Instructions to use hanxin2000/Qwen3.8-27B-Quality with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use hanxin2000/Qwen3.8-27B-Quality 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("hanxin2000/Qwen3.8-27B-Quality") 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 hanxin2000/Qwen3.8-27B-Quality with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "hanxin2000/Qwen3.8-27B-Quality"
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": "hanxin2000/Qwen3.8-27B-Quality" } ] } } }Run Pi
# Start Pi in your project directory: pi
- OpenClaw new
How to use hanxin2000/Qwen3.8-27B-Quality with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "hanxin2000/Qwen3.8-27B-Quality"
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 "hanxin2000/Qwen3.8-27B-Quality" \ --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 hanxin2000/Qwen3.8-27B-Quality with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "hanxin2000/Qwen3.8-27B-Quality"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "hanxin2000/Qwen3.8-27B-Quality" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "hanxin2000/Qwen3.8-27B-Quality", "messages": [ {"role": "user", "content": "Hello"} ] }' - Hermes Agent
How to use hanxin2000/Qwen3.8-27B-Quality 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 "hanxin2000/Qwen3.8-27B-Quality"
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 hanxin2000/Qwen3.8-27B-Quality
Run Hermes
hermes
- Atomic Chat
Qwen3.8-27B-Quality
GDN-tiered MLX hybrid of Qwen/Qwen3.8-27B for oMLX. Built with mlx_lm.convert plus re-embedded BF16 MTP. Not an oQ export.
About 26 GB on disk. Intended as a quality-leaning local checkpoint: keep GDN gates and MTP in BF16, protect attention and residual down_proj, compress the bulky MLP gate/up path.
Quantization
| Precision | Tensors |
|---|---|
| BF16 | Entire MTP head; per-layer GDN A_log, dt_bias, conv1d, in_proj_a, in_proj_b, in_proj_z |
| 8-bit affine / gs64 | embed_tokens, lm_head, all self_attn, linear_attn.in_proj_qkv, linear_attn.out_proj, MLP on layers 0–7 and 56–63 |
| 6-bit affine / gs32 | Middle-layer mlp.down_proj |
| 4-bit affine / gs32 | Middle-layer mlp.gate_proj and mlp.up_proj |
Lightning MTP stays attached as language_model.mtp.* in model-mtp.safetensors. mlx_lm.convert strips mtp.*; those 15 tensors are copied back from the BF16 source.
Recommended sampling
Qwen3.8 official defaults:
temperature=1.0top_p=0.95top_k=20- thinking on (can be disabled per request)
oMLX: enable Lightning MTP, mtp_num_draft_tokens=3.
Use in oMLX
Place the folder under ~/.omlx/models/Qwen3.8-27B-Quality (or Import). Enable MTP in model settings. Do not run oQ on this checkpoint if you want the BF16 MTP/gates kept.
Use with mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("hanxin2000/Qwen3.8-27B-Quality")
print(generate(model, tokenizer, prompt="Hello", max_tokens=64))
MTP speculative decoding is an oMLX path. Stock mlx_lm.generate will run the quantized trunk; it may ignore the embedded MTP head.
Files
model-0000n-of-00005.safetensors— quantized language trunkmodel-mtp.safetensors— BF16 MTPmodel.safetensors.index.jsonconfig.json, tokenizer,chat_template.jinja
License
Apache 2.0, same as the Qwen3.8-27B source weights.
Acknowledgements
Base model: Qwen Team, Qwen3.8-27B. Runtime: oMLX / MLX.
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