mlx-community / Ornith-1.5-9B-OptiQ-4bit

huggingface.co
Total runs: 785
24-hour runs: 0
7-day runs: 740
30-day runs: 740
Model's Last Updated: August 26 2026
text-generation

Introduction of Ornith-1.5-9B-OptiQ-4bit

Model Details of Ornith-1.5-9B-OptiQ-4bit

mlx-community/Ornith-1.5-9B-OptiQ-4bit

Built with mlx-optiq , the MLX-native toolkit to quantize, fine-tune, and serve LLMs locally on Apple Silicon, no PyTorch and no cloud. All OptiQ quants · Docs

OptiQ mixed-precision quant of ornith-ai/Ornith-1.5-9B-MLX , a 9B reasoning model. 7 GB on disk.

What it is
Property Value
Base ornith-ai/Ornith-1.5-9B-MLX (Qwen3.5, 9B, 32 layers)
Method OptiQ mixed-precision, per-layer 4/8-bit
Bit allocation Reused from the Ornith-1.0-9B OptiQ recipe : same architecture and layer count, so the per-layer sensitivity ranking transfers directly and no per-model sweep is needed
Layer split 116 components at 4-bit, 134 at 8-bit
Group size 64
On disk 7 GB

Following the naming llama.cpp uses for its mixed quants, the "4bit" label denotes the family, not the weighted average.

Run it
pip install "mlx-optiq>=0.4.28"
import optiq  # registers the arch
from mlx_lm import load, generate

model, tok = load("mlx-community/Ornith-1.5-9B-OptiQ-4bit")
prompt = tok.apply_chat_template(
    [{"role": "user", "content": "Explain mixed-precision quantization in two sentences."}],
    tokenize=False, add_generation_prompt=True,
)
print(generate(model, tok, prompt=prompt, max_tokens=400))

For an OpenAI- and Anthropic-compatible endpoint with mixed-precision KV cache:

optiq serve --model mlx-community/Ornith-1.5-9B-OptiQ-4bit

This is a reasoning model, so give it a generous token budget.

Links

Runs of mlx-community Ornith-1.5-9B-OptiQ-4bit on huggingface.co

785
Total runs
0
24-hour runs
237
3-day runs
740
7-day runs
740
30-day runs

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Ornith-1.5-9B-OptiQ-4bit huggingface.co

Ornith-1.5-9B-OptiQ-4bit huggingface.co is an AI model on huggingface.co that provides Ornith-1.5-9B-OptiQ-4bit's model effect (), which can be used instantly with this mlx-community Ornith-1.5-9B-OptiQ-4bit model. huggingface.co supports a free trial of the Ornith-1.5-9B-OptiQ-4bit model, and also provides paid use of the Ornith-1.5-9B-OptiQ-4bit. Support call Ornith-1.5-9B-OptiQ-4bit model through api, including Node.js, Python, http.

Ornith-1.5-9B-OptiQ-4bit huggingface.co Url

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mlx-community Ornith-1.5-9B-OptiQ-4bit online free

Ornith-1.5-9B-OptiQ-4bit huggingface.co is an online trial and call api platform, which integrates Ornith-1.5-9B-OptiQ-4bit's modeling effects, including api services, and provides a free online trial of Ornith-1.5-9B-OptiQ-4bit, you can try Ornith-1.5-9B-OptiQ-4bit online for free by clicking the link below.

mlx-community Ornith-1.5-9B-OptiQ-4bit online free url in huggingface.co:

https://huggingface.co/mlx-community/Ornith-1.5-9B-OptiQ-4bit

Ornith-1.5-9B-OptiQ-4bit install

Ornith-1.5-9B-OptiQ-4bit is an open source model from GitHub that offers a free installation service, and any user can find Ornith-1.5-9B-OptiQ-4bit on GitHub to install. At the same time, huggingface.co provides the effect of Ornith-1.5-9B-OptiQ-4bit install, users can directly use Ornith-1.5-9B-OptiQ-4bit installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

Ornith-1.5-9B-OptiQ-4bit install url in huggingface.co:

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