Instructions to use RecurvAI/Recurv-Medical-Lllama with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RecurvAI/Recurv-Medical-Lllama with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="RecurvAI/Recurv-Medical-Lllama",
	filename="recurv_medical_llama.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use RecurvAI/Recurv-Medical-Lllama with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf RecurvAI/Recurv-Medical-Lllama
# Run inference directly in the terminal:
llama-cli -hf RecurvAI/Recurv-Medical-Lllama

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf RecurvAI/Recurv-Medical-Lllama
# Run inference directly in the terminal:
llama-cli -hf RecurvAI/Recurv-Medical-Lllama

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 RecurvAI/Recurv-Medical-Lllama
# Run inference directly in the terminal:
./llama-cli -hf RecurvAI/Recurv-Medical-Lllama

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 RecurvAI/Recurv-Medical-Lllama
# Run inference directly in the terminal:
./build/bin/llama-cli -hf RecurvAI/Recurv-Medical-Lllama

Use Docker

docker model run hf.co/RecurvAI/Recurv-Medical-Lllama

LM Studio
Jan

vLLM

How to use RecurvAI/Recurv-Medical-Lllama with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/RecurvAI/Recurv-Medical-Lllama

Ollama
How to use RecurvAI/Recurv-Medical-Lllama with Ollama:
```
ollama run hf.co/RecurvAI/Recurv-Medical-Lllama
```

Unsloth Studio new

How to use RecurvAI/Recurv-Medical-Lllama 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 RecurvAI/Recurv-Medical-Lllama 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 RecurvAI/Recurv-Medical-Lllama to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for RecurvAI/Recurv-Medical-Lllama to start chatting

Docker Model Runner
How to use RecurvAI/Recurv-Medical-Lllama with Docker Model Runner:
```
docker model run hf.co/RecurvAI/Recurv-Medical-Lllama
```

Lemonade

How to use RecurvAI/Recurv-Medical-Lllama with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull RecurvAI/Recurv-Medical-Lllama

Run and chat with the model

lemonade run user.Recurv-Medical-Lllama-{{QUANT_TAG}}

List all available models

lemonade list

🧠 Recurv-Medical-Llama Model

Overview

The Recurv-Medical-Llama model is a fine-tuned version of Meta's LLaMa 3.1 8B, developed to provide precise and contextual assistance for healthcare professionals and researchers. This model excels in answering medical queries, assisting in anamnesis, and generating detailed explanations tailored for medical scenarios, leveraging state-of-the-art instruction tuning techniques.

(Knowledge cut-off date: 22th January, 2025)

🎯 Key Features

Optimized for medical-specific queries across various specialties.
Fine-tuned for clinical and research-oriented workflows.
Lightweight parameter-efficient fine-tuning with LoRA (Low-Rank Adaptation).
Multi-turn conversation support for context-rich interactions.
Generates comprehensive answers and evidence-based suggestions.

🚀 Model Card

Parameter	Details
Base Model	Meta LLaMa 3.1 8B
Fine-Tuning Framework	LoRA
Dataset Size	67,299 high-quality Q&A pairs
Context Length	4,096 tokens
Training Steps	100,000
Model Size	8 billion parameters

📊 Model Architecture

Dataset Sources

The dataset comprises high-quality Q&A pairs curated from medical textbooks, research papers, and clinical guidelines.

Source	Description
PubMed	Extracted insights from open-access medical research.
Clinical Guidelines	Data sourced from WHO, CDC, and specialty-specific guidelines.
EHR-Simulated Data	Synthetic datasets modeled on real-world patient records for anamnesis workflows.

🛠️ Installation and Usage

1. Installation

pip install llama-cpp-python --prefer-binary --extra-index-url=https://jllllll.github.io/llama-cpp-python-cuBLAS-wheels/AVX2/cu118

2. Load the Model

from llama_cpp import Llama

llm = Llama(
    model_path="recurv_medical_llama.gguf",
    n_ctx=2048,         # Context window
    n_threads=4         # Number of CPU threads to use
)

3. Run Inference

prompt = "What is Paracetamol?"
output = llm(
    prompt,
    max_tokens=256,     # Maximum number of tokens to generate
    temperature=0.5,    # Controls randomness (0.0 = deterministic, 1.0 = creative)
    top_p=0.95,         # Nucleus sampling parameter
    stop=["###"],       # Optional stop words
    echo=True           # Include prompt in the output
)

# Print the generated text
print(output['choices'][0]['text'])

🌟 Try The Model

🚀 Recurv-Medical-Llama on Our Website

🙌 Contributing

We welcome contributions to enhance Recurv-Medical-Llama. You can:

Share feedback or suggestions on the Hugging Face Model Hub
Submit pull requests or issues for model improvement.

📜 License

This model is licensed under the MIT License.

📞 Community

For questions or support, connect with us via:

Twitter: RecurvAI
Email: support@recurvai.com

🤝 Acknowledgments

Special thanks to the medical community and researchers for their valuable insights and support in building this model. Together, we’re advancing AI in healthcare.

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GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Model tree for RecurvAI/Recurv-Medical-Lllama

Base model

meta-llama/Llama-3.1-8B

Finetuned

meta-llama/Llama-3.1-8B-Instruct

Quantized

(637)

this model

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Recurv-Medical-Lllama