Instructions to use protocolsyncllc/medgap-qwen-3.8-27b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use protocolsyncllc/medgap-qwen-3.8-27b 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 protocolsyncllc/medgap-qwen-3.8-27b
# Run inference directly in the terminal:
llama cli -hf protocolsyncllc/medgap-qwen-3.8-27b

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf protocolsyncllc/medgap-qwen-3.8-27b
# Run inference directly in the terminal:
llama cli -hf protocolsyncllc/medgap-qwen-3.8-27b

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 protocolsyncllc/medgap-qwen-3.8-27b
# Run inference directly in the terminal:
./llama-cli -hf protocolsyncllc/medgap-qwen-3.8-27b

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 protocolsyncllc/medgap-qwen-3.8-27b
# Run inference directly in the terminal:
./build/bin/llama-cli -hf protocolsyncllc/medgap-qwen-3.8-27b

Use Docker

docker model run hf.co/protocolsyncllc/medgap-qwen-3.8-27b

Llama new
LM Studio
Jan

vLLM

How to use protocolsyncllc/medgap-qwen-3.8-27b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "protocolsyncllc/medgap-qwen-3.8-27b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "protocolsyncllc/medgap-qwen-3.8-27b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/protocolsyncllc/medgap-qwen-3.8-27b

Ollama
How to use protocolsyncllc/medgap-qwen-3.8-27b with Ollama:
```
ollama run hf.co/protocolsyncllc/medgap-qwen-3.8-27b
```
Unsloth Desktop

How to use protocolsyncllc/medgap-qwen-3.8-27b with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf protocolsyncllc/medgap-qwen-3.8-27b

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": "protocolsyncllc/medgap-qwen-3.8-27b"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use protocolsyncllc/medgap-qwen-3.8-27b with Docker Model Runner:
```
docker model run hf.co/protocolsyncllc/medgap-qwen-3.8-27b
```

Lemonade

How to use protocolsyncllc/medgap-qwen-3.8-27b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull protocolsyncllc/medgap-qwen-3.8-27b

Run and chat with the model

lemonade run user.medgap-qwen-3.8-27b-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use protocolsyncllc/medgap-qwen-3.8-27b with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf protocolsyncllc/medgap-qwen-3.8-27b

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 protocolsyncllc/medgap-qwen-3.8-27b

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use protocolsyncllc/medgap-qwen-3.8-27b with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf protocolsyncllc/medgap-qwen-3.8-27b

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 "protocolsyncllc/medgap-qwen-3.8-27b" \
  --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"

Gemma 4 12B FDA Warning Letter

This is a fine tune of Gemma 4 12B on FDA warning letters for medical devices.

Base model: google/gemma-4-12B-it
Dataset: medical-devices.jsonl
Hardware: 2x RTX 3090 48gb VRAM
Library: Unsloth
Epochs: 3 Technique: LoRA
Learning Rate: 2e-4
Inference GGUF: medgap-gemma-4-12b_q8.gguf

Downloads last month: 507

Safetensors

Model size

28B params

Tensor type

BF16

Model tree for protocolsyncllc/medgap-qwen-3.8-27b

Base model

google/gemma-4-12B

Finetuned

google/gemma-4-12B-it

Quantized

(385)

this model

Quantizations

1 model

protocolsyncllc
/

medgap-qwen-3.8-27b

Gemma 4 12B FDA Warning Letter

Model tree for protocolsyncllc/medgap-qwen-3.8-27b

Dataset used to train protocolsyncllc/medgap-qwen-3.8-27b