Open-GVP-Qwen3-4B-Instruct

💊 Open-GVP-Qwen3-4B-Instruct

Domain-adapted Qwen3-4B model specialized in Good Pharmacovigilance Practices (GVP) guidelines issued by the European Medicines Agency (EMA).

This repository contains both the merged Safetensors version and GGUF quantized versions of the model.

Available formats:

• Merged Safetensors (for Transformers / vLLM / etc.)
• GGUF: BF16, Q8_0, Q6_K

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📖 Model Description

Open-GVP-Qwen3-4B-Instruct is a domain-specialized version of Qwen/Qwen3-4B-Instruct-2507, fine-tuned using LoRA on a curated dataset of approximately 15,000 high-quality question-answer pairs derived from the official EMA Good Pharmacovigilance Practices (GVP) guidelines.

The model covers all GVP Modules and related Addendums, with particular strength in areas such as:

• ICSR collection, management, and submission (Module VI)
• Signal management (Module IX)
• Risk management systems (Module V)
• Periodic safety update reports (PSUR / PBRER)
• Pharmacovigilance system master file (PSMF)

Important: This model performs best when used as part of a RAG (Retrieval-Augmented Generation) pipeline alongside the original GVP PDF documents, rather than as a standalone source of regulatory advice.

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🗂️ Coverage

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📦 Available Formats

Recommendation:

• Use Safetensors for maximum quality and flexibility.
• Use Q8_0 or Q6_K GGUF for local / CPU-friendly deployment.

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🚀 Quick Start

1. Transformers (Safetensors)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "drvivekpoojary/Open-GVP-Qwen3-4B-Instruct"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

messages = [
    {"role": "system", "content": "You are an expert pharmacovigilance assistant specialized in EMA Good Pharmacovigilance Practices (GVP)."},
    {"role": "user", "content": "What is the definition of a serious adverse reaction according to GVP Module VI?"}
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer([text], return_tensors="pt").to(model.device)

outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.2)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

2. Ollama (GGUF)

# Recommended (Q8_0)
ollama run hf.co/drvivekpoojary/Open-GVP-Qwen3-4B-Instruct:Q8_0

# Alternative options
ollama run hf.co/drvivekpoojary/Open-GVP-Qwen3-4B-Instruct:Q6_K
ollama run hf.co/drvivekpoojary/Open-GVP-Qwen3-4B-Instruct:BF16

3. llama.cpp

./llama-cli \
  -m Open-GVP-Qwen3-4B-Q8_0.gguf\
  -p "What is the definition of a serious adverse reaction according to GVP Module VI?" \
  -n 512 \
  -c 4096 \
  --temp 0.2

4. Python (llama-cpp-python)

from llama_cpp import Llama

llm = Llama(
    model_path="Open-GVP-Qwen3-4B-Q8_0.gguf",
    n_ctx=4096,
    n_gpu_layers=-1,      # set to 0 for pure CPU
    verbose=False
)

response = llm.create_chat_completion(
    messages=[
        {
            "role": "system",
            "content": "You are an expert pharmacovigilance assistant specialized in EMA Good Pharmacovigilance Practices (GVP)."
        },
        {
            "role": "user",
            "content": "What is the definition of a serious adverse reaction according to GVP Module VI?"
        }
    ],
    max_tokens=512,
    temperature=0.2
)

print(response["choices"][0]["message"]["content"])

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✅ Recommended Use Cases

❌ Not Recommended For

• Standalone regulatory decision-making
• High-stakes compliance or submission decisions without human review
• Replacing qualified pharmacovigilance professionals
• Use outside the scope of EMA GVP guidelines
• Generating content for regulatory submissions without expert verification

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🔧 Training Details

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⚠️ Disclaimer

• This model is intended for research, educational, and internal professional support purposes only.

• It does not constitute regulatory advice. All outputs should be carefully reviewed by qualified pharmacovigilance professionals before being used in any compliance, case processing, reporting, or decision-making context.

• The model may produce incomplete, outdated, or inaccurate responses, particularly on complex or nuanced regulatory questions. The author assumes no liability for any decisions made based on the model’s outputs.

• No In-Training Evaluation: Evaluation loss (eval_loss) was not computed during training, and per-epoch checkpoints were not preserved.

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👤 Author

Dr. Vivek Poojary

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📄 License

Apache License 2.0