amd/Qwen3-8B_rai_1.8.0_medusa_1.0_npu开源了新模型

AMD amd/Qwen3-8B_rai_1.8.0_medusa_1.0_npu amd/Qwen3-8B_rai_1.8.0_medusa_1.0_npu https://huggingface.co/amd/Qwen3-8B_rai_1.8.0_medusa_1.0_npu

amd/Qwen3-8B_rai_1.8.0_medusa_1.0_npu · 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) / Qwen3-8B_rai_1.8.0_medusa_1.0_npu (https://huggingface.co/amd/Qwen3-8B_rai_1.8.0_medusa_1.0_npu) Like 0 Follow AMD 3.35k Text Generation (https://huggingface.co/models?pipeline_tag=text-generation) ONNX (https://huggingface.co/models?library=onnx) English (https://huggingface.co/models?language=en) amd (https://huggingface.co/models?other=amd) ryzenai (https://huggingface.co/models?other=ryzenai) medusa_1.0_npu (https://huggingface.co/models?other=medusa_1.0_npu) conversational (https://huggingface.co/models?other=conversational) License: apache-2.0 Model card (https://huggingface.co/amd/Qwen3-8B_rai_1.8.0_medusa_1.0_npu) Files Files and versions xet (https://huggingface.co/amd/Qwen3-8B_rai_1.8.0_medusa_1.0_npu/tree/main) Community (https://huggingface.co/amd/Qwen3-8B_rai_1.8.0_medusa_1.0_npu/discussions) Copy to bucket new You need to agree to share your contact information to access this model This repository is publicly accessible, but you have to accept the conditions to access its files and content. Log in (https://huggingface.co/login?next=/amd/Qwen3-8B_rai_1.8.0_medusa_1.0_npu) or Sign Up (https://huggingface.co/join?next=/amd/Qwen3-8B_rai_1.8.0_medusa_1.0_npu) to review the conditions and access this model content. Qwen3-8B_rai_1.8.0_medusa_1.0_npu (https://huggingface.co/amd/Qwen3-8B_rai_1.8.0_medusa_1.0_npu#qwen3-8brai_180_medusa_10npu) (https://huggingface.co/amd/Qwen3-8B_rai_1.8.0_medusa_1.0_npu#qwen3-8b_rai_180_medusa_10_npu) Qwen3-8B_rai_1.8.0_medusa_1.0_npu Source model: Qwen3-8B (https://huggingface.co/Qwen/Qwen3-8B) (https://huggingface.co/amd/Qwen3-8B_rai_1.8.0_medusa_1.0_npu#introduction) Introduction This model was created using Quark Quantization, followed by OGA Model Builder, and finalized with post-processing for NPU deployment with Medusa 1.0 speculative decoding. (https://huggingface.co/amd/Qwen3-8B_rai_1.8.0_medusa_1.0_npu#quantization-strategy) Quantization Strategy AWQ / Group 128 / Asymmetric / BFP16 activations / UINT4 Weights (https://huggingface.co/amd/Qwen3-8B_rai_1.8.0_medusa_1.0_npu#quick-start) Quick Start For quickstart please refer "Medusa M1.0 LLM Guide" (https://huggingface.co/amd/Qwen3-8B_rai_1.8.0_medusa_1.0_npu#evaluation-scores) Evaluation scores Benchmark scores not yet available for this model. (https://huggingface.co/amd/Qwen3-8B_rai_1.8.0_medusa_1.0_npu#license) License Modifications copyright(c) 2025 Advanced Micro Devices, Inc. All rights reserved. AMD has modified the original model files for optimized deployme