Instructions to use AtomicChat/Ornith-35B-MLX-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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

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"}
     ]
   }'

worthant commited on 7 days ago

Commit

2a54826

verified ·

1 Parent(s): 4b332f5

forge: regenerate the model card

Browse files

Files changed (1) hide show

README.md +34 -54

README.md CHANGED Viewed

@@ -1,6 +1,7 @@
 ---
 license: mit
-license_link: https://huggingface.co/deepreinforce-ai/Ornith-1.0-35B
 base_model:
 - deepreinforce-ai/Ornith-1.0-35B
 base_model_relation: quantized
@@ -10,100 +11,79 @@ library_name: mlx
 tags:
 - atomic-chat
 - ornith
-- deepreinforce
-- coding
-- agent
 - mlx
 - apple-silicon
 - quantized
-- qwen3_5
 ---
 <center>
 <div style="display:flex; justify-content:center; align-items:center; gap:2%; max-width:560px; margin:0 auto;">
-<a href="https://atomic.chat"><img src="https://huggingface.co/AtomicChat/ornith-35b-GGUF/resolve/main/pill_atomic_v3.png" alt="Atomic Chat" style="width:100%; height:auto; max-width:186px;"></a>
-<a href="https://discord.gg/8wGSsvmg4V"><img src="https://huggingface.co/AtomicChat/ornith-35b-GGUF/resolve/main/pill_discord_v3.png" alt="Discord" style="width:100%; height:auto; max-width:184px;"></a>
-<a href="https://github.com/AtomicBot-ai/Atomic-Chat"><img src="https://huggingface.co/AtomicChat/ornith-35b-GGUF/resolve/main/pill_github_v3.png" alt="GitHub" style="width:100%; height:auto; max-width:141px;"></a>
 </div>
 <br/>
-<img src="https://huggingface.co/AtomicChat/ornith-35b-GGUF/resolve/main/hero.png" alt="Ornith 1.0 35B" style="width:520px; max-width:100%; height:auto; margin-bottom:0.6em;"/>
 <div style="display:flex; justify-content:center; gap:0.5em;">
 <a href="https://huggingface.co/deepreinforce-ai/Ornith-1.0-35B"><strong>Base model: deepreinforce-ai/Ornith-1.0-35B</strong></a>
 </div>
 </center>
-**Ornith 1.0 35B**, quantized to **MLX 4-bit** by [Atomic Chat](https://atomic.chat) for Apple Silicon. Built straight from DeepReinforce's original weights. Runs fully offline on your Mac.
 ## Highlights
-- **A self-improving open-source family for agentic coding** from DeepReinforce, built for tool-calling and terminal-based coding agents.
-- **Sparse Mixture-of-Experts**: 256 routed experts with 8 active per token plus a shared expert, across 40 layers (`qwen3_5_moe`).
-- **Post-trained on top of Gemma 4 and Qwen 3.5**, the mid-size member of the Ornith 1.0 lineup.
-- **Strong agentic coding scores**: 75.6 on SWE-bench Verified and 64.2 on Terminal-Bench 2.1 (Terminus-2).
-- **262,144-token native context** for long files and multi-step agent traces.
-- **Pure open**: MIT licensed, globally accessible with no regional limits.
-- **Full quant ladder** with an importance matrix on every quant over [`calibration_datav3`](https://gist.github.com/bartowski1182/eb213dccb3571f863da82e99418f81e8).
 > [!NOTE]
-> This is the **MLX 4-bit** build for Apple Silicon (M-series). For llama.cpp/Ollama/CPU use the [GGUF repo](https://huggingface.co/AtomicChat/ornith-35b-GGUF).
 ## Model Overview
 | Property | Value |
 |---|---|
 | Base model | `deepreinforce-ai/Ornith-1.0-35B` |
-| Total parameters | ~35B total (MoE; model name). Active per token not stated |
 | Layers | 40 |
-| Experts | 256 routed + 1 shared, 8 active per token |
-| Context length | 262,144 |
-| Architecture | `qwen3_5_moe` sparse MoE, post-trained on Gemma 4 and Qwen 3.5 |
-| This repo | MLX **4-bit** quant for Apple Silicon (~19.5 GB), built from the original weights. |
-<img src="https://huggingface.co/AtomicChat/ornith-35b-GGUF/resolve/main/benchmark.png" alt="Ornith 1.0 35B benchmarks" style="width:100%; max-width:900px;"/>
-Scores are DeepReinforce's published results for the full-precision base `deepreinforce-ai/Ornith-1.0-35B`. MLX quants run the same model locally; lower bit-widths trade a little accuracy for size/speed.
-## MLX quants in this series
-[4-bit](https://huggingface.co/AtomicChat/ornith-35b-MLX-4bit) ← this · [5-bit](https://huggingface.co/AtomicChat/ornith-35b-MLX-5bit) · [6-bit](https://huggingface.co/AtomicChat/ornith-35b-MLX-6bit) · [8-bit](https://huggingface.co/AtomicChat/ornith-35b-MLX-8bit)
-## Run on Apple Silicon
-```bash
-pip install mlx-lm
-mlx_lm.generate --model AtomicChat/ornith-35b-MLX-4bit --prompt "Write a quicksort in Python" --max-tokens 512
-```
-```python
-from mlx_lm import load, generate
-model, tokenizer = load("AtomicChat/ornith-35b-MLX-4bit")
-msg = [{"role": "user", "content": "Write a quicksort in Python"}]
-prompt = tokenizer.apply_chat_template(msg, add_generation_prompt=True)
-print(generate(model, tokenizer, prompt=prompt, max_tokens=512, verbose=True))
-```
-Or open it in **[Atomic Chat](https://atomic.chat)**: search `AtomicChat/ornith-35b-MLX-4bit` and hit **Use this model**.
-## Recommended sampling
 | Parameter | Value |
 |---|---|
-| temperature | 0.6 |
-| top_p | 0.95 |
 | top_k | 20 |
-DeepReinforce's recommended sampling parameters. The card notes that `temperature=1.0` reproduces the reported benchmark setup.
-## How this was made
 1. Download `deepreinforce-ai/Ornith-1.0-35B` (original weights).
-2. Convert + quantize to MLX with `mlx_lm.convert -q --q-bits 4 --q-group-size 64`.
 ## License
-Released by DeepReinforce under the MIT license, globally accessible with no regional limits. Quantized to MLX by Atomic Chat.

