Qwythos-9B-Claude-Mythos-5-1M-GGUF
Coding123defy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 ↗DISCOVER & COMPARE
Explore AI models 84991
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 ↗MiniMax-H3
Codydaoust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 ↗Qwen3.8-27B-Uncensored-GGUF
Codydaoust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 ↗Model_Compilation
Coercer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 ↗novaFurryXL_ilV170_GGUF
Coercer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 ↗waiIllustriousSDXL_v170_GGUF
Coercer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 ↗FLM-2-52B-Instruct-2407
CofeAI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 ↗Tele-FLM
CofeAITele-FLM (aka FLM-2) is a 52B open-sourced multilingual large language model that features a stable, efficient pre-training paradigm and enhanced factual judgement capabilities. Built upon the decoder-only transformer architecture, it has been trained on approximately 2T tokens. Tele-FLM demonstrates superior performances at its scale, and sometimes surpass larger models. In addition to sharing the model weights, we provide the core designs, engineering practices, and training details, anticipating their benefits for both academic and industrial communities. - **Developed by:** BAAI & TeleAI - **Language(s):** English; Chinese; Other languages - **License:** Apache 2.0 Although we've made extensive efforts to thoroughly clean and filter the training corpus for the model, due to the open nature of the dataset, the model may still have picked up on some unsafe examples. Consequently, the model may still generate unexpected content, including but not limited to discrimination, bias, or offensive language. We would like to strongly advise users not to spread any unsafe content generated by the model. The project developers cannot be held responsible for any repercussions stemming from the dissemination of harmful information.
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View details ↗CogACT-Base
CogACT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 ↗VHHBERT
COGNANO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 ↗canary-1b-v2-mlx-bf16
CogniSoftOrg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 ↗ColNetraEmbed
Cognitive-Lab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 ↗Uncensored
cognitivecomputationsVenice Uncensored Dolphin Mistral 24B Venice Edition is a fine-tuned variant of Mistral-Small-24B-Instruct-2501, developed by dphn.ai in collaboration with Venice.ai. This model is designed as an “uncensored” instruct-tuned LLM, preserving...
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$0.2 / M input tokensOpenRouter · cognitivecomputations/dolphin-mistral-24b-venice-edition
View details ↗GLM-4.7-Flash-abliterated-12GB-GGUF
cognitivers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 ↗Ornith-1.5-35B-A3B-Abliterated-12GB-GGUF
cognitivers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 ↗Qwen3.8-27B-OBLITERATED-12GB-GGUF
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View details ↗Mistral-7b-Inst-0.2-Bulleted-Notes_GGUF
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View details ↗This model is distilled from the zero-shot classification pipeline on the unlabeled GoEmotions dataset using this script. It was trained with mixed precision for 10 epochs and otherwise used the default script arguments. The model can be used like any other model trained on GoEmotions, but will likely not perform as well as a model trained with full supervision. It is primarily intended as a demo of how an expensive NLI-based zero-shot model can be distilled to a more efficient student, allowing a classifier to be trained with only unlabeled data. Note that although the GoEmotions dataset allow multiple labels per instance, the teacher used single-label classification to create psuedo-labels.
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View details ↗https://huggingface.co/jinaai/jina-embeddings-v2-base-en with ONNX weights to be compatible with Transformers.js.
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View details ↗
aya-101
Cohere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 ↗aya-23-35B
Cohere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 ↗aya-23-8B
Cohere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 ↗aya-expanse-32b
Cohere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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