LiquidAI/LFM2.5-2.6B-ONNX开源了新模型
根据官方来源,LiquidAI/LFM2.5-2.6B-ONNX开源了新模型。详细信息请以原始来源为准。
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Liquid AI LiquidAI/LFM2.5-2.6B-ONNX LiquidAI/LFM2.5-2.6B-ONNX https://huggingface.co/LiquidAI/LFM2.5-2.6B-ONNX
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LiquidAI/LFM2.5-2.6B-ONNX · 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/LiquidAI) LiquidAI (https://huggingface.co/LiquidAI) / LFM2.5-2.6B-ONNX (https://huggingface.co/LiquidAI/LFM2.5-2.6B-ONNX) like 18 Follow Liquid AI 4.72k Text Generation (https://huggingface.co/models?pipeline_tag=text-generation) ONNX (https://huggingface.co/models?library=onnx) 16 languages lfm2 (https://huggingface.co/models?other=lfm2) liquid (https://huggingface.co/models?other=liquid) edge (https://huggingface.co/models?other=edge) lfm2.5 (https://huggingface.co/models?other=lfm2.5) onnxruntime (https://huggingface.co/models?other=onnxruntime) webgpu (https://huggingface.co/models?other=webgpu) conversational (https://huggingface.co/models?other=conversational) License: lfm1.0 Model card (https://huggingface.co/LiquidAI/LFM2.5-2.6B-ONNX) Files Files and versions xet (https://huggingface.co/LiquidAI/LFM2.5-2.6B-ONNX/tree/main) Community 1 (https://huggingface.co/LiquidAI/LFM2.5-2.6B-ONNX/discussions) Copy to bucket new LFM2.5-2.6B-ONNX (https://huggingface.co/LiquidAI/LFM2.5-2.6B-ONNX#lfm25-26b-onnx) Recommended Variants (https://huggingface.co/LiquidAI/LFM2.5-2.6B-ONNX#recommended-variants) Model Files (https://huggingface.co/LiquidAI/LFM2.5-2.6B-ONNX#model-files) Python (onnxruntime) (https://huggingface.co/LiquidAI/LFM2.5-2.6B-ONNX#python-onnxruntime) WebGPU (Transformers.js) (https://huggingface.co/LiquidAI/LFM2.5-2.6B-ONNX#webgpu-transformersjs) Try LFM (https://playground.liquid.ai/) • Docs (https://docs.liquid.ai/lfm/getting-started/welcome) • LEAP (https://leap.liquid.ai/) • Discord (https://discord.com/invite/liquid-ai) (https://huggingface.co/LiquidAI/LFM2.5-2.6B-ONNX#lfm25-26b-onnx) LFM2.5-2.6B-ONNX LFM2.5 is a new family of hybrid models designed for on-device deployment. It builds on the LFM2 architecture with extended pre-training and reinforcement learning. Find more details in the original model card: https://huggingface.co/LiquidAI/LFM2.5-2.6B (https://huggingface.co/LiquidAI/LFM2.5-2.6B) (https://huggingface.co/LiquidAI/LFM2.5-2.6B-ONNX#recommended-variants) Recommended Variants Precision Size Platform Use Case Q4 ~1.9 GB WebGPU, Server Recommended for most uses (quantized embedding) Q4F16 ~1.5 GB WebGPU Quantized embedding and q4 weights with FP16 runtime and caches FP16 ~2.1 GB WebGPU, Server Higher quality Q8 ~2.1 GB Server only Balance of quality and size WebGPU: Use Q4, Q4F16, or FP16 (Q8 is not supported on WebGPU). Server (CPU/GPU): All variants supported. Q4 and Q4F16 use a quantized input embedding. Q4F16 uses FP16 runtime tensors and caches while quantizing the LM head and decoder line
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