Gouda-2.0
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View details ↗DISCOVER & COMPARE
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Ricotta-2.0
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View details ↗Ricotta-2.0-Instruct
GoudaAI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 ↗Qwen2.5-VL-7B-Instruct-GGUF
goushuai123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 ↗stsb-roberta-base-off-topic
govtech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 ↗ARACHNE-FOUNDATION-50B
gowdavidwan2003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 ↗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 ↗bge-m3-onnx-int8
gpahalThis model outputs dense, sparse and ColBERT embedding representations all at once. The output is a list of numpy arrays in previously mentioned order of representations. Note: dense and ColBERT embeddings are normalized like the default behavior in the original FlagEmbedding library, if you want unnormalized outputs you can modify the code in `export_onnx_int8.py` and re-run the script. This model also has "O2" level graph optimizations applied, you can read more about optimization levels here. If you want ONNX model with different optimization or without optimizations, you can re-run the ONNX export script `export_onnx_int8.py` with appropriate optimization argument.
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View details ↗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 ↗Copy-Lightx2v
gpt-horse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 ↗Copy-Wan_GGUF
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View details ↗Meta-Llama-3.1-8B-Instruct-128k-GGUF
GPT4All-Community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 ↗Phi-3.1-mini-128k-instruct-GGUF
GPT4All-Community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 ↗paraphrase-albert-small-v2
GPTCache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 ↗bce-reranker-base_v1-GGUF
gpustack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 ↗bge-m3-GGUF
gpustack**Model creator**: BAAI **Original model**: bge-m3 **GGUF quantization**: based on llama.cpp release 61408e7f For more details please refer to our github repo: https://github.com/FlagOpen/FlagEmbedding In this project, we introduce BGE-M3, which is distinguished for its versatility in Multi-Functionality, Multi-Linguality, and Multi-Granularity. - Multi-Functionality: It can simultaneously perform the three common retrieval functionalities of embedding model: dense retrieval, multi-vector retrieval, and sparse retrieval. - Multi-Linguality: It can support more than 100 working languages. - Multi-Granularity: It is able to process inputs of different granularities, spanning from short sentences to long documents of up to 8192 tokens.
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View details ↗bge-reranker-v2-m3-GGUF
gpustack**Model creator**: BAAI **Original model**: bge-reranker-v2-m3 **GGUF quantization**: based on llama.cpp release f4d2b **More details please refer to our Github: FlagEmbedding.** - Model List - Usage - Fine-tuning - Evaluation - Citation
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View details ↗FLUX.1-dev-GGUF
gpustack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 ↗FLUX.1-Fill-dev-GGUF
gpustack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 ↗FLUX.1-schnell-GGUF
gpustack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 ↗GLM-5.3-W4A8
gpustack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 ↗jina-reranker-v1-tiny-en-GGUF
gpustack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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