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

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use RecurvAI/Recurv-Medical-Lllama 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 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 serve -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

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

Atomic Chat

Recurv-Medical-Lllama

File size: 758 Bytes

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

llm = Llama(
    model_path="recurv_llama_13B.gguf",
    n_ctx=2048,         # Context window
    n_threads=4         # Number of CPU threads to use
)

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'])