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AI 모델 84760

• Ling-2.6-1T↗

  inclusionAI

• Ling-Coder-lite↗

  inclusionAI

• Qwen3-30B-A3B-Instruct-2507-MXFP8-CT-AutoRound↗

  INCModel

• AdvancedMathBench-AutoVerifier↗

  internlm

• llama-68m↗

  JackFram

  This is a LLaMA-like model with only 68M parameters trained on Wikipedia and part of the C4-en and C4-realnewslike datasets. The model is mainly developed as a base Small Speculative Model in the SpecInfer paper. > **Notes on Failed Tasks:** The `Arithmetic` and `SocialIQA` benchmarks failed during execution due to runtime pipeline incompatibilities, yielding no score. Total evaluation runtime was **44.74 minutes**.

• Qwen3-ASR-0.6B-ONNX↗

  jiangzhuo9357

• jina-embeddings-v3-hf↗

  jinaai

• kanana-2-30b-a3b-instruct-2601↗

  kakaocorp

• LTX-2↗

  Lightricks

  This model card focuses on the LTX-2 model, as presented in the paper LTX-2: Efficient Joint Audio-Visual Foundation Model. The codebase is available here. LTX-2 is a DiT-based audio-video foundation model designed to generate synchronized video and audio within a single model. It brings together the core building blocks of modern video generation, with open weights and a focus on practical, local execution. - **Developed by:** Lightricks - **Model type:** Diffusion-based audio-video foundation model - **Language(s):** English

• Qwen-Image-2.1-Lightning↗

  lightx2v

  A **4-step distilled LoRA for Qwen-Image-2.1**, supporting text-to-image generation and image editing. Training was performed entirely on **16 Huawei Ascend 910B NPUs**, using DMD in LightX2V. For inference on heterogeneous hardware platforms, refer to the LightX2V platform scripts. Both examples use the training noise schedule: four steps with dynamic exponential shifting and no terminal stretching. Training and LightX2V inference keep latents in FP32; these BF16 Diffusers examples can produce different numerical results.