amd/Qwen3.8-Flash-Next-Quark-MXFP4-PLEFP8开源了新模型

AMD amd/Qwen3.8-Flash-Next-Quark-MXFP4-PLEFP8 amd/Qwen3.8-Flash-Next-Quark-MXFP4-PLEFP8 https://huggingface.co/amd/Qwen3.8-Flash-Next-Quark-MXFP4-PLEFP8

amd/Qwen3.8-Flash-Next-Quark-MXFP4-PLEFP8 · Hugging Face Hugging Face (https://huggingface.co/) Models (https://huggingface.co/models) Datasets (https://huggingface.co/datasets) Spaces (https://huggingface.co/spaces) Buckets new (https://huggingface.co/storage) Docs (https://huggingface.co/docs) Enterprise (https://huggingface.co/enterprise) Pricing (https://huggingface.co/pricing) Website Tasks (https://huggingface.co/tasks) HuggingChat (https://huggingface.co/chat) Collections (https://huggingface.co/collections) Languages (https://huggingface.co/languages) Organizations (https://huggingface.co/organizations) Community Blog (https://huggingface.co/blog) Posts (https://huggingface.co/posts) Daily Papers (https://huggingface.co/papers) Hardware (https://huggingface.co/hardware) Learn (https://huggingface.co/learn) Discord (https://huggingface.co/join/discord) Forum (https://discuss.huggingface.co/) GitHub (https://github.com/huggingface) Solutions Team & Enterprise (https://huggingface.co/enterprise) Hugging Face PRO (https://huggingface.co/pro) Enterprise Support (https://huggingface.co/support) Inference Providers (https://huggingface.co/inference/models) Inference Endpoints (https://huggingface.co/inference-endpoints) Storage Buckets (https://huggingface.co/storage) Log In (https://huggingface.co/login) Sign Up (https://huggingface.co/join) (https://huggingface.co/amd) amd (https://huggingface.co/amd) / Qwen3.8-Flash-Next-Quark-MXFP4-PLEFP8 (https://huggingface.co/amd/Qwen3.8-Flash-Next-Quark-MXFP4-PLEFP8) Like 0 Follow AMD 3.3k Image-Text-to-Text (https://huggingface.co/models?pipeline_tag=image-text-to-text) Transformers (https://huggingface.co/models?library=transformers) Safetensors (https://huggingface.co/models?library=safetensors) English (https://huggingface.co/models?language=en) qwen4_exp (https://huggingface.co/models?other=qwen4_exp) conversational (https://huggingface.co/models?other=conversational) 8-bit precision (https://huggingface.co/models?other=8-bit) quark (https://huggingface.co/models?other=quark) License: other Model card (https://huggingface.co/amd/Qwen3.8-Flash-Next-Quark-MXFP4-PLEFP8) Files Files and versions xet (https://huggingface.co/amd/Qwen3.8-Flash-Next-Quark-MXFP4-PLEFP8/tree/main) Community (https://huggingface.co/amd/Qwen3.8-Flash-Next-Quark-MXFP4-PLEFP8/discussions) Deploy Copy to bucket new Use this model Instructions to use amd/Qwen3.8-Flash-Next-Quark-MXFP4-PLEFP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started. Libraries Transformers (https://huggingface.co/amd/Qwen3.8-Flash-Next-Quark-MXFP4-PLEFP8?library=transformers) How to use amd/Qwen3.8-Flash-Next-Quark-MXFP4-PLEFP8 with Transformers: # Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="amd/Qwen3.8-Flash-Next-Quark-MXFP4-PLEFP8") 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("amd/Qwen3.8-Flash-Next-Quark-MXFP4-PLEFP8") model = AutoModelForMultimodalLM.from_pretrained("amd/Qwen3.8-Flash-Next-Quark-MXFP4-PLEFP8", 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"].s