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AI 모델 85003
- smollm2-360m-e2-pilot-lr1e-5↗
trantam31
- Qwen3.5-2B-Claude-4.6-Opus-Reasoning-Distilled-heretic-v2↗
tvall43
- Qwen-Image-ComfyUI↗
unsloth
- whisper-tamil-large-v2↗
vasista22
This model is a fine-tuned version of openai/whisper-large-v2 on the Tamil data available from multiple publicly available ASR corpuses. It has been fine-tuned as a part of the Whisper fine-tuning sprint. **NOTE:** The code used to train this model is available for re-use in the whisper-finetune repository. In order to evaluate this model on an entire dataset, the evaluation codes available in the whisper-finetune repository can be used.
- ShotVL-7B↗
Vchitect
This model is a fine-tuned version of Qwen/Qwen2.5-VL-7B-Instruct, introduced in the paper ShotBench: Expert-Level Cinematic Understanding in Vision-Language Models. It is trained on the largest and high-quality dataset for cinematic language understanding to date. It currently achieves state-of-the-art performance on ShotBench, a comprehensive benchmark for evaluating cinematography understanding in vision-language models. Abbreviations: SS = Shot Size , SF = Shot Framing , CA = Camera Angle , LS = Lens Size , LT = Lighting Type , LC = Lighting Conditions , SC = Shot Composition , CM = Camera Movement . Underline marks previous best in each group. Our ShotVL models establish new SOTA. Open-Sourced VLMs Qwen2.5-VL-3B-Instruct 54.6 56.6 43.1 36.6 59.3 45.1 41.5 31.9 46.1 Qwen2.5-VL-7B-Instruct 69.1 73.5 53.2 47.0 60.5 47.4 49.9 30.2 53.8 LLaVA-NeXT-Video-7B 35.9 37.1 32.5 27.8 50.9 31.7 28.0 31.3 34.4 LLaVA-Video-7B-Qwen2 56.9 65.4 45.1 36.0 63.5 45.4 37.4 35.3 48.1 LLaVA-Onevision-Qwen2-7B-Ov-Chat 58.4 71.0 52.3 38.7 59.5 44.9 50.9 39.7 51.9 InternVL2.5-8B 56.3 70.3 50.8 41.1 60.2 45.1 50.1 33.6 50.9 InternVL3-2B 56.3 56.0 44.4 34.6 56.8 44.6 43.0 38.1 46.7 InternVL3-8B 62.1 65.8 46.8 42.9 58.0 44.3 46.8 44.2 51.4 InternVL3-14B 59.6 82.2 55.4 40.7 61.7 44.6 51.1 38.2 54.2 Internlm-xcomposer2d5-7B 51.1 71.0 39.8 32.7 59.3 35.7 35.7 38.8 45.5 Ovis2-8B 35.9 37.1 32.5 27.8 50.9 31.7 28.0 35.3 34.9 VILA1.5-3B 33.4 44.9 32.1 28.6 50.6 35.7 28.4 21.5 34.4 VILA1.5-8B 40.6 44.5 39.1 29.7 48.9 32.9 34.4 36.9 38.4 VILA1.5-13B 36.7 54.6 40.7 34.8 52.8 35.4 34.2 31.3 40.1 Instructblip-vicuna-7B 27.0 27.9 34.5 29.4 44.4 29.7 27.1 25.0 30.6 Instructblip-vicuna-13B 26.8 29.2 27.9 28.0 39.0 24.0 27.1 22.0 28.0 InternVL2.5-38B 67.8 85.4 55.4 41.7 61.7 48.9 52.4 44.0 57.2 InternVL3-38B 68.0 84.0 51.9 43.6 64.4 46.9 54.7 44.6 57.3 Qwen2.5-VL-32B-Instruct 62.3 76.6 51.0 48.3 61.7 44.0 52.2 43.8 55.0 Qwen2.5-VL-72B-Instruct 75.1 82.9 56.7 46.8 59.0 49.4 54.1 48.9 59.1 InternVL3-78B 69.7 80.0 54.5 44.0 65.5 47.4 51.8 44.4 57.2 Proprietary VLMs Gemini-2.0-flash 48.9 75.5 44.6 31.9 62
- franken-SOLAR-18B-v1.0-GGUF↗
vicgalle
- Wan2.2-TI2V-5B-Diffusers↗
Wan-AI
💜 Wan    ｜    🖥️ GitHub    |   🤗 Hugging Face    |   🤖 ModelScope    |    📑 Technical Report    |    📑 Blog    |   💬 WeChat Group    |    📖 Discord    We are excited to introduce **Wan2.2**, a major upgrade to our foundational video models. With **Wan2.2**, we have focused on incorporating the following innovations: - 👍 **Effective MoE Architecture**: Wan2.2 introduces a Mixture-of-Experts (MoE) architecture into video diffusion models. By separating the denoising process cross timesteps with specialized powerful expert models, this enlarges the overall model capacity while maintaining the same computational cost.
- c66-h16↗
wandb01
- PartCrafter↗
wgsxm
- opus-mt-ko-en↗
Xenova