mlx-community/Qwen3.8-27B-OBLITERATED-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 OBLITERATUS/Qwen3.8-27B-OBLITERATED, a Qwen3.8-family vision-language model with a bundled MTP speculation head. 21 GB on disk.

What it is

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

Run it

Qwen3.8 and the MTP/vision sidecars register through OptiQ, so import optiq once before loading:

pip install "mlx-optiq>=0.4.27"

import optiq  # registers the arch + MTP/vision sidecars
from mlx_lm import load, generate

model, tok = load("mlx-community/Qwen3.8-27B-OBLITERATED-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 image input plus an OpenAI- and Anthropic-compatible endpoint with mixed-precision KV cache:

optiq serve --model mlx-community/Qwen3.8-27B-OBLITERATED-OptiQ-4bit

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

Links

• Project website: mlx-optiq.com
• All OptiQ quants: mlx-optiq.com/models
• Base model: OBLITERATUS/Qwen3.8-27B-OBLITERATED