README.md

---
language:
- en
license: cc-by-nc-4.0
tags:
- pharmacovigilance
- medical
- mistral
- qlora
- faers
- drug-safety
- adverse-events
base_model: mistralai/Mistral-7B-Instruct-v0.3
---

# pv-biomistral-7b

A pharmacovigilance-specialised language model fine-tuned from
[Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/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.

---

## ⚠️ 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.

---

## 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](https://ollama.com/download) 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**
```bash
cd /path/to/downloaded/files
ollama create pv-mistral-v2 -f Modelfile
```

**Step 3 — Run**
```bash
ollama run pv-mistral-v2
```

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

---

## Setup — llama-cpp-python (Alternative)

```bash
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.

---

## Setup — Jan App (Windows/Mac)

1. Download [Jan](https://jan.ai)
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

---

## 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 |

---

## 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.

---

## 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.

---

## 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

---

## 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.

---

## 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.