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AI 模型 84757

• Qwen3-30B-A3B-Thinking-2507-GGUF↗

  unsloth

  Over the past three months, we have continued to scale the **thinking capability** of Qwen3-30B-A3B, improving both the **quality and depth** of reasoning. We are pleased to introduce **Qwen3-30B-A3B-Thinking-2507**, featuring the following key enhancements: **NOTE**: This version has an increased thinking length. We strongly recommend its use in highly complex reasoning tasks. **Qwen3-30B-A3B-Thinking-2507** has the following features: - Type: Causal Language Models - Training Stage: Pretraining & Post-training - Number of Parameters: 30.5B in total and 3.3B activated - Number of Paramaters (Non-Embedding): 29.9B - Number of Layers: 48 - Number of Attention Heads (GQA): 32 for Q and 4 for KV - Number of Experts: 128 - Number of Activated Experts: 8 - Context Length: **262,144 natively**.

• Z-Image-Turbo-GGUF↗

  vantagewithai

  Z-Image is a powerful and highly efficient image generation model with **6B** parameters. Currently there are three variants: 📸 **Photorealistic Quality**: **Z-Image-Turbo** delivers strong photorealistic image generation while maintaining excellent aesthetic quality. 📖 **Accurate Bilingual Text Rendering**: **Z-Image-Turbo** excels at accurately rendering complex Chinese and English text.

• Qwen3.8-27B-test-DFlash2-v0.3-GGUF↗

  vvsotnikov

• whisper-tiny↗

  Xenova

  https://huggingface.co/openai/whisper-tiny with ONNX weights to be compatible with Transformers.js. If you haven't already, you can install the Transformers.js JavaScript library from NPM using: Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using 🤗 Optimum and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).

• accent-classifier↗

  ylacombe

• EAGLE-LLaMA3-Instruct-8B↗

  yuhuili

  EAGLE (Extrapolation Algorithm for Greater Language-model Efficiency) is a new baseline for fast decoding of Large Language Models (LLMs) with provable performance maintenance. This approach involves extrapolating the second-top-layer contextual feature vectors of LLMs, enabling a significant boost in generation efficiency. EAGLE-2 uses the confidence scores from the draft model to approximate acceptance rates, dynamically adjusting the draft tree structure, which further enhances performance. EAGLE-3 removes the feature prediction constraint in EAGLE and simulates this process during training using training-time testing. Considering that top-layer features are limited to next-token prediction, EAGLE-3 replaces them with a fusion of low-, mid-, and high-level semantic features. EAGLE-3 further improves generation speed while ensuring lossless performance.

• d_m12↗

  yuki0011

• bolt-instruct-v2↗

  aisquared

• BAR-2x7B-Math↗

  allenai

• Lumina-Image-2.0↗

  Alpha-VLLM