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View details ↗DISCOVER & COMPARE
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View details ↗vitalik.eth-13b
gaianet基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗xLAM-1b-fc-r-GGUF
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View details ↗xLAM-8x7b-r-GGUF
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View details ↗Yi-1.5-6B-Chat-GGUF
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View details ↗Yi-1.5-9B-Chat-16K-GGUF
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View details ↗Yi-Coder-1.5B-Chat-GGUF
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View details ↗Abel-7B-002
GAIRWe released **Abel-7B-002**, resulting in a stronger (35% improvement on GSM8K, 126% improvement on MATH) and more generalized model, achieving the best performance among all 7B models (80.44 on GSM8K, 29.46 on MATH)
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View details ↗gajosep
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View details ↗x-humor-unrestricted
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View details ↗cerbero-7b
galatolo> 🚀 **New Release**: **cerbero-7b-openchat** our latest SOTA model based on **openchat3.5**, delivering performance **on par with** or **superior** to **ChatGPT 3.5**! > 📢 **cerbero-7b** is the first **100% Free** and Open Source **Italian Large Language Model** (LLM) ready to be used for **research** or **commercial applications**. **Try an online demo here** (quantized demo running on CPU, a lot less powerful than the original cerbero-7b)
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View details ↗Qwen3.8-27B-GGUF
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View details ↗LeVJEPA-VideoMix-Large
galilai-groupA ViT-L/16 video encoder trained with LeV-JEPA, a self-supervised objective combining a multi-crop prediction loss with SIGReg. No labels are used at any point. `trust_remote_code=True` is required: this is a custom architecture (RoPE + block-causal attention) rather than a stock `transformers` model, so the modeling code ships with the weights. Normalise with the ImageNet statistics used in training — `mean=[0.485, 0.456, 0.406]`, `std=[0.229, 0.224, 0.225]` — and resize/crop to 224. Frames are sampled at roughly 7.5 fps in training, so a 16-frame clip covers about two seconds.
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View details ↗Llama-3-8B-Physics_Master-GGUF
gallen881基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗wolofToFrenchTranslator_nllb
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View details ↗GALX-Titan-27B-v2.0
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View details ↗farm8
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View details ↗RoBERTa-base-japanese-sentencepiece
ganchengguang基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗qwen38-fcf-gguf
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