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AI 模型 85004
- Flint-40B-A2.5B-JANGH2↗
OsaurusAI
- PP-LCNet_x1_0_doc_ori↗
PaddlePaddle
The Document Image Orientation Classification Module is primarily designed to distinguish the orientation of document images and correct them through post-processing. During processes such as document scanning or ID photo capturing, the device might be rotated to achieve clearer images, resulting in images with various orientations. Standard OCR pipelines may not handle these images effectively. By leveraging image classification techniques, the orientation of documents or IDs containing text regions can be pre-determined and adjusted, thereby improving the accuracy of OCR processing. The key accuracy metrics are as follow: PP-LCNet_x1_0_doc_ori 99.06 7 A document image classification model based on PP-LCNet_x1_0, with four categories: 0°, 90°, 180°, and 270°. For details about PaddlePaddle installation, please refer to the PaddlePaddle official website.
- 5HTiCtJq59gTnQfPAWDtouQiEdazV3nLD2sDVBe1Svu61m5y_vgg↗
PassionFriend
- rtdetr_r101vd↗
PekingU
- gemma-4-12B-it-qat-GGUF↗
prithivMLmods
- Q-ReAlign-Pro-9B↗
q-future
- DeepSeek-R1-Distill-Qwen-14B-GGUF↗
QuantFactory
- pair-preference-model-LLaMA3-8B-GGUF↗
QuantFactory
- Qwen3-8B-FP8↗
阿里巴巴 · Qwen
Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models. Built upon extensive training, Qwen3 delivers groundbreaking advancements in reasoning, instruction-following, agent capabilities, and multilingual support, with the following key features: - **Uniquely support of seamless switching between thinking mode** (for complex logical reasoning, math, and coding) and **non-thinking mode** (for efficient, general-purpose dialogue) **within single model**, ensuring optimal performance across various scenarios. - **Significantly enhancement in its reasoning capabilities**, surpassing previous QwQ (in thinking mode) and Qwen2.5 instruct models (in non-thinking mode) on mathematics, code generation, and commonsense logical reasoning. - **Superior human preference alignment**, excelling in creative writing, role-playing, multi-turn dialogues, and instruction following, to deliver a more natural, engaging, and immersive conversational experience. - **Expertise in agent capabilities**, enabling precise integration with external tools in both thinking and unthinking modes and achieving leading performance among open-source models in complex agent-based tasks. - **Support of 100+ languages and dialects** with strong capabilities for **multilingual instruction following** and **translation**. This repo contains the FP8 version of **Qwen3-8B**, which has the following features: - Type: Causal Language Models - Training Stage: Pretraining & Post-training - Number of Parameters: 8.2B - Number of Paramaters (Non-Embedding): 6.95B - Number of Layers: 36 - Number of Attention Heads (GQA): 32 for Q and 8 for KV - Context Length: 32,768 natively and 131,072 tokens with YaRN.
- benhaotang_-_nemo-math-science-philosophy-12B-gguf↗
RichardErkhov