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Sponsored by
APIMart
AtomicChat / Ornith-35B-MLX-5bit
Model's Last Updated: July 23 2026
text-generation
Introduction of Ornith-35B-MLX-5bit
Model Details of Ornith-35B-MLX-5bit
Ornith 1.0 35B , quantized to MLX 5-bit by 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.
This is the MLX 5-bit build for Apple Silicon (M-series). For llama.cpp/Ollama/CPU use the GGUF repo .
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 5-bit quant for Apple Silicon (~23.8 GB), built from the original weights. |
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 · 5-bit ← this · 6-bit · 8-bit
Run on Apple Silicon
pip install mlx-lm
mlx_lm.generate --model AtomicChat/ornith-35b-MLX-5bit --prompt "Write a quicksort in Python" --max-tokens 512
from mlx_lm import load, generate
model, tokenizer = load("AtomicChat/ornith-35b-MLX-5bit")
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
: search
AtomicChat/ornith-35b-MLX-5bit
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
-
Download
deepreinforce-ai/Ornith-1.0-35B(original weights). -
Convert + quantize to MLX with
mlx_lm.convert -q --q-bits 5 --q-group-size 64.
License
Released by DeepReinforce under the MIT license, globally accessible with no regional limits. Quantized to MLX by Atomic Chat.
Runs of AtomicChat Ornith-35B-MLX-5bit on huggingface.co
107
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More Information About Ornith-35B-MLX-5bit huggingface.co Model
Url of Ornith-35B-MLX-5bit
Ornith-35B-MLX-5bit huggingface.co Url
Provider of Ornith-35B-MLX-5bit huggingface.co
Other API from AtomicChat
Updated:August 19 2026
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