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 17 days ago

Commit

1327e07

verified ·

1 Parent(s): aed68a8

Tighten MTPLX section, drop self-link, evergreen heading

Browse files

Files changed (1) hide show

README.md +3 -4

README.md CHANGED Viewed

@@ -18,9 +18,9 @@ pipeline_tag: text-generation
 # 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
@@ -29,10 +29,9 @@ 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

 # Qwen3.6-27B MTPLX Optimized
+## Run this with MTPLX
+**MTPLX** is 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.
 ```bash
 pip install mtplx
 **Project:** [github.com/youssofal/MTPLX](https://github.com/youssofal/MTPLX)
+**Other MTPLX checkpoints:**
 - [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)
 - [Qwen3.5-4B-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