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AI 模型 85003

• CodeV-CL-7B↗

  yang-z

• Youtu-LLM-2B↗

  腾讯

  **Youtu-LLM** is a new, small, yet powerful LLM, contains only 1.96B parameters, supports 128k long context, and has native agentic talents. On general evaluations, Youtu-LLM significantly outperforms SOTA LLMs of similar size in terms of Commonsense, STEM, Coding and Long Context capabilities; in agent-related testing, Youtu-LLM surpasses larger-sized leaders and is truly capable of completing multiple end2end agent tasks. **Youtu-LLM** has the following features: - Type: Autoregressive Causal Language Models with Dense MLA - Release versions: Base and Instruct - Number of Parameters: 1.96B - Number of Layers: 32 - Number of Attention Heads (MLA): 16 for Q/K/V - MLA Rank: 1,536 for Q, 512 for K/V - MLA Dim: 128 for QK Nope, 64 for QK Rope, and 128 for V - Context Length: 131,072 - Vocabulary Size: 128,256 > **Note:** The GGUF weights of Youtu-LLM-2B are available on the `GGUF` branch of this repository. Please specify the revision (`GGUF`) when downloading them:

• GLM-5.2-FP8↗

  zai-org

  We're introducing GLM-5.2, our latest flagship model for long-horizon tasks. It marks a substantial leap in long-horizon task capability over its predecessor GLM-5.1 and, for the first time, delivers that capability on a **solid 1M-token context**. GLM-5.2's new capabilities include: - **Solid 1M Context:** A solid 1M-token context that stably sustains long-horizon work - **Advanced Coding with Flexible Effort**: Stronger coding capabilities with multiple thinking effort levels to balance performance and latency - **Improved Architecture**: We propose IndexShare, which reuses the same indexer across every four sparse attention layers, reducing per-token FLOPs by 2.9× at a 1M context length. We also improve GLM-5.2’s MTP layer for speculative decoding, increasing the acceptance length by up to 20% - **Pure Open**: An MIT open-source license — no regional limits, technical access without borders GLM-5.2 supports deployment with the following frameworks. Feel free to try them out: - SGLang (v0.5.13.post1+) — see cookbook - vLLM (v0.23.0+) — see recipes - Transformers (v0.5.12+) — see transformers docs - KTransformers (v0.5.12+) — see tutorial - Unsloth (v0.1.47-beta+) — see guide - For deployment on the `Ascend NPU` platform, inference frameworks such as vLLM-Ascend, xLLM and SGLang are supported — see here.

• miqu-1-70b-sf↗

  152334H

• c71-h4↗

  aalv2

• persian-ezafe-albert↗

  abreza

• SabiYarn_MoE-280M↗

  Aletheia-ng

• static-retrieval-multilingual-69m-v1↗

  amgix

• Qwen2.5-0.5B-Instruct-Gensyn-Swarm-pale_wary_bear↗

  Antonwen

• qwen3-tts-0-6b-custom-voice-mix-precision↗

  Arm

  This repository packages a mixed-precision, mixed-framework deployment bundle derived from `Qwen/Qwen3-TTS-12Hz-0.6B-CustomVoice` for multilingual text-to-speech using preset speakers. The bundle uses GGUF, ONNX, and LiteRT artifacts and supports streaming synthesis at 24 kHz. Qwen3-TTS-12Hz-0.6B-CustomVoice is a multilingual text-to-speech model that synthesizes speech using a selected preset speaker. Follow these steps to run Qwen3-TTS 0.6B Custom Voice on macOS® with Apple Silicon or on Linux® with an Arm®-based CPU, such as a Raspberry Pi® 5.