Instructions to use M37labsorg/ade-extract-3b-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use M37labsorg/ade-extract-3b-v1 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 M37labsorg/ade-extract-3b-v1:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf M37labsorg/ade-extract-3b-v1:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf M37labsorg/ade-extract-3b-v1:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf M37labsorg/ade-extract-3b-v1:Q4_K_M

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 M37labsorg/ade-extract-3b-v1:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf M37labsorg/ade-extract-3b-v1:Q4_K_M

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 M37labsorg/ade-extract-3b-v1:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf M37labsorg/ade-extract-3b-v1:Q4_K_M

Use Docker

docker model run hf.co/M37labsorg/ade-extract-3b-v1:Q4_K_M

LM Studio
Jan
Ollama
How to use M37labsorg/ade-extract-3b-v1 with Ollama:
```
ollama run hf.co/M37labsorg/ade-extract-3b-v1:Q4_K_M
```
Unsloth Desktop

How to use M37labsorg/ade-extract-3b-v1 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf M37labsorg/ade-extract-3b-v1:Q4_K_M

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": "M37labsorg/ade-extract-3b-v1:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use M37labsorg/ade-extract-3b-v1 with Docker Model Runner:
```
docker model run hf.co/M37labsorg/ade-extract-3b-v1:Q4_K_M
```

Lemonade

How to use M37labsorg/ade-extract-3b-v1 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull M37labsorg/ade-extract-3b-v1:Q4_K_M

Run and chat with the model

lemonade run user.ade-extract-3b-v1-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use M37labsorg/ade-extract-3b-v1 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf M37labsorg/ade-extract-3b-v1:Q4_K_M

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 M37labsorg/ade-extract-3b-v1:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use M37labsorg/ade-extract-3b-v1 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf M37labsorg/ade-extract-3b-v1:Q4_K_M

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 "M37labsorg/ade-extract-3b-v1:Q4_K_M" \
  --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"

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

ade-extract-3b-v3

Fine-tuned locally with SLM Forge on 2026-09-25.

Goal: Rebuild ADE Extract as a consistent JSON-only information extractor: report only facts explicitly stated in the text, use null for missing optional details, and return an empty events list when no adverse effect is stated. Train a clean adapter from the original base; treat the result as an experimental extraction aid, not medical advice, and do not claim commercial clearance for the ADE Corpus v2 data unless its license is verified. Base model: mlx-community/Qwen2.5-3B-Instruct-4bit Runs on: Apple Silicon Macs with 8 GB unified memory or more.

Lineage

SFT (job 50): train_loss 0.018, peak_mem_gb 7.15, val_loss_start 0.875, val_loss 0.025

Usage

pip install mlx-lm
mlx_lm.generate --model engagepy/ade-extract-3b-v3 --prompt "Hello"

Recommended sampling

{
  "temperature": 0.7,
  "top_p": 0.95
}

System prompt

Extract only what the text explicitly states. Return only valid JSON in exactly this shape: {"events":[{"drug":null,"adverse_effect":"...","evidence_span":"...","severity":null,"dosage":null,"action_taken":null}]}. Include one event per explicitly described adverse effect. Fill drug, adverse_effect, severity, dosage, and action_taken only when each is stated in the text; otherwise use JSON null. Do not infer a drug-event link from timing alone, or infer causality, severity, dosage, or actions. Quote the shortest exact, contiguous evidence span supporting the event. If no adverse effect is stated, return {"events":[]}. This is an extraction aid, not medical advice.

Run it anywhere (GGUF)

GGUF builds of this model, for llama.cpp, Ollama and LM Studio:

ade-extract-3b-v3-Q4_K_M.gguf (Q4_K_M, 1.80 GB)
ade-extract-3b-v3-Q8_0.gguf (Q8_0, 3.06 GB)

llama-cli -m ade-extract-3b-v3-Q4_K_M.gguf -p "Hello"   # llama.cpp
ollama run hf.co/engagepy/ade-extract-3b-v3:Q4_K_M   # Ollama

In LM Studio, search for the repository or load the .gguf file.

Downloads last month: -

Safetensors

Model size

3B params

Tensor type

U32

F16

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