Instructions to use CWClabs/CWC-Mistral-Nemo-12B-V2-q4_k_m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CWClabs/CWC-Mistral-Nemo-12B-V2-q4_k_m with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="CWClabs/CWC-Mistral-Nemo-12B-V2-q4_k_m",
	filename="CWC-Mistral-Nemo-12B-v2-GGUF-q4_k_m-health-nutrition-natural-medicine.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use CWClabs/CWC-Mistral-Nemo-12B-V2-q4_k_m 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 CWClabs/CWC-Mistral-Nemo-12B-V2-q4_k_m:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf CWClabs/CWC-Mistral-Nemo-12B-V2-q4_k_m:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf CWClabs/CWC-Mistral-Nemo-12B-V2-q4_k_m:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf CWClabs/CWC-Mistral-Nemo-12B-V2-q4_k_m: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 CWClabs/CWC-Mistral-Nemo-12B-V2-q4_k_m:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf CWClabs/CWC-Mistral-Nemo-12B-V2-q4_k_m: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 CWClabs/CWC-Mistral-Nemo-12B-V2-q4_k_m:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf CWClabs/CWC-Mistral-Nemo-12B-V2-q4_k_m:Q4_K_M

Use Docker

docker model run hf.co/CWClabs/CWC-Mistral-Nemo-12B-V2-q4_k_m:Q4_K_M

LM Studio
Jan
Ollama
How to use CWClabs/CWC-Mistral-Nemo-12B-V2-q4_k_m with Ollama:
```
ollama run hf.co/CWClabs/CWC-Mistral-Nemo-12B-V2-q4_k_m:Q4_K_M
```

Unsloth Studio

How to use CWClabs/CWC-Mistral-Nemo-12B-V2-q4_k_m 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 CWClabs/CWC-Mistral-Nemo-12B-V2-q4_k_m 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 CWClabs/CWC-Mistral-Nemo-12B-V2-q4_k_m to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for CWClabs/CWC-Mistral-Nemo-12B-V2-q4_k_m to start chatting

How to use CWClabs/CWC-Mistral-Nemo-12B-V2-q4_k_m with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf CWClabs/CWC-Mistral-Nemo-12B-V2-q4_k_m:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "CWClabs/CWC-Mistral-Nemo-12B-V2-q4_k_m:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use CWClabs/CWC-Mistral-Nemo-12B-V2-q4_k_m with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf CWClabs/CWC-Mistral-Nemo-12B-V2-q4_k_m: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 CWClabs/CWC-Mistral-Nemo-12B-V2-q4_k_m:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use CWClabs/CWC-Mistral-Nemo-12B-V2-q4_k_m with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf CWClabs/CWC-Mistral-Nemo-12B-V2-q4_k_m: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 "CWClabs/CWC-Mistral-Nemo-12B-V2-q4_k_m: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"

Docker Model Runner
How to use CWClabs/CWC-Mistral-Nemo-12B-V2-q4_k_m with Docker Model Runner:
```
docker model run hf.co/CWClabs/CWC-Mistral-Nemo-12B-V2-q4_k_m:Q4_K_M
```

Lemonade

How to use CWClabs/CWC-Mistral-Nemo-12B-V2-q4_k_m with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull CWClabs/CWC-Mistral-Nemo-12B-V2-q4_k_m:Q4_K_M

Run and chat with the model

lemonade run user.CWC-Mistral-Nemo-12B-V2-q4_k_m-Q4_K_M

List all available models

lemonade list

Model created by non-profit CWC (Consumer Wellness Center) with strong emphasis on curated training data in the realms of nutrition, natural health, wellness, disease prevention, phytochemistry and similar topics.

Built on open-mistral-nemo 12B, with credit to Mistral, this model achieves very strong vector db alterations through a variety of SFT techniques to overcome the pro-pharma bias found in nearly all base models.

The curated data set for training consists of over 100 million pages of content selected through algorithmic classification, including science papers, transcripts, book text, article text and more. No user-generated comments or chat data were used.

This allows the model to achieve RAG-like domain knowledge without using RAG. Out of the box, with default system prompts, it is outperforming far larger and more complex models with RAG layers.

Recommended context window of 8192. Flash attention supported.

This model is provided by CWC, and its development was led by Mike Adams.

Additional models and quants will also be published.

Downloads last month: 630

GGUF

Model size

12B params

Architecture

llama

Hardware compatibility

4-bit

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