Athene-70B-IMat-GGUF
legraphista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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AutoCoder-IMat-GGUF
legraphista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 ↗AutoCoder_S_6.7B-IMat-GGUF
legraphista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-IMat-GGUF
legraphista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-IMat-GGUF
legraphista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 ↗c4ai-command-r-plus-08-2024-IMat-GGUF
legraphistaOriginal Model: CohereForAI/c4ai-command-r-plus-08-2024 Original dtype: `FP16` (`float16`) Quantized by: llama.cpp b3645 IMatrix dataset: here If the model file is big, it has been split into multiple files. In order to download them all to a local folder, run: According to this investigation, it appears that lower quantizations are the only ones that benefit from the imatrix input (as per hellaswag results).
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View details ↗codegeex4-all-9b-IMat-GGUF
legraphistaOriginal Model: THUDM/codegeex4-all-9b Original dtype: `BF16` (`bfloat16`) Quantized by: https://github.com/ggerganov/llama.cpp/pull/8031 IMatrix dataset: here If the model file is big, it has been split into multiple files. In order to download them all to a local folder, run: According to this investigation, it appears that lower quantizations are the only ones that benefit from the imatrix input (as per hellaswag results).
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View details ↗Codestral-22B-v0.1-IMat-GGUF
legraphista_Llama.cpp imatrix quantization of mistralai/Codestral-22B-v0.1 (legraphista/Codestral-22B-v0.1-hf-FIM-fix)_ Original model: mistralai/Codestral-22B-v0.1 Quantized HF Model: legraphista/Codestral-22B-v0.1-hf-FIM-fix Original dtype: `BF16` (`bfloat16`) Quantized by: llama.cpp b3046 IMatrix dataset: here If the model file is big, it has been split into multiple files. In order to download them all to a local folder, run:
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View details ↗datagemma-rag-27b-it-IMat-GGUF
legraphista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 ↗datagemma-rig-27b-it-IMat-GGUF
legraphistaOriginal Model: google/datagemma-rig-27b-it Original dtype: `BF16` (`bfloat16`) Quantized by: llama.cpp b3750 IMatrix dataset: here If the model file is big, it has been split into multiple files. In order to download them all to a local folder, run: According to this investigation, it appears that lower quantizations are the only ones that benefit from the imatrix input (as per hellaswag results).
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View details ↗DeepSeek-Coder-V2-Instruct-0724-IMat-GGUF
legraphista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 ↗DeepSeek-Coder-V2-Instruct-IMat-GGUF
legraphista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 ↗DeepSeek-Coder-V2-Lite-Base-IMat-GGUF
legraphista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 ↗DeepSeek-Coder-V2-Lite-Instruct-IMat-GGUF
legraphista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 ↗DeepSeek-V2.5-IMat-GGUF
legraphistaOriginal Model: deepseek-ai/DeepSeek-V2.5 Original dtype: `BF16` (`bfloat16`) Quantized by: llama.cpp b3675 IMatrix dataset: here If the model file is big, it has been split into multiple files. In order to download them all to a local folder, run: According to this investigation, it appears that lower quantizations are the only ones that benefit from the imatrix input (as per hellaswag results).
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View details ↗DeepSeek-V2-Lite-Chat-IMat-GGUF
legraphistaOriginal Model: deepseek-ai/DeepSeek-V2-Lite-Chat Original dtype: `BF16` (`bfloat16`) Quantized by: llama.cpp fork PR 7519 IMatrix dataset: here 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: According to this investigation, it appears that lower quantizations are the only ones that benefit from the imatrix input (as per hellaswag results).
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View details ↗DeepSeek-V2-Lite-IMat-GGUF
legraphistaOriginal Model: deepseek-ai/DeepSeek-V2-Lite Original dtype: `BF16` (`bfloat16`) Quantized by: llama.cpp https://github.com/ggerganov/llama.cpp/pull/7519 IMatrix dataset: here If the model file is big, it has been split into multiple files. In order to download them all to a local folder, run: According to this investigation, it appears that lower quantizations are the only ones that benefit from the imatrix input (as per hellaswag results).
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View details ↗dolphin-2.9.1-llama-3-70b-IMat-GGUF
legraphista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 ↗dolphin-2.9.1-llama-3-8b-IMat-GGUF
legraphistaOriginal Model: cognitivecomputations/dolphin-2.9.1-llama-3-8b Original dtype: `BF16` (`bfloat16`) Quantized by: llama.cpp b3006 IMatrix dataset: here If the model file is big, it has been split into multiple files. In order to download them all to a local folder, run: According to this investigation, it appears that lower quantizations are the only ones that benefit from the imatrix input (as per hellaswag results).
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View details ↗dolphin-2.9.1-mixtral-1x22b-IMat-GGUF
legraphista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 ↗dolphin-2.9.2-Phi-3-Medium-IMat-GGUF
legraphistaOriginal Model: cognitivecomputations/dolphin-2.9.2-Phi-3-Medium Original dtype: `BF16` (`bfloat16`) Quantized by: llama.cpp b3072 IMatrix dataset: here If the model file is big, it has been split into multiple files. In order to download them all to a local folder, run: According to this investigation, it appears that lower quantizations are the only ones that benefit from the imatrix input (as per hellaswag results).
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View details ↗gemma-2-27b-it-IMat-GGUF
legraphistaOriginal Model: google/gemma-2-27b-it Original dtype: `BF16` (`bfloat16`) Quantized by: llama.cpp b3790 IMatrix dataset: here If the model file is big, it has been split into multiple files. In order to download them all to a local folder, run: According to this investigation, it appears that lower quantizations are the only ones that benefit from the imatrix input (as per hellaswag results).
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View details ↗gemma-2-2b-IMat-GGUF
legraphista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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