Vickstester/PV-BioMistral-1

pv-biomistral-7b

A pharmacovigilance-specialised language model fine-tuned from Mistral-7B-Instruct-v0.3 on 100,000 FAERS-derived training examples across five structured PV tasks.

This is the community testing release. It contains only the Q4_K_M quantized GGUF for local inference via Ollama or llama-cpp-python.

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⚠️ Important Disclaimer

This model is a research prototype intended for pharmacovigilance professionals to evaluate and provide feedback on. It is not a validated system and must not be used for:

• Autonomous pharmacovigilance decision-making
• Generating or contributing to regulatory submissions
• Replacing qualified pharmacovigilance assessor judgment
• Clinical or safety-critical decisions of any kind

All model outputs require review by a qualified pharmacovigilance professional. This tool is for exploratory and research purposes only.

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Model Details

Property	Value
Base model	mistralai/Mistral-7B-Instruct-v0.3
Fine-tuning method	QLoRA (4-bit NF4, LoRA r=16)
Training records	100,000
Training epochs	3
Data source	FAERS public database (FDA)
Quantization	Q4_K_M (GGUF)
Model size	4.37 GB
Context window	8192 tokens
Framework	TRL 1.0.0, Transformers, PEFT

Setup — Ollama (Recommended)

Requirements

• Ollama installed
• ~5 GB free disk space
• 8 GB RAM minimum, 16 GB recommended
• GPU optional but recommended for faster inference

Installation

Step 1 — Download both files from this repository:

• pv-biomistral-7b-Q4_K_M.gguf (4.37 GB)
• Modelfile

Place both in the same folder.

Step 2 — Create the Ollama model

cd /path/to/downloaded/files
ollama create pv-mistral-v2 -f Modelfile

Step 3 — Run

ollama run pv-mistral-v2

Windows users: Use the full path e.g. cd C:\Users\YourName\Downloads\pv-model\

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Setup — llama-cpp-python (Alternative)

pip install llama-cpp-python[server]

python -m llama_cpp.server \
  --model pv-biomistral-7b-Q4_K_M.gguf \
  --chat_format mistral-instruct \
  --n_gpu_layers -1 \
  --n_ctx 8192

Then open http://localhost:8000/docs for the Swagger UI.

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Setup — Jan App (Windows/Mac)

1. Download Jan
2. Import Model → select the GGUF file
3. Set temperature to 0.1 in chat settings
4. Add system prompt from the Modelfile SYSTEM field

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Expected Performance by Hardware

Hardware	Speed	Response Time
Mac Mini M4 / Apple Silicon	25-35 tokens/sec	2-5 sec/case
Windows + NVIDIA GPU (8GB+ VRAM)	25-40 tokens/sec	2-4 sec/case
Snapdragon X Elite (16GB)	8-15 tokens/sec	5-12 sec/case
Windows CPU only (16-24GB RAM)	3-6 tokens/sec	15-30 sec/case

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Known Limitations

• Probable causality underrepresented: Training data contained only 70 Probable causality examples out of 100,000 records, reflecting real-world FAERS spontaneous reporting patterns. The model may default to Possible even for cases with confirmed positive dechallenge and no confounders.

• Spontaneous reports only: Trained exclusively on FAERS spontaneous adverse event reports. Performance on clinical trial safety data, EHR-derived cases, or non-English source material is untested.

• Not formally validated: The model has not been validated against any regulatory standard including ICH E2D, ICH E2A, or WHO-UMC guidelines.

• Short context optimised: Designed for single-case inputs under 512 tokens.

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CIOMS WG XIV Alignment

This model is designed to operate within a Human-in-the-Loop (HITL) framework consistent with CIOMS Working Group XIV recommendations for AI in drug safety. All outputs are decision-support signals requiring human adjudication by a qualified pharmacovigilance professional.

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Feedback

This is a community testing release. Please evaluate the model on real cases from your practice area and share findings. Particular interest in:

• Causality outputs where you would classify Probable
• Cases with unusual drug combinations or rare reactions
• Narrative quality from a safety database entry perspective
• Therapeutic areas where performance appears weaker

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Training Data

Trained on 10,000 cases from the FDA Adverse Event Reporting System (FAERS), accessed via public database export. No proprietary, confidential, or patient-identifiable data beyond what is publicly available in FAERS was used.

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License

Base model (Mistral-7B-Instruct-v0.3): Apache 2.0 Fine-tuned weights: CC BY-NC 4.0 (non-commercial research use only)

By downloading this model you agree to use it for research purposes only and not for any commercial application or regulatory submission.