FDA Task Classifier - GGUF

A specialized language model fine-tuned for extracting regulatory tasks from FDA correspondence documents.

Model Details

Model Type: LlamaForCausalLM
Parameters: 361.82M
Quantization: Q8_0 GGUF
Context Window: 4096 tokens
File Size: 369 MB
License: Apache 2.0

Quick Start with Ollama

The easiest way to use this model is with Ollama:

# Pull the Modelfile from this repo
wget https://huggingface.co/llama-farm/fda-task-classifier-gguf/raw/main/Modelfile

# Create the model in Ollama
ollama create fda-task-classifier -f Modelfile

# Run the model
ollama run fda-task-classifier

Or download manually:

# Download the GGUF file
wget https://huggingface.co/llama-farm/fda-task-classifier-gguf/resolve/main/model.gguf

# Create a Modelfile
cat > Modelfile << 'EOF'
FROM ./model.gguf

PARAMETER temperature 0.3
PARAMETER top_p 0.9
PARAMETER top_k 40
PARAMETER num_ctx 4096
PARAMETER num_predict 512

SYSTEM """You are an FDA regulatory task extraction specialist. Your role is to analyze document chunks and identify specific FDA regulatory tasks, requirements, and action items.

When analyzing text, focus on:
- Regulatory submissions and deadlines
- Clinical trial requirements
- Manufacturing and quality control tasks
- Compliance and reporting obligations
- Safety monitoring requirements
- Documentation and record-keeping tasks

Extract tasks in a structured format with:
- Task description
- Regulatory category (e.g., clinical, manufacturing, compliance)
- Priority level if mentioned
- Deadline if specified
- Relevant FDA regulation references

Be precise and factual. Only extract tasks that are explicitly stated or clearly implied in the text."""
EOF

# Create model in Ollama
ollama create fda-task-classifier -f Modelfile

Usage Examples

Simple Task Extraction

ollama run fda-task-classifier "Extract all FDA regulatory tasks from this text:

The sponsor must submit a complete Chemistry, Manufacturing, and Controls (CMC)
section as part of the IND application within 30 days of this notice. Additionally,
the clinical protocol must be amended to include enhanced safety monitoring procedures."

Output:

1. Submit complete CMC section within 30 days
   Category: Manufacturing/Submission
   Priority: Critical
   Deadline: 30 days from notice

2. Amend clinical protocol to include enhanced safety monitoring
   Category: Clinical/Safety
   Priority: High

API Usage

import requests

response = requests.post('http://localhost:11434/api/generate', json={
    "model": "fda-task-classifier",
    "prompt": "Extract tasks from: The sponsor should provide updated stability data...",
    "stream": False
})

print(response.json()['response'])

Model Specialization

This model is specifically trained to identify:

✅ Submission Requirements

IND/NDA submissions
Supplemental applications
Annual reports

✅ Clinical Trial Directives

Protocol amendments
Safety monitoring
Patient enrollment criteria

✅ Manufacturing Tasks

CMC requirements
Quality control procedures
GMP compliance

✅ Regulatory Compliance

21 CFR citations
Inspection responses
CAPA plans

✅ Safety Obligations

Adverse event reporting
REMS requirements
Risk assessments

Integration with LlamaFarm

This model is designed to work seamlessly with LlamaFarm:

# llamafarm.yaml
runtime:
  models:
    - name: fda-task-classifier
      provider: ollama
      model: fda-task-classifier
      base_url: http://localhost:11434/v1

agents:
  - name: fda_document_analyzer
    type: document_analyzer
    model: fda-task-classifier
    description: Extracts FDA regulatory tasks from documents

Performance

Speed: ~2-3 seconds per document chunk on M1 Mac
Accuracy: Optimized for FDA regulatory language
Context: 4096 tokens (sufficient for most FDA letter sections)
Memory: ~500MB RAM usage

Files in This Repository

model.gguf - Quantized model weights (Q8_0)
Modelfile - Ollama model configuration
README.md - Original documentation
USAGE.md - Detailed usage examples
model_info.json - Model metadata

Technical Details

Architecture: LlamaForCausalLM Quantization: Q8_0 (8-bit quantization) Base Model: [Undisclosed] Training Data: FDA correspondence, deficiency letters, meeting minutes

Recommended Parameters:

temperature: 0.3 - More deterministic outputs
top_p: 0.9 - Focused sampling
num_ctx: 4096 - Optimized context window
num_predict: 512 - Concise task lists

Use Cases

Regulatory Document Processing
- Extract action items from FDA deficiency letters
- Identify compliance obligations
- Track submission deadlines
Quality Assurance
- Parse inspection observations (483s)
- Extract CAPA requirements
- Identify GMP violations
Clinical Operations
- Extract protocol amendment requirements
- Identify safety reporting obligations
- Track clinical trial milestones
Automated Compliance
- Build task tracking systems
- Create regulatory calendars
- Generate compliance reports

Limitations

Optimized for FDA documents (US regulatory text)
May not generalize well to other regulatory bodies (EMA, PMDA)
Works best with formal regulatory correspondence
Limited to English language

Citation

If you use this model in your research or application, please cite:

@software{fda_task_classifier_2025,
  title={FDA Task Classifier GGUF},
  author={LlamaFarm Team},
  year={2025},
  url={https://huggingface.co/llama-farm/fda-task-classifier-gguf}
}

License

Apache 2.0 - See LICENSE file for details

llama-farm
/

fda-task-classifier-gguf