Instructions to use malekoo/Qwen3.8-27B-MLX-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use malekoo/Qwen3.8-27B-MLX-8bit with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("malekoo/Qwen3.8-27B-MLX-8bit") prompt = "Write a story about Einstein" messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Pi
How to use malekoo/Qwen3.8-27B-MLX-8bit with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "malekoo/Qwen3.8-27B-MLX-8bit"
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "mlx-lm": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "malekoo/Qwen3.8-27B-MLX-8bit" } ] } } }Run Pi
# Start Pi in your project directory: pi
- OpenClaw new
How to use malekoo/Qwen3.8-27B-MLX-8bit with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "malekoo/Qwen3.8-27B-MLX-8bit"
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "malekoo/Qwen3.8-27B-MLX-8bit" \ --custom-provider-id mlx-lm \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
- MLX LM
How to use malekoo/Qwen3.8-27B-MLX-8bit with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "malekoo/Qwen3.8-27B-MLX-8bit"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "malekoo/Qwen3.8-27B-MLX-8bit" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "malekoo/Qwen3.8-27B-MLX-8bit", "messages": [ {"role": "user", "content": "Hello"} ] }' - Hermes Agent
How to use malekoo/Qwen3.8-27B-MLX-8bit with Hermes Agent:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "malekoo/Qwen3.8-27B-MLX-8bit"
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default malekoo/Qwen3.8-27B-MLX-8bit
Run Hermes
hermes
- Atomic Chat
Qwen3.8-27B — MLX, 8-bit (group size 64, affine)
This model was converted to MLX format (8-bit (group size 64, affine) quantization) from
Qwen/Qwen3.8-27B using
mlx-lm.
Refer to the original model card
for more details on the model.
Weights: ~29 GB (8.501 effective bits/weight) on disk (bf16 MLX artifact: ~54 GB; the original
checkpoint is ~55 GB including the vision encoder and MTP drafter, which MLX
conversions drop).
This conversion is text-only. mlx-lm's qwen3_5 implementation strips the
base model's vision encoder and multi-token-prediction drafter; this artifact
does not accept images or videos. For vision use, run the original checkpoint
with Transformers, vLLM, or SGLang.
Quantization changes the numerics. The base card's benchmark numbers do not transfer to this artifact as-is. Measured on this build: wikitext-2 (test) perplexity 6.9447, vs 6.9352 for the bf16 conversion in the identical harness (+0.14% relative, effectively lossless; 4-bit: 7.0871) — disjoint 2048-token windows, no overlap, single token stream. Decode ~18 tok/s on an M5 Max MacBook Pro at 28.9 GB peak memory (4-bit: ~33 tok/s at 15.5 GB; bf16: ~10 tok/s at 54 GB).
Model description (inherited from the base card)
Qwen3.8-27B is the compact dense model of the Qwen3.8 generation, built on the
Qwen3.5 architecture: 27B parameters, 64 layers in a 3:1 hybrid of Gated
DeltaNet linear attention and gated full attention (24Q/4KV, head dim 256),
vocab 248,320, context 262,144 native. Thinking mode on by default with
reasoning_effort control (xhigh default / medium / low) and
preserve_thinking. See the
base model card for details and
benchmarks.
Use with mlx-lm
pip install -U mlx-lm
mlx_lm.generate --model malekoo/Qwen3.8-27B-MLX-8bit --prompt "Explain KV caches briefly." --max-tokens 2048
OpenAI-compatible server: mlx_lm.server --model malekoo/Qwen3.8-27B-MLX-8bit --port 8080.
Thinking control via chat_template_kwargs: enable_thinking,
preserve_thinking, reasoning_effort (xhigh/medium/low).
Recommended sampling (from the base card)
- Thinking mode:
temperature=1.0,top_p=0.95,top_k=20,min_p=0.0,presence_penalty=0.0,repetition_penalty=1.0 - Instruct (non-thinking) mode:
temperature=0.7,top_p=0.80,top_k=20,min_p=0.0,presence_penalty=1.5,repetition_penalty=1.0
Conversion provenance
- Source:
Qwen/Qwen3.8-27B(bf16 safetensors, released 2026-08-14) - Quantized with
mlx_lm.convert -q --q-bits 8 --q-group-size 64 - Toolchain: mlx
0.32.1.dev20260814+3d23f7d87, mlx-lm at ml-explore/mlx-lm main254d153
License and attribution
Apache-2.0, inherited from the base model. Copyright the Qwen team; this repository is a quantized conversion and claims no additional rights.
Citation
@misc{qwen38,
title = {{Qwen3.8-Max}: A New Bar for Coding and Cowork},
url = {https://qwen.ai/blog?id=qwen3.8},
author = {{Qwen Team}},
month = {August},
year = {2026}
}
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