Qwen3.8-27B — MLX int4 (complete VLM, Krill-native)

A mirror of the MLX 4-bit quantization of Qwen/Qwen3.8-27B, packaged for Krill's native Apple-silicon runtime.

Provenance, plainly. The original model and weights are by Qwen (Alibaba). The 4-bit MLX conversion is mlx-community/Qwen3.8-27B-4bit — this repo mirrors those weights byte-for-byte, it does not re-quantize them. It exists so Krill's qwen3.8-27b alias resolves to a pinned, tested blob. If you are not using Krill, prefer pulling from mlx-community directly.

Why this build

👁️ Complete vision-language model — the vision tower is included and kept at bf16 while the decoder is quantized, so image and video understanding work, not just text.
⚡ Native Krill runtime. Runs as native Swift + MLX on Apple Silicon. Qwen3.8 is a qwen3_5-class hybrid, and Krill ships a from-scratch native runtime for that architecture — the GatedDeltaNet (SSM) linear-attention layers, the gated full-attention layers, the vision tower and the 3D interleaved mRoPE are all Swift. No Python bridge, no mlx_vlm passthrough.
🧠 Thinking mode with tunable depth, including the reasoning_effort control (xhigh / medium / low).
📏 262,144-token context natively.

Fit before you pull

~16.1 GiB resident. That fits a 24 GB Apple-silicon box, but with little headroom — expect memory pressure under concurrent load, and prefer closing other large apps. On a 16 GB machine it will swap; don't.

Run in Krill (recommended)

# install Krill
brew tap srvsngh99/krill && brew install krill
# or:
curl -fsSL https://raw.githubusercontent.com/srvsngh99/Krill/main/install.sh | sh

# run Qwen3.8-27B (pulls this repo)
krill run qwen3.8-27b "Explain why a hybrid linear-attention decoder saves memory."

# with an image
krill run qwen3.8-27b --image diagram.png "What does this diagram show?"

# dial the reasoning depth down for short answers
KRILL_REASONING_EFFORT=low krill run qwen3.8-27b "One-line summary of RAII."

Run with mlx_vlm

pip install -U mlx-vlm
python -m mlx_vlm generate --model srv-sngh/Qwen3.8-27B-mlx-4bit \
  --prompt "Describe this image." --image path/to/image.jpg --max-tokens 200

About Qwen3.8-27B

A dense 27B vision-language model built on the Qwen3.5 architecture: 64 layers arranged as 16 × (3 × Gated DeltaNet → 1 × Gated Attention), hidden size 5120, FFN 17408, 24 query / 4 KV heads at head-dim 256, and a Qwen3-VL-style vision tower (depth 27, patch 16, spatial merge 2). Thinking mode is on by default; context is 262,144 tokens natively and extensible to ~1M with YaRN.

Selected results from the official model card (full model, not this quantization):

Benchmark	Qwen3.8-27B
SWE-bench Pro	61.7
QwenSWEBench	79.0
Terminal Bench 2.1 (Terminus)	73.0
CoWorkBench (long-horizon office work)	70.7
LiveCodeBench v6	90.3
GPQA Diamond	89.2
IFBench	79.5

Quantization

field	value
mode	affine
bits	4
group size	64
vision tower	preserved (bf16, not quantized)
MTP head	stripped
on disk	~16.1 GiB

Sampling

Per Qwen's recommendations:

Thinking mode: temperature=1.0, top_p=0.95, top_k=20
Instruct (non-thinking) mode: temperature=0.7, top_p=0.80, top_k=20, presence_penalty=1.5

License

Apache-2.0, inherited from the upstream model. Credit for the model belongs to Qwen; credit for the MLX conversion belongs to mlx-community.

About this build

Quantized and tested by Sourav Singh for Krill, a Mac-native LLM runtime and coding agent in one Swift + MLX binary. More Apple Silicon builds: huggingface.co/srv-sngh. Notes on building with local models and frontier AI: souravailabs.ai · GitHub · LinkedIn · YouTube

srv-sngh/Qwen3.8-27B-mlx-4bit