 ---
 license: mit
+license_link: https://huggingface.co/deepreinforce-ai/Ornith-1.0-35B/blob/main/LICENSE
+thumbnail: https://huggingface.co/AtomicChat/ornith-35b-MLX-4bit/resolve/main/hero.png
 base_model:
 - deepreinforce-ai/Ornith-1.0-35B
 base_model_relation: quantized
 tags:
 - atomic-chat
 - ornith
+- deepreinforce-ai
 - mlx
 - apple-silicon
 - quantized
 ---
 <center>
 <div style="display:flex; justify-content:center; align-items:center; gap:2%; max-width:560px; margin:0 auto;">
+<a href="https://atomic.chat" style="flex:0 1 auto; min-width:0;"><img src="https://huggingface.co/AtomicChat/ornith-35b-MLX-4bit/resolve/main/pill_atomic_v3.png" alt="Atomic Chat" style="width:100%; height:auto; max-width:186px;"></a>
+<a href="https://discord.gg/8wGSsvmg4V" style="flex:0 1 auto; min-width:0;"><img src="https://huggingface.co/AtomicChat/ornith-35b-MLX-4bit/resolve/main/pill_discord_v3.png" alt="Join Discord" style="width:100%; height:auto; max-width:184px;"></a>
+<a href="https://github.com/AtomicBot-ai/Atomic-Chat" style="flex:0 1 auto; min-width:0;"><img src="https://huggingface.co/AtomicChat/ornith-35b-MLX-4bit/resolve/main/pill_github_v3.png" alt="GitHub" style="width:100%; height:auto; max-width:141px;"></a>
 </div>
 <br/>
+<img src="https://huggingface.co/AtomicChat/ornith-35b-MLX-4bit/resolve/main/hero.png" alt="Ornith 1.0 35B" style="width:100%; max-width:100%; height:auto; margin-bottom:0.6em;"/>
 <div style="display:flex; justify-content:center; gap:0.5em;">
 <a href="https://huggingface.co/deepreinforce-ai/Ornith-1.0-35B"><strong>Base model: deepreinforce-ai/Ornith-1.0-35B</strong></a>
 </div>
 </center>
+**Ornith 1.0 35B**, self-quantized to MLX by [Atomic Chat](https://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.
 > [!NOTE]
+> 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](https://atomic.chat):** search `AtomicChat/ornith-35b-MLX-4bit` and 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
 1. Download `deepreinforce-ai/Ornith-1.0-35B` (original weights).
+2. Convert and quantize with `mlx_lm.convert` on our pipeline.
 ## License
+Original model by DeepReinforce, released under the MIT license. Full terms: [MIT](https://huggingface.co/deepreinforce-ai/Ornith-1.0-35B/blob/main/LICENSE). Quantized by Atomic Chat.