Instructions to use macmacmacmac/Qwen3.8-Flash-Next-MLX-4bit-MTP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use macmacmacmac/Qwen3.8-Flash-Next-MLX-4bit-MTP with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="macmacmacmac/Qwen3.8-Flash-Next-MLX-4bit-MTP") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("macmacmacmac/Qwen3.8-Flash-Next-MLX-4bit-MTP") model = AutoModelForMultimodalLM.from_pretrained("macmacmacmac/Qwen3.8-Flash-Next-MLX-4bit-MTP", device_map="auto") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] inputs = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use macmacmacmac/Qwen3.8-Flash-Next-MLX-4bit-MTP with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "macmacmacmac/Qwen3.8-Flash-Next-MLX-4bit-MTP" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "macmacmacmac/Qwen3.8-Flash-Next-MLX-4bit-MTP", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/macmacmacmac/Qwen3.8-Flash-Next-MLX-4bit-MTP
- SGLang
How to use macmacmacmac/Qwen3.8-Flash-Next-MLX-4bit-MTP with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "macmacmacmac/Qwen3.8-Flash-Next-MLX-4bit-MTP" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "macmacmacmac/Qwen3.8-Flash-Next-MLX-4bit-MTP", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "macmacmacmac/Qwen3.8-Flash-Next-MLX-4bit-MTP" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "macmacmacmac/Qwen3.8-Flash-Next-MLX-4bit-MTP", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }' - Docker Model Runner
How to use macmacmacmac/Qwen3.8-Flash-Next-MLX-4bit-MTP with Docker Model Runner:
docker model run hf.co/macmacmacmac/Qwen3.8-Flash-Next-MLX-4bit-MTP
| { | |
| "architectures": [ | |
| "Qwen4ExpForConditionalGeneration" | |
| ], | |
| "image_token_id": 248056, | |
| "language_model_only": false, | |
| "mlx_lm_extra_tensors": { | |
| "mtp_file": "mtp.safetensors", | |
| "mtp_format": "torch-layout-fp16-v1", | |
| "mtp_reason": "MTP is retained outside the trunk because the referenced MLX qwen4_exp model has no MTP module; module-blind quantization would not be load-safe.", | |
| "vision_file": "vision.safetensors", | |
| "vision_format": "torch-layout-source-dtype-v1", | |
| "vision_reason": "The referenced MLX qwen4_exp model is text-only; the complete vision tower is retained by key, dtype, and shape in a sidecar." | |
| }, | |
| "model_type": "qwen4_exp", | |
| "quantization": { | |
| "bits": 4, | |
| "group_size": 64, | |
| "mode": "affine", | |
| "model.layers.1.ple.ple_embedding.ngram_embedding.shard_0": { | |
| "bits": 4, | |
| "group_size": 32, | |
| "mode": "affine" | |
| }, | |
| "model.layers.1.ple.ple_embedding.ngram_embedding.shard_1": { | |
| "bits": 4, | |
| "group_size": 32, | |
| "mode": "affine" | |
| }, | |
| "model.layers.1.ple.ple_embedding.ngram_embedding.shard_10": { | |
| "bits": 4, | |
| "group_size": 32, | |
| "mode": "affine" | |
| }, | |
| "model.layers.1.ple.ple_embedding.ngram_embedding.shard_100": { | |
| "bits": 4, | |
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| }, | |
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| "model.layers.1.ple.ple_embedding.ngram_embedding.shard_107": { | |
| "bits": 4, | |
| "group_size": 32, | |
| "mode": "affine" | |
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| "bits": 4, | |
| "group_size": 32, | |
| "mode": "affine" | |
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| "model.layers.1.ple.ple_embedding.ngram_embedding.shard_112": { | |
| "bits": 4, | |
| "group_size": 32, | |
| "mode": "affine" | |
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| "bits": 4, | |
| "group_size": 32, | |
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| "group_size": 32, | |
| "mode": "affine" | |
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| "model.layers.1.ple.ple_embedding.ngram_embedding.shard_117": { | |
| "bits": 4, | |
| "group_size": 32, | |
| "mode": "affine" | |
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| "bits": 4, | |
| "group_size": 32, | |
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| "bits": 4, | |
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| "model.layers.1.ple.ple_embedding.ngram_embedding.shard_120": { | |
| "bits": 4, | |
| "group_size": 32, | |
| "mode": "affine" | |
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| "bits": 4, | |
| "group_size": 32, | |
| "mode": "affine" | |
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| "bits": 4, | |
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| "bits": 4, | |
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| "bits": 4, | |
| "group_size": 32, | |
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| "bits": 4, | |
| "group_size": 32, | |
| "mode": "affine" | |
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| "bits": 4, | |
| "group_size": 32, | |
| "mode": "affine" | |
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| "bits": 4, | |
| "group_size": 32, | |
| "mode": "affine" | |
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| "bits": 4, | |
| "group_size": 32, | |
| "mode": "affine" | |
| }, | |
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| "bits": 4, | |
| "group_size": 32, | |
| "mode": "affine" | |
| }, | |
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| "bits": 4, | |
| "group_size": 32, | |
| "mode": "affine" | |
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| "bits": 4, | |
| "group_size": 32, | |
| "mode": "affine" | |
| }, | |
| "model.layers.1.ple.ple_embedding.ngram_embedding.shard_19": { | |
| "bits": 4, | |
| "group_size": 32, | |
| "mode": "affine" | |
| }, | |
| "model.layers.1.ple.ple_embedding.ngram_embedding.shard_2": { | |
| "bits": 4, | |
| "group_size": 32, | |
| "mode": "affine" | |
| }, | |
| "model.layers.1.ple.ple_embedding.ngram_embedding.shard_20": { | |
| "bits": 4, | |
| "group_size": 32, | |
| "mode": "affine" | |
| }, | |
| "model.layers.1.ple.ple_embedding.ngram_embedding.shard_21": { | |
| "bits": 4, | |
| "group_size": 32, | |
| "mode": "affine" | |
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| "model.layers.1.ple.ple_embedding.ngram_embedding.shard_22": { | |
| "bits": 4, | |
| "group_size": 32, | |
| "mode": "affine" | |
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| "bits": 4, | |
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| "bits": 4, | |
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| "bits": 4, | |
| "group_size": 32, | |
| "mode": "affine" | |
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| "bits": 4, | |
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| "mode": "affine" | |
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| "bits": 4, | |
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| "mode": "affine" | |
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| "bits": 4, | |
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| "mode": "affine" | |
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| "bits": 4, | |
| "group_size": 32, | |
| "mode": "affine" | |
| }, | |
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| "bits": 4, | |
| "group_size": 32, | |
| "mode": "affine" | |
| }, | |
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| "bits": 4, | |
| "group_size": 32, | |
| "mode": "affine" | |
| }, | |
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| "bits": 4, | |
| "group_size": 32, | |
| "mode": "affine" | |
| }, | |
| "model.layers.1.ple.ple_embedding.ngram_embedding.shard_63": { | |
| "bits": 4, | |
| "group_size": 32, | |
| "mode": "affine" | |
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