Instructions to use Youssofal/Qwen3.6-27B-MTPLX-Optimized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Youssofal/Qwen3.6-27B-MTPLX-Optimized 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("Youssofal/Qwen3.6-27B-MTPLX-Optimized")

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
LM Studio

Pi new

How to use Youssofal/Qwen3.6-27B-MTPLX-Optimized with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "Youssofal/Qwen3.6-27B-MTPLX-Optimized"

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": "Youssofal/Qwen3.6-27B-MTPLX-Optimized"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Youssofal/Qwen3.6-27B-MTPLX-Optimized 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 "Youssofal/Qwen3.6-27B-MTPLX-Optimized"

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 Youssofal/Qwen3.6-27B-MTPLX-Optimized

Run Hermes

hermes

MLX LM

How to use Youssofal/Qwen3.6-27B-MTPLX-Optimized with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "Youssofal/Qwen3.6-27B-MTPLX-Optimized"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "Youssofal/Qwen3.6-27B-MTPLX-Optimized"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "Youssofal/Qwen3.6-27B-MTPLX-Optimized",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Youssofal commited on 19 days ago

Commit

ac8e4d3

verified ·

1 Parent(s): fe68f16

Update MTPLX section: runtime is now released

Browse files

Replace 'MTPLX — coming soon' placeholder with concise project explainer, install command, GitHub link, and the four MTPLX model cross-links.

Files changed (1) hide show

README.md +19 -3

README.md CHANGED Viewed

@@ -18,9 +18,25 @@ pipeline_tag: text-generation
 # Qwen3.6-27B MTPLX Optimized
-> ## MTPLX — coming soon
->
-> This checkpoint is the verified default for the upcoming **MTPLX** inference engine — an MLX-native runtime for **native Multi-Token-Prediction speculative decoding on Apple Silicon**. **The runtime is not publicly released yet.** This model card is published in advance so you can review the architecture, MTP head, and quantization decisions while the runtime is finalized. Watch [github.com/youssofal/mtplx](https://github.com/youssofal/mtplx) for the release.
 This artifact pairs the Qwen3.6-27B trunk — MLX-quantized with MTPLX's `gdn8-speed4` policy (8-bit Gated Delta Network linears, 4-bit MLP, BF16 norms) — with a **calibrated INT4 Multi-Token-Prediction sidecar** grafted onto the trunk. The MTP head is what enables *native* speculative decoding: the model drafts its own tokens, with no external draft model required.

 # Qwen3.6-27B MTPLX Optimized
+## MTPLX is released
+This checkpoint runs on **MTPLX** — an MLX-native runtime for native Multi-Token-Prediction speculative decoding on Apple Silicon. Up to **2.24× faster decode** at real coding temperatures (`temp=0.6 / top_p=0.95 / top_k=20`), using the model's own built-in MTP heads. No external drafter, no greedy hack, no distribution drift.
+```bash
+pip install mtplx
+mtplx start
+```
+**Project:** [github.com/youssofal/MTPLX](https://github.com/youssofal/MTPLX)
+**MTPLX model fleet on Hugging Face:**
+- [Qwen3.6-27B-MTPLX-Optimized-Speed](https://huggingface.co/Youssofal/Qwen3.6-27B-MTPLX-Optimized-Speed) — 4-bit flagship speed (63 TPS on M5 Max)
+- [Qwen3.6-27B-MTPLX-Optimized](https://huggingface.co/Youssofal/Qwen3.6-27B-MTPLX-Optimized) — verified default (GDN8-Speed4 trunk + CyanKiwi INT4 MTP)
+- [Qwen3.5-4B-MTPLX-Optimized-Speed](https://huggingface.co/Youssofal/Qwen3.5-4B-MTPLX-Optimized-Speed) — small 4-bit speed-test
+- [Qwen3.5-4B-Optimized-MTPLX](https://huggingface.co/Youssofal/Qwen3.5-4B-Optimized-MTPLX) — small 8-bit
+---
 This artifact pairs the Qwen3.6-27B trunk — MLX-quantized with MTPLX's `gdn8-speed4` policy (8-bit Gated Delta Network linears, 4-bit MLP, BF16 norms) — with a **calibrated INT4 Multi-Token-Prediction sidecar** grafted onto the trunk. The MTP head is what enables *native* speculative decoding: the model drafts its own tokens, with no external draft model required.