gemma-4-E2B-sec-extraction-GGUF

DEPRECATED — Please use gemma-4-E2B-sec-extraction-GGUF-v2 instead.

v2 was trained on a combined instruction + corrective dataset (3,957 examples vs. 2,726) and shows measurably stronger extraction quality, including a 2 percentage point reduction in hallucination rate versus the base Gemma 4 E2B model (10.7% vs. 12.7%). v2 also improves symbol compliance (+0.9%), reduces bare number errors (-0.8%), and eliminates year-as-value hallucinations entirely.

This v1 model remains available for reproducibility but is no longer recommended for production use.

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Gemma 4 E2B — SEC Financial Extraction (v1, GGUF) [DEPRECATED]

A fine-tuned Gemma 4 E2B model specialized for extracting structured financial data from SEC Exhibit 10 material contracts. Quantized to Q4_K_M GGUF for efficient local inference.

What This Model Does

Given raw text from an SEC filing (employment agreements, credit facilities, merger agreements, etc.), this model extracts structured JSON containing:

• Metadata — effective dates and contracting party names
• Financial terms — dollar amounts and percentages classified into 13 categories (salary, bonus, severance, equity_grant, credit_facility, interest_rate, etc.)
• Debt covenants — financial maintenance tests classified into 7 categories (leverage_ratio, interest_coverage, debt_service, net_worth, etc.)

Why You Should Use v2 Instead

v2 is a strict upgrade — same base model, same hardware requirements, better extraction quality across all measured dimensions.

Upgrade: TheTokenFactory/gemma-4-E2B-sec-extraction-GGUF-v2

Usage

LM Studio

1. Download gemma-4-E2B-it.Q4_K_M.gguf (3.4 GB)
2. Import into LM Studio
3. Set GPU Layers to max (35/35), Context Length to 4096
4. Send extraction prompts via the chat API at http://localhost:1234/v1

Python (via OpenAI-compatible API)

from openai import OpenAI

client = OpenAI(base_url="http://localhost:1234/v1", api_key="lm-studio")

response = client.chat.completions.create(
    model="gemma",
    temperature=0.1,
    messages=[
        {"role": "system", "content": "You are a financial analyst AI. Extract ALL monetary dollar amounts and financial percentages. Output strictly as JSON."},
        {"role": "user", "content": "<contract text here>"},
    ],
)
print(response.choices[0].message.content)

Training Details

Financial Term Types (13 categories)

salary bonus severance retirement_benefit equity_grant credit_facility loan_amount interest_rate fee threshold purchase_price compensation other

Covenant Types (7 categories)

leverage_ratio interest_coverage debt_service net_worth liquidity fixed_charge other

Hardware Requirements

Limitations

• Temporal scope: Trained on S&P 500 filings from a 6-month window
• Universe: Large-cap US equities only (S&P 500)
• Language: English only
• Label quality: Silver-standard (model-generated, not human-annotated)
• No corrective training: v1 was trained only on positive examples, without the corrective/hard-negative signal that improves v2

License

CC-BY-4.0. SEC filings are public domain; this model's value is in the fine-tuning for structured extraction.

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Trained 2x faster with Unsloth

<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>