Thinking mode on by default. The model emits a <think>…</think> reasoning block before the final response. Disable per request with enable_thinking=False (instruct mode).
reasoning_effort — xhigh (default), medium, or low to tune reasoning depth and cost.
preserve_thinking — on by default; retains thinking blocks from all historical messages for agentic continuity and KV-cache efficiency.
Context: up to 1,000,000 tokens. The model's native context window is 1M tokens.
Recommended sampling — thinking mode: temperature=1.0, top_p=0.95, top_k=20; instruct mode: temperature=0.7, top_p=0.8, top_k=20, presence_penalty=1.5.
These are chat-template-level features: the original chat_template.jinja ships with this repo.

What this conversion is

Text-only. The original is a vision-language model (Qwen3_5ForConditionalGeneration); mlx-lm converts the language model only — the vision tower (model.visual.*) is stripped. Image/video inputs are NOT supported.
MTP preserved. The multi-token prediction module (mtp.*, ~0.42B params) is kept as mtp.safetensors (bf16, unquantized). mlx-lm's qwen3_5 runtime does not execute multi-token prediction yet, so the weights are inert under mlx-lm but remain available for runtimes that implement multi-token prediction.
Quantization: 4-bit affine group quantization, group size 64. Weights ~14.9 GiB (original bf16 ≈ 54 GiB).

Usage

bash

pip install mlx-lm

# chat (thinking mode is on by default via the shipped chat template)
python -m mlx_lm generate --model dogfoodai/Qwen3.8-27B-4bit \
    --prompt "Explain the Gated DeltaNet architecture in one paragraph."

python

from mlx_lm import load, generate

model, tokenizer = load("dogfoodai/Qwen3.8-27B-4bit")
messages = [{"role": "user", "content": "Write a haiku about quantization."}]
prompt = tokenizer.apply_chat_template(messages, add_generation_prompt=True)
print(generate(model, tokenizer, prompt=prompt, max_tokens=128))

# instruct mode: skip the <think> block
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True, enable_thinking=False
)

Conversion

Reproduced with the script in this repo (convert_to_mlx_4bit.py), which wraps mlx-lm's convert API:

bash

python3 convert_to_mlx_4bit.py \
    --hf-path Qwen/Qwen3.8-27B \
    --mlx-path mlx_model \
    --upload-repo dogfoodai/Qwen3.8-27B-4bit

Internally this runs the equivalent of:

mlx_lm.convert --hf-path Qwen/Qwen3.8-27B --mlx-path mlx_model -q --q-bits 4 --q-group-size 64

Caveats

Benchmarks on the original model card are for the unquantized bf16 model and were not re-run on this 4-bit build.
The vision tower (model.visual.*) is not part of this text-only conversion; for image/video understanding, use the original transformers or vLLM checkpoint.