Instructions to use imaadd05/Mellum2.1-12B-A2.5B-Thinking-mlx-6bit-g64 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use imaadd05/Mellum2.1-12B-A2.5B-Thinking-mlx-6bit-g64 with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("imaadd05/Mellum2.1-12B-A2.5B-Thinking-mlx-6bit-g64") prompt = "Write a story about Einstein" messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Pi
How to use imaadd05/Mellum2.1-12B-A2.5B-Thinking-mlx-6bit-g64 with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "imaadd05/Mellum2.1-12B-A2.5B-Thinking-mlx-6bit-g64"
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "mlx-lm": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "imaadd05/Mellum2.1-12B-A2.5B-Thinking-mlx-6bit-g64" } ] } } }Run Pi
# Start Pi in your project directory: pi
- MLX LM
How to use imaadd05/Mellum2.1-12B-A2.5B-Thinking-mlx-6bit-g64 with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "imaadd05/Mellum2.1-12B-A2.5B-Thinking-mlx-6bit-g64"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "imaadd05/Mellum2.1-12B-A2.5B-Thinking-mlx-6bit-g64" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "imaadd05/Mellum2.1-12B-A2.5B-Thinking-mlx-6bit-g64", "messages": [ {"role": "user", "content": "Hello"} ] }' - Hermes Agent
How to use imaadd05/Mellum2.1-12B-A2.5B-Thinking-mlx-6bit-g64 with Hermes Agent:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "imaadd05/Mellum2.1-12B-A2.5B-Thinking-mlx-6bit-g64"
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 imaadd05/Mellum2.1-12B-A2.5B-Thinking-mlx-6bit-g64
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use imaadd05/Mellum2.1-12B-A2.5B-Thinking-mlx-6bit-g64 with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "imaadd05/Mellum2.1-12B-A2.5B-Thinking-mlx-6bit-g64"
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 "imaadd05/Mellum2.1-12B-A2.5B-Thinking-mlx-6bit-g64" \ --custom-provider-id mlx-lm \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
Download conversion.json from imaadd05/Mellum2.1-12B-A2.5B-Thinking-mlx-6bit-g64: direct link, hf CLI and curl.
- Browser
- Download file 2.9 kB
-
https://huggingface.co/imaadd05/Mellum2.1-12B-A2.5B-Thinking-mlx-6bit-g64/resolve/main/conversion.json
- Command line
-
hf download hf://imaadd05/Mellum2.1-12B-A2.5B-Thinking-mlx-6bit-g64/conversion.json
-
curl -L -o conversion.json https://huggingface.co/imaadd05/Mellum2.1-12B-A2.5B-Thinking-mlx-6bit-g64/resolve/main/conversion.json
2.9 kB
| { | |
| "source": "JetBrains/Mellum2.1-12B-A2.5B-Thinking", | |
| "source_revision": "92ddae9fc7665e9f801d141d2e5a6b2caf2460c4", | |
| "created_utc": "2026-10-09T08:32:06.014227+00:00", | |
| "quantization": { | |
| "mode": "affine", | |
| "bits": 6, | |
| "group_size": 64, | |
| "router_bits": 8, | |
| "non_quantized_dtype": "bfloat16" | |
| }, | |
| "device": "gpu", | |
| "platform": "Linux-7.0.0-38-generic-x86_64-with-glibc2.39", | |
| "python": "3.12.3", | |
| "packages": { | |
| "mlx": "0.32.3", | |
| "mlx-lm": "0.32.0", | |
| "transformers": "5.19.0", | |
| "huggingface-hub": "1.33.0", | |
| "safetensors": "0.8.0", | |
| "numpy": "2.5.3" | |
| }, | |
| "source_sha256": { | |
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| "special_tokens_map.json": "20e1ead0b0986b55989aa8116bafe63efcbae0daf8ed047970cd28b36aada4d9", | |
| "tokenizer.json": "58548a346eb073e5132bf7d8ad17dc6971bca36ade378ca4d2bfbc49bf60da2a", | |
| "tokenizer_config.json": "e2950960d2a3040a1938e6357c8c3628d531133c464286a7869b2e0504fcc59e" | |
| }, | |
| "weight_bytes": 9873101418, | |
| "validation_status": "pending" | |
| } | |