Instructions to use AtomicChat/ornith-35b-MLX-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use AtomicChat/ornith-35b-MLX-4bit 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("AtomicChat/ornith-35b-MLX-4bit") 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 AtomicChat/ornith-35b-MLX-4bit with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "AtomicChat/ornith-35b-MLX-4bit"
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "mlx-lm": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "AtomicChat/ornith-35b-MLX-4bit" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use AtomicChat/ornith-35b-MLX-4bit 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 "AtomicChat/ornith-35b-MLX-4bit"
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 AtomicChat/ornith-35b-MLX-4bit
Run Hermes
hermes
- OpenClaw new
How to use AtomicChat/ornith-35b-MLX-4bit with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "AtomicChat/ornith-35b-MLX-4bit"
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 "AtomicChat/ornith-35b-MLX-4bit" \ --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"
- MLX LM
How to use AtomicChat/ornith-35b-MLX-4bit with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "AtomicChat/ornith-35b-MLX-4bit"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "AtomicChat/ornith-35b-MLX-4bit" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "AtomicChat/ornith-35b-MLX-4bit", "messages": [ {"role": "user", "content": "Hello"} ] }'
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 "AtomicChat/ornith-35b-MLX-4bit" \
--custom-provider-id mlx-lm \
--custom-compatibility openai \
--custom-text-input \
--accept-risk \
--skip-healthRun OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
Ornith 1.0 35B, self-quantized to MLX by Atomic Chat. Built straight from DeepReinforce's original weights with a per-tensor importance matrix, so this is not a repack of somebody else's files. Runs fully offline.
Highlights
- 0.0B parameters: the weights this repo quantizes.
- Context length: 262,144 tokens (256K), as published by DeepReinforce.
- 40 layers: Mixture-of-Experts.
- Modalities: Text, Image.
- Full imatrix ladder: every quant is calibrated with an importance matrix.
- State-of-the-Art Coding Agents: Available in 9B-Dense, 31B-Dense, 35B-MoE, and 397B-MoE (post-trained on top of Gemma 4 and Qwen 3.5), achieving state-of-the-art performance among open-source models of comparable size on coding benchmarks such as Terminal-Bench 2.1, SWE-Bench, NL2Repo and OpenClaw.
- Self-Improving Training Framework: Ornith-1.0 employs RL to learn to generate not only solution rollouts, but also the scallfold that drive those rollouts. By jointly optimizing the scaffold and the resulting solution, the model discovers better search trajectories and generates higher-quality solutions.
These MLXs are self-quantized from the original weights, not a repack. The importance matrix keeps low-bit quants closer to the full-precision model.
Model Overview
| Property | Value |
|---|---|
| Base model | deepreinforce-ai/Ornith-1.0-35B |
| Parameters | 0.0B |
| Layers | 40 |
| Experts | 256 routed (top-8) |
| Context length | 262,144 tokens (256K) |
| Vocabulary | 248,320 |
| Modalities | Text, Image |
| Architecture | Mixture-of-Experts, 256 experts (top-8), 16 attention heads over 2 KV heads, Qwen3_5MoeForConditionalGeneration |
| This repo | MLX weights |
Get started
- Atomic Chat: search
AtomicChat/ornith-35b-MLX-4bitand hit Use this model. - mlx-lm:
mlx_lm.generate --model AtomicChat/ornith-35b-MLX-4bit --prompt "Hello" --max-tokens 512 - Server:
mlx_lm.server --model AtomicChat/ornith-35b-MLX-4bit --port 8080
Best practices
| Parameter | Value |
|---|---|
| temperature | 1.0 |
| top_p | 1.0 |
| top_k | 20 |
DeepReinforce's recommended sampling configuration for deepreinforce-ai/Ornith-1.0-35B.
How these were made
- Download
deepreinforce-ai/Ornith-1.0-35B(original weights). - Convert and quantize with
mlx_lm.converton our pipeline.
License
Original model by DeepReinforce, released under the MIT license. Full terms: MIT. Quantized by Atomic Chat.
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Model tree for AtomicChat/ornith-35b-MLX-4bit
Base model
deepreinforce-ai/Ornith-1.0-35B


Start the MLX server
# Install MLX LM: uv tool install mlx-lm# Start a local OpenAI-compatible server: mlx_lm.server --model "AtomicChat/ornith-35b-MLX-4bit"