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AI 模型 84760

• wav2vec2-large-mms-1b-tatar-v2↗

  AigizK

• ru-girlfriend-roleplay-1.5b-gguf-q8↗

  aiplatforms

• qwen1.5-110b-chat↗

  阿里巴巴 · Qwen

  Qwen1.5 是 Qwen2 的测试版，是一个基于 Transformer 的纯解码器语言模型，在海量数据上进行了预训练。与此前发布的 Qwen 相比，改进之处包括：Qwen1.5 是一个语言模型系列，包含不同规模的解码器语言模型。对于每个规模，我们都发布了基础语言模型和对齐聊天模型。它基于 Transformer 架构，采用 SwiGLU 激活函数、注意力 QKV 偏置、分组查询注意力、滑动窗口注意力与全注意力混合等技术。此外，我们还配备了适配多种自然语言和代码的改进型 tokenizer。对于测试版，暂时未包含 GQA（32B 和 110B 除外）以及 SWA 与全注意力的混合模式。我们在海量数据上完成了模型的预训练，并通过监督微调和直接偏好优化两种方式对模型进行了后训练。

• qwen3-235b-a22b↗

  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**. **Qwen3-235B-A22B** has the following features: - Type: Causal Language Models - Training Stage: Pretraining & Post-training - Number of Parameters: 235B in total and 22B activated - Number of Paramaters (Non-Embedding): 234B - Number of Layers: 94 - Number of Attention Heads (GQA): 64 for Q and 4 for KV - Number of Experts: 128 - Number of Activated Experts: 8 - Context Length: 32,768 natively and 131,072 tokens with YaRN.

• qwen3-30b-a3b-thinking-2507↗

  阿里巴巴 · Qwen

  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: - **Significantly improved performance** on reasoning tasks, including logical reasoning, mathematics, science, coding, and academic benchmarks that typically require human expertise. - **Markedly better general capabilities**, such as instruction following, tool usage, text generation, and alignment with human preferences. - **Enhanced 256K long-context understanding** capabilities. **NOTE**: This version has an increased thinking length. We strongly recommend its use in highly complex reasoning tasks.

• olmOCR-2-7B-1025-FP8↗

  allenai

  Quantized to FP8 Version of olmOCR-2-7B-1025, using llmcompressor. This is a release of the olmOCR model that's fine tuned from Qwen2.5-VL-7B-Instruct using the olmOCR-mix-1025 dataset. It has been additionally fine tuned using GRPO RL training to boost its performance at math equations, tables, and other tricky OCR cases. Quick links: - 📃 Paper - 🤗 SFT Dataset - 🤗 RL Dataset - 🛠️ Code - 🎮 Demo

• Kimi-K2.7-Code-DSpark-GGUF↗

  Anbeeld

• qwen_rp_0.5b↗

  andybanana

• hinglish-minilm↗

  anuragwagh0

• detr-resnet-50-finetuned-federated-600-epochs-Taichung_Port_dataset↗

  ARG-NCTU