Instructions to use drvivekpoojary/Open-GVP-Qwen2.5-7B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use drvivekpoojary/Open-GVP-Qwen2.5-7B-Instruct with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="drvivekpoojary/Open-GVP-Qwen2.5-7B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("drvivekpoojary/Open-GVP-Qwen2.5-7B-Instruct")
model = AutoModelForCausalLM.from_pretrained("drvivekpoojary/Open-GVP-Qwen2.5-7B-Instruct", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use drvivekpoojary/Open-GVP-Qwen2.5-7B-Instruct with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf drvivekpoojary/Open-GVP-Qwen2.5-7B-Instruct:BF16
# Run inference directly in the terminal:
llama cli -hf drvivekpoojary/Open-GVP-Qwen2.5-7B-Instruct:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf drvivekpoojary/Open-GVP-Qwen2.5-7B-Instruct:BF16
# Run inference directly in the terminal:
llama cli -hf drvivekpoojary/Open-GVP-Qwen2.5-7B-Instruct:BF16

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf drvivekpoojary/Open-GVP-Qwen2.5-7B-Instruct:BF16
# Run inference directly in the terminal:
./llama-cli -hf drvivekpoojary/Open-GVP-Qwen2.5-7B-Instruct:BF16

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf drvivekpoojary/Open-GVP-Qwen2.5-7B-Instruct:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf drvivekpoojary/Open-GVP-Qwen2.5-7B-Instruct:BF16

Use Docker

docker model run hf.co/drvivekpoojary/Open-GVP-Qwen2.5-7B-Instruct:BF16

LM Studio
Jan

vLLM

How to use drvivekpoojary/Open-GVP-Qwen2.5-7B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "drvivekpoojary/Open-GVP-Qwen2.5-7B-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "drvivekpoojary/Open-GVP-Qwen2.5-7B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/drvivekpoojary/Open-GVP-Qwen2.5-7B-Instruct:BF16

SGLang

How to use drvivekpoojary/Open-GVP-Qwen2.5-7B-Instruct with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "drvivekpoojary/Open-GVP-Qwen2.5-7B-Instruct" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "drvivekpoojary/Open-GVP-Qwen2.5-7B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "drvivekpoojary/Open-GVP-Qwen2.5-7B-Instruct" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "drvivekpoojary/Open-GVP-Qwen2.5-7B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use drvivekpoojary/Open-GVP-Qwen2.5-7B-Instruct with Ollama:
```
ollama run hf.co/drvivekpoojary/Open-GVP-Qwen2.5-7B-Instruct:BF16
```

Unsloth Studio

How to use drvivekpoojary/Open-GVP-Qwen2.5-7B-Instruct with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for drvivekpoojary/Open-GVP-Qwen2.5-7B-Instruct to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for drvivekpoojary/Open-GVP-Qwen2.5-7B-Instruct to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for drvivekpoojary/Open-GVP-Qwen2.5-7B-Instruct to start chatting

How to use drvivekpoojary/Open-GVP-Qwen2.5-7B-Instruct with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf drvivekpoojary/Open-GVP-Qwen2.5-7B-Instruct:BF16

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "drvivekpoojary/Open-GVP-Qwen2.5-7B-Instruct:BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use drvivekpoojary/Open-GVP-Qwen2.5-7B-Instruct with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf drvivekpoojary/Open-GVP-Qwen2.5-7B-Instruct:BF16

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "drvivekpoojary/Open-GVP-Qwen2.5-7B-Instruct:BF16" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use drvivekpoojary/Open-GVP-Qwen2.5-7B-Instruct with Docker Model Runner:
```
docker model run hf.co/drvivekpoojary/Open-GVP-Qwen2.5-7B-Instruct:BF16
```

Lemonade

How to use drvivekpoojary/Open-GVP-Qwen2.5-7B-Instruct with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull drvivekpoojary/Open-GVP-Qwen2.5-7B-Instruct:BF16

Run and chat with the model

lemonade run user.Open-GVP-Qwen2.5-7B-Instruct-BF16

List all available models

lemonade list

Hermes Agent

How to use drvivekpoojary/Open-GVP-Qwen2.5-7B-Instruct with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf drvivekpoojary/Open-GVP-Qwen2.5-7B-Instruct:BF16

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default drvivekpoojary/Open-GVP-Qwen2.5-7B-Instruct:BF16

Run Hermes

hermes

Atomic Chat

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💊 Open-GVP-Qwen2.5-7B-Instruct

Domain-adapted Qwen2.5-7B 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

📖 Model Description

Open-GVP-Qwen2.5-7B-Instruct is a domain-specialized version of Qwen/Qwen2.5-7B-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

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

Format	Files	Best For
Merged Safetensors	`model-00001-of-00004.safetensors` (4 shards) + config	Transformers, vLLM, TGI, full-precision inference
GGUF BF16	`Open-GVP-Qwen2.5-7B-BF16.gguf`	Highest quality GGUF
GGUF Q8_0	`Open-GVP-Qwen2.5-7B-Q8_0.gguf`	Excellent quality / speed balance
GGUF Q6_K	`Open-GVP-Qwen2.5-7B-Q6_K.gguf`	Good quality with lower resource usage

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)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "drvivekpoojary/Open-GVP-Qwen2.5-7B-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-Qwen2.5-7B-Instruct:Q8_0

# Alternative options
ollama run hf.co/drvivekpoojary/Open-GVP-Qwen2.5-7B-Instruct:Q6_K
ollama run hf.co/drvivekpoojary/Open-GVP-Qwen2.5-7B-Instruct:BF16

3. llama.cpp

./llama-cli \
  -m Open-GVP-Qwen2.5-7B-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-Qwen2.5-7B-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

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

Property	Value
Base Model	Qwen/Qwen2.5-7B-Instruct
Fine-tuning Method	QLoRA
LoRA Rank	64
LoRA Alpha	128
LoRA Dropout	0.05
Training Data	~15,000 GVP Q&A pairs (all modules)
Epochs	4
Context Length	1024
Precision	bfloat16
Framework	LlamaFactory
Hardware	NVIDIA GPU (16 GB VRAM)

⚠️ 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

Downloads last month: -

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for drvivekpoojary/Open-GVP-Qwen2.5-7B-Instruct

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-7B-Instruct

Quantized

(386)

this model