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AI モデル 84759
- Cosmos-Reason2-32B-GGUF↗
Hob-forge
- granite-4.0-tiny-preview↗
ibm-granite
**Model Summary:** Granite-4-Tiny-Preview is a 7B parameter fine-grained hybrid mixture-of-experts (MoE) instruct model fine-tuned from Granite-4.0-Tiny-Base-Preview using a combination of open source instruction datasets with permissive license and internally collected synthetic datasets tailored for solving long context problems. This model is developed using a diverse set of techniques with a structured chat format, including supervised fine-tuning, and model alignment using reinforcement learning. - **Developers:** Granite Team, IBM - **Website**: Granite Docs - **Release Date**: May 2nd, 2025 - **License:** Apache 2.0 **Supported Languages:** English, German, Spanish, French, Japanese, Portuguese, Arabic, Czech, Italian, Korean, Dutch, and Chinese. However, users may fine-tune this Granite model for languages beyond these 12 languages.
- K2-Horizon-32B↗
IFM
K2-Horizon-32B is the large dense member of the K2-Horizon family: a 32B decoder-only model with a 512K context window. Open-weight dense models K2-Horizon-32B Qwen3.8-27B Muse Glimmer-30B IBM Granite 4.2 30B # Params 32B 27B 30B 30B # Activated params 32B 27B 30B 30B Architecture Dense Dense Dense Dense Agents tau3-Banking Agentic tool use 24.1 48.0 23.5 14.4 Coding Terminal-Bench 2.1 Agentic terminal use 62.6 79.8 51.7 26.6 SciCode Scientific coding 37.9 44.7 43.6 36.6 Scientific Reasoning Humanity's Last Exam (without tools) Expert-level reasoning 25.2 33.9 22.0 11.2 GPQA Diamond Graduate-level science QA 82.8 90.5 83.5 64.4 CritPt Frontier physics reasoning 2.9 5.4 2.6 0.3 General AA-LCR Long-context reasoning 68.0 77.3 80.0 46.7 AA-Omniscience Accuracy Factual accuracy 18.2 15.6 27.0 10.1 AA-Omniscience Non-Hallucination Non-hallucination rate 69.7 69.7 18.1 74.4 Scores in %. Bold marks the best score in each row. Sections follow the Artificial Analysis Intelligence Index categories. Baseline scores are from Artificial Analysis; Muse Glimmer-30B at high reasoning effort, other open models in their reasoning mode.
- ViT-SO400M-14-SigLIP2-378__webli↗
immich-app
- whisper-large-v3↗
ivrit-ai
Language detection capability of this model has been degraded during training - it is intended for mostly-hebrew audio transcription. Language token should be explicitly set to Hebrew. Additionally, the tanslation task was not trained and also degraded. This model would not be able to translate in any reasonable capacity. Please follow the original model card for usage details - replacing with this model name. You can also fine other weight formats ad quantizations on the ivrit ai HF page.
- wav2vec2-large-xlsr-53-finnish↗
jonatasgrosman
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Finnish using the train and validation splits of Common Voice 6.1 and CSS10. When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned thanks to the GPU credits generously given by the OVHcloud :) The script used for training can be found here: https://github.com/jonatasgrosman/wav2vec2-sprint
- wav2vec2-large-xls-r-1b-Swedish↗
kingabzpro
- jina-embeddings-v3↗
koble-ai
- CLIP-convnext_base_w-laion2B-s13B-b82K↗
laion
- pixel-art-lora↗
Limbicnation
A LoRA adapter trained on FLUX.2-klein-4B for generating pixel art character sprites. Optimized for game-ready assets with transparent backgrounds. 2. Load FLUX.2-klein-4B as the base checkpoint 3. Add a **Load LoRA** node, point to `pytorch_lora_weights.safetensors` 4. Set LoRA strength: **0.85 - 1.4** (tested range) 5. Use trigger words in your positive prompt This model was trained with full data provenance tracking. All source images are CC0 public domain, making the outputs safe for commercial game development, Steam releases, and B2B studio use.