Instructions to use rapid-mlx/Qwen3.8-27B-4bit-MTP-fp16-MLX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use rapid-mlx/Qwen3.8-27B-4bit-MTP-fp16-MLX 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("rapid-mlx/Qwen3.8-27B-4bit-MTP-fp16-MLX") 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 rapid-mlx/Qwen3.8-27B-4bit-MTP-fp16-MLX with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "rapid-mlx/Qwen3.8-27B-4bit-MTP-fp16-MLX"
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": "rapid-mlx/Qwen3.8-27B-4bit-MTP-fp16-MLX" } ] } } }Run Pi
# Start Pi in your project directory: pi
- MLX LM
How to use rapid-mlx/Qwen3.8-27B-4bit-MTP-fp16-MLX with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "rapid-mlx/Qwen3.8-27B-4bit-MTP-fp16-MLX"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "rapid-mlx/Qwen3.8-27B-4bit-MTP-fp16-MLX" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "rapid-mlx/Qwen3.8-27B-4bit-MTP-fp16-MLX", "messages": [ {"role": "user", "content": "Hello"} ] }' - Hermes Agent
How to use rapid-mlx/Qwen3.8-27B-4bit-MTP-fp16-MLX 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 "rapid-mlx/Qwen3.8-27B-4bit-MTP-fp16-MLX"
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 rapid-mlx/Qwen3.8-27B-4bit-MTP-fp16-MLX
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use rapid-mlx/Qwen3.8-27B-4bit-MTP-fp16-MLX with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "rapid-mlx/Qwen3.8-27B-4bit-MTP-fp16-MLX"
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 "rapid-mlx/Qwen3.8-27B-4bit-MTP-fp16-MLX" \ --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 4-bit MLX + MTP — FP16 sibling
This is the M1/M2-oriented FP16 sibling of
rapid-mlx/Qwen3.8-27B-4bit-MTP-MLX.
It preserves the 4-bit target weights and colocated native Multi-Token
Prediction (MTP) sidecar, while storing every non-quantized BF16 tensor as
FP16. M1 and M2 chips do not accelerate BF16 matrix multiplication; M3 and
newer Macs should normally use the original BF16 checkpoint.
Rapid-MLX
rapid-mlx serve qwen3.8-27b-4bit-fp16 --no-think
Or use the repository ID directly:
rapid-mlx serve rapid-mlx/Qwen3.8-27B-4bit-MTP-fp16-MLX --no-think
--no-spec-decode remains available when a workload does not benefit from
MTP. Rapid does not silently substitute this checkpoint for the regular
qwen3.8-27b-4bit alias.
Conversion and validation
- Source revision:
aa985c29ff5b334cbfdcbbc787d47e66e9d9e456 - 1,705 BF16 tensors converted to FP16.
- 506 non-BF16 tensors preserved with identical names, shapes, dtypes, and values, including all packed U32 quantized weights.
- All four safetensors files were read back and verified; the MTP sidecar loaded all 31/31 expected tensors in Rapid-MLX.
- On an M3 Ultra, two of three fixed greedy prompts were byte-identical to the BF16 source. The third differed by one semantically equivalent phrase. This is a normal FP16/BF16 numerical boundary, so universal byte identity is not claimed.
- On the FP16 target, greedy output with MTP enabled was byte-identical to its autoregressive output in the paired smoke test.
The machine-readable fp16-conversion.json records the conversion contract
and counts. M1/M2 prefill measurements will be added after the paired hardware
qualification completes.
Contents
model-*.safetensors: Qwen3.8-27B MLX 4-bit target weights with FP16 non-quantized tensorsmtp/model.safetensors: matching 4-bit native MTP drafter with FP16 non-quantized tensors- tokenizer, processor, generation, and model configuration files
Upstream model licenses and limitations continue to apply.
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