drvivekpoojary

Open-GVP-Qwen2.5-14B-Instruct

README

License: apache-2.0

📖 Model Description

Open-GVP-Qwen2.5-14B-Instruct is a domain-specialized version of Qwen/Qwen2.5-14B-Instruct, 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.

🗂️ Coverage

Table with columns: Category, Details
Category	Details
GVP Modules	All Modules
Addendum	Included
Training Data Size	~15,000 instruction-format Q&A pairs
Primary Focus	Regulatory interpretation & PV operations

📦 Available Formats

Table with columns: Format, Files, Best For
Format	Files	Best For
Merged Safetensors	`model-00001-of-00004.safetensors` (4 shards) + config	Transformers, vLLM, TGI, full-precision inference
GGUF BF16	`Open-GVP-Qwen2.5-14B-BF16.gguf`	Highest quality GGUF
GGUF Q8_0	`Open-GVP-Qwen2.5-14B-Q8_0.gguf`	Excellent quality / speed balance
GGUF Q6_K	`Open-GVP-Qwen2.5-14B-Q6_K.gguf`

Recommendation:

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

🚀 Quick Start

1. Transformers (Safetensors)

python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "drvivekpoojary/Open-GVP-Qwen2.5-14B-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)

bash
# Recommended (Q8_0)
ollama run hf.co/drvivekpoojary/Open-GVP-Qwen2.5-14B-Instruct:Q8_0

# Alternative options
ollama run hf.co/drvivekpoojary/Open-GVP-Qwen2.5-14B-Instruct:Q6_K
ollama run hf.co/drvivekpoojary/Open-GVP-Qwen2.5-14B-Instruct:BF16

3. llama.cpp

bash
./llama-cli \
  -m Open-GVP-Qwen2.5-14B-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)

python
from llama_cpp import Llama

llm = Llama(
    model_path="Open-GVP-Qwen2.5-14B-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"])

✅ Recommended Use Cases

Table with columns: Use Case, Description
Use Case	Description
GVP Knowledge Assistant	Answer questions on GVP modules, definitions, and requirements
PV Staff Training & Onboarding	Support training of new pharmacovigilance team members
RAG Pipeline	Use as the generator together with official GVP PDFs
Internal Regulatory Chatbot	Backend for company-internal PV compliance assistants
Offline / Air-gapped Environments	Run completely locally without internet access
Edge & Low-Resource Deployment	Suitable for laptops and workstations (especially GGUF versions)

❌ 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

🔧 Training Details

Table with columns: Property, Value
Property	Value
Base Model	Qwen/Qwen2.5-14B-Instruct
Fine-tuning Method	QLoRA
LoRA Rank	32
LoRA Alpha	64
LoRA Dropout	0.05
Training Data	~15,000 GVP Q&A pairs (all modules)
Epochs	4
Context Length	768

⚠️ 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.

👤 Author

Dr. Vivek Poojary

📄 License

Apache License 2.0

Available on FriendliAI

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Model Details

Model Provider

drvivekpoojary

Model Tree

Base

Qwen/Qwen2.5-14B-Instruct

Quantized

this model

Input Modalities

Text

Output Modalities