Instructions to use drvivekpoojary/Open-GVP-Qwen2.5-14B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use drvivekpoojary/Open-GVP-Qwen2.5-14B-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="drvivekpoojary/Open-GVP-Qwen2.5-14B-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-Qwen2.5-14B-Instruct")
model = AutoModelForCausalLM.from_pretrained("drvivekpoojary/Open-GVP-Qwen2.5-14B-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-Qwen2.5-14B-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-Qwen2.5-14B-Instruct:BF16
# Run inference directly in the terminal:
llama cli -hf drvivekpoojary/Open-GVP-Qwen2.5-14B-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-Qwen2.5-14B-Instruct:BF16
# Run inference directly in the terminal:
llama cli -hf drvivekpoojary/Open-GVP-Qwen2.5-14B-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-Qwen2.5-14B-Instruct:BF16
# Run inference directly in the terminal:
./llama-cli -hf drvivekpoojary/Open-GVP-Qwen2.5-14B-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-Qwen2.5-14B-Instruct:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf drvivekpoojary/Open-GVP-Qwen2.5-14B-Instruct:BF16

Use Docker

docker model run hf.co/drvivekpoojary/Open-GVP-Qwen2.5-14B-Instruct:BF16

LM Studio
Jan

vLLM

How to use drvivekpoojary/Open-GVP-Qwen2.5-14B-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-Qwen2.5-14B-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-Qwen2.5-14B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/drvivekpoojary/Open-GVP-Qwen2.5-14B-Instruct:BF16

SGLang

How to use drvivekpoojary/Open-GVP-Qwen2.5-14B-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-Qwen2.5-14B-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-Qwen2.5-14B-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-Qwen2.5-14B-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-Qwen2.5-14B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use drvivekpoojary/Open-GVP-Qwen2.5-14B-Instruct with Ollama:
```
ollama run hf.co/drvivekpoojary/Open-GVP-Qwen2.5-14B-Instruct:BF16
```
Unsloth Desktop

How to use drvivekpoojary/Open-GVP-Qwen2.5-14B-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-Qwen2.5-14B-Instruct:BF16

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/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-Qwen2.5-14B-Instruct:BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use drvivekpoojary/Open-GVP-Qwen2.5-14B-Instruct with Docker Model Runner:
```
docker model run hf.co/drvivekpoojary/Open-GVP-Qwen2.5-14B-Instruct:BF16
```

Lemonade

How to use drvivekpoojary/Open-GVP-Qwen2.5-14B-Instruct with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull drvivekpoojary/Open-GVP-Qwen2.5-14B-Instruct:BF16

Run and chat with the model

lemonade run user.Open-GVP-Qwen2.5-14B-Instruct-BF16

List all available models

lemonade list

Hermes Agent

How to use drvivekpoojary/Open-GVP-Qwen2.5-14B-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-Qwen2.5-14B-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-Qwen2.5-14B-Instruct:BF16

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use drvivekpoojary/Open-GVP-Qwen2.5-14B-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-Qwen2.5-14B-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-Qwen2.5-14B-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"

Open-GVP-Qwen2.5-14B-Instruct / README.md

drvivekpoojary

Update README.md

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	---
	license: apache-2.0
	language:
	- en
	base_model: Qwen/Qwen2.5-14B-Instruct
	tags:
	- pharmacovigilance
	- gvp
	- regulatory
	- qwen2.5
	- medical
	- drug-safety
	- instruction-tuning
	- gguf
	- safetensors
	- llama.cpp
	- ollama
	pipeline_tag: text-generation
	library_name: transformers
	---

	# 💊 Open-GVP-Qwen2.5-14B-Instruct

	> Domain-adapted Qwen2.5-14B 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-Qwen2.5-14B-Instruct is a domain-specialized version of [Qwen/Qwen2.5-14B-Instruct](https://huggingface.co/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

	\| 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-Qwen2.5-14B-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` \| 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)

	```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

	\| 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/Qwen2.5-14B-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 \|
	\| 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