Instructions to use tokenfires/Qwen3.8-27B-MLX-6bit-MTPLX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use tokenfires/Qwen3.8-27B-MLX-6bit-MTPLX 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("tokenfires/Qwen3.8-27B-MLX-6bit-MTPLX") 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 tokenfires/Qwen3.8-27B-MLX-6bit-MTPLX with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "tokenfires/Qwen3.8-27B-MLX-6bit-MTPLX"
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "mlx-lm": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "tokenfires/Qwen3.8-27B-MLX-6bit-MTPLX" } ] } } }Run Pi
# Start Pi in your project directory: pi
- MLX LM
How to use tokenfires/Qwen3.8-27B-MLX-6bit-MTPLX with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "tokenfires/Qwen3.8-27B-MLX-6bit-MTPLX"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "tokenfires/Qwen3.8-27B-MLX-6bit-MTPLX" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "tokenfires/Qwen3.8-27B-MLX-6bit-MTPLX", "messages": [ {"role": "user", "content": "Hello"} ] }' - Hermes Agent
How to use tokenfires/Qwen3.8-27B-MLX-6bit-MTPLX 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 "tokenfires/Qwen3.8-27B-MLX-6bit-MTPLX"
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 tokenfires/Qwen3.8-27B-MLX-6bit-MTPLX
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use tokenfires/Qwen3.8-27B-MLX-6bit-MTPLX with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "tokenfires/Qwen3.8-27B-MLX-6bit-MTPLX"
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 "tokenfires/Qwen3.8-27B-MLX-6bit-MTPLX" \ --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"
Qwen3.8-27B — MLX 6-bit + MTPLX MTP adapter
A 6-bit MLX quantization of Qwen/Qwen3.8-27B with a trained multi-token-prediction (MTP) sidecar for MTPLX — native speculative decoding on Apple Silicon with no external draft model.
Built with MTPLX Forge v2.9.2, which converts, calibrates, and then verifies the speedup on real hardware before accepting the adapter. On the build machine (Apple Silicon M5 Max, macOS 26.6.2), Forge measured 2.87× faster decode than autoregressive baseline at MTP depth 3.
Contents
| File | Purpose |
|---|---|
model-0000*-of-00005.safetensors |
Language model trunk, 6-bit affine quantization (group size 64) |
mtp.safetensors |
MTP draft heads, kept BF16 (depth up to 3) |
model-vision.safetensors |
Vision tower (333 tensors), included from the base model |
mtplx_runtime.json |
Forge provenance, MTP contract, and verified speed evidence |
Total footprint: ~22 GB on disk, ~25.4 GB resident during inference.
Quantization recipe
- Trunk: 6-bit, affine mode, group size 64 (MLX)
- MTP sidecar: BF16 (unquantized — draft heads must track the trunk's distribution closely; quantizing them costs acceptance rate)
- Source:
Qwen/Qwen3.8-27B@1d4bf0f, BF16 native
Measured performance
Forge verification (greedy continuation, single prompt, M5 Max — recorded in mtplx_runtime.json):
| MTP depth | Acceptance by position | tok/s | vs. baseline |
|---|---|---|---|
| 0 (autoregressive) | — | 22.6 | 1.00× |
| 1 | 97% | 42.3 | 1.87× |
| 2 | 98% / 94% | 57.4 | 2.53× |
| 3 | 99% / 95% / 89% | 65.1 | 2.87× |
Verdict: mtp_depth_wins — depth 3 fastest on this hardware, quality checks passed at every depth, no configurations rejected.
Interactive chat (sampled at temperature 0.6, best of 3 runs, same machine):
| Metric | Value |
|---|---|
| Prefill | 60 tok/s |
| Decode | 50 tok/s |
| Draft acceptance | 75% |
| Memory | 25.4 GB |
| Cold-start first prompt | 36.9 tok/s |
Sampled decoding accepts fewer drafts than the greedy verification pass, so real-chat throughput sits below the verify numbers. Both are honest measurements of the same artifact.
Usage
Requires MTPLX ≥ 2.9 on Apple Silicon:
mtplx pull tokenfires/Qwen3.8-27B-MLX-6bit-MTPLX
mtplx run tokenfires/Qwen3.8-27B-MLX-6bit-MTPLX
Recommended sampler settings (shipped in mtplx_runtime.json): temperature 0.6, top-k 20, top-p 0.95. Recommended profile: sustained.
The trunk is standard MLX 6-bit and also loads in mlx-lm without the MTP speedup; the mtp.safetensors sidecar is only used by MTPLX.
Provenance
- Forged locally 2026-08-25 with MTPLX Forge v2.9.2 (
arch_id: qwen3-next-mtp) - Full recipe, MTP contract, acceptance data, and artifact fingerprint in
mtplx_runtime.json - Base model license: Apache-2.0 (included as
LICENSE)
This is a community quantization, not affiliated with Qwen or the MTPLX project.
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Qwen/Qwen3.8-27B
