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Llama-3.1_OpenScholar-8B-GGUF
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View details ↗If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run: You can either specify a new local-dir (Llama-3.1-SauerkrautLM-8b-Instruct-Q8_0) or download them all in place (./) The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have.
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View details ↗Llama-3.1-Storm-8B-GGUF
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View details ↗Llama-3.1-SuperNova-Lite-GGUF
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View details ↗Llama-3.1-Tulu-3-405B-GGUF
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View details ↗Llama-3.1-Tulu-3-70B-DPO-GGUF
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View details ↗Llama-3.1-Tulu-3-70B-GGUF
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View details ↗Llama-3.1-Tulu-3-70B-SFT-GGUF
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View details ↗Llama-3.1-Tulu-3-8B-DPO-GGUF
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View details ↗Llama-3.1-Tulu-3-8B-GGUF
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View details ↗Llama-3.1-Tulu-3-8B-SFT-GGUF
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View details ↗Llama-3.1-WhiteRabbitNeo-2-70B-GGUF
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View details ↗Llama-3.1-WhiteRabbitNeo-2-8B-GGUF
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View details ↗Llama-3-11.5B-Instruct-Coder-v2-GGUF
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View details ↗Llama-3.2-1B-Instruct-GGUF
bartowskiAll quants made using imatrix option with dataset from here Some of these quants (Q3_K_XL, Q4_K_L etc) are the standard quantization method with the embeddings and output weights quantized to Q8_0 instead of what they would normally default to. Some say that this improves the quality, others don't notice any difference. If you use these models PLEASE COMMENT with your findings. I would like feedback that these are actually used and useful so I don't keep uploading quants no one is using.
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View details ↗Llama-3.2-3B-Instruct-GGUF
bartowskiAll quants made using imatrix option with dataset from here Some of these quants (Q3_K_XL, Q4_K_L etc) are the standard quantization method with the embeddings and output weights quantized to Q8_0 instead of what they would normally default to. Some say that this improves the quality, others don't notice any difference. If you use these models PLEASE COMMENT with your findings. I would like feedback that these are actually used and useful so I don't keep uploading quants no one is using.
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View details ↗Original model: https://huggingface.co/chuanli11/Llama-3.2-3B-Instruct-uncensored All quants made using imatrix option with dataset from here Some of these quants (Q3_K_XL, Q4_K_L etc) are the standard quantization method with the embeddings and output weights quantized to Q8_0 instead of what they would normally default to.
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View details ↗llama-3.2-Korean-Bllossom-3B-GGUF
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View details ↗Llama-3.3-70B-Instruct-ablated-GGUF
bartowskiSome of these quants (Q3_K_XL, Q4_K_L etc) are the standard quantization method with the embeddings and output weights quantized to Q8_0 instead of what they would normally default to. If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run: You can either specify a new local-dir (Llama-3.3-70B-Instruct-ablated-Q8_0) or download them all in place (./)
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View details ↗Original model: https://huggingface.co/huihui-ai/Llama-3.3-70B-Instruct-abliterated All quants made using imatrix option with dataset from here Some of these quants (Q3_K_XL, Q4_K_L etc) are the standard quantization method with the embeddings and output weights quantized to Q8_0 instead of what they would normally default to.
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View details ↗Llama-3.3-70B-Instruct-GGUF
bartowskiAll quants made using imatrix option with dataset from here Some of these quants (Q3_K_XL, Q4_K_L etc) are the standard quantization method with the embeddings and output weights quantized to Q8_0 instead of what they would normally default to. If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run:
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Price not verifiedCheck multimodal and channel pricing separately
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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