Stripe" \
  --model bartowski/Llama-3.2-1B-Instruct-GGUF \
  --fields "name:str,age:int,job_title:str,company:str" \
  --spans

Generate an HTML visualization

fuse extract "Sarah Chen is a 34-year-old architect at Stripe" \
  --model bartowski/Llama-3.2-1B-Instruct-GGUF \
  --fields "name:str,age:int,job_title:str,company:str" \
  --html result.html

Opens a color-coded view with highlighted spans, solid/dashed outlines for explicit/implicit extractions, and a field legend.

See CLI Reference for all commands and options.

Next steps