언어부터 이미지, 오픈 웨이트부터 API까지. 기능과 실제 비용을 비교하세요.
다음 아이디어에 맞는 AI를 찾아보세요.
All quants made using imatrix option with dataset from here - llama.cpp - ramalama - LM Studio - koboldcpp - Jan AI - Text Generation Web UI - LoLLMs Note: if it's a newly supported model, you may need to wait for an update from the developers.
번역 대기 중 · 원문 표시
All quants made using imatrix option with dataset from here - llama.cpp - ramalama - LM Studio - koboldcpp - Jan AI - Text Generation Web UI - LoLLMs Note: if it's a newly supported model, you may need to wait for an update from the developers.
번역 대기 중 · 원문 표시
All quants made using imatrix option with dataset from here - llama.cpp - ramalama - LM Studio - koboldcpp - Jan AI - Text Generation Web UI - LoLLMs Note: if it's a newly supported model, you may need to wait for an update from the developers.
번역 대기 중 · 원문 표시
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: You can either specify a new local-dir (google_medgemma-27b-it-Q8_0) or download them all in place (./)
번역 대기 중 · 원문 표시
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: You can either specify a new local-dir (google_medgemma-4b-it-Q8_0) or download them all in place (./)
번역 대기 중 · 원문 표시
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。
기본 정보 등록 · 상세 정보 보완 예정
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。
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。
기본 정보 등록 · 상세 정보 보완 예정
If you're using an ARM chip, the Q4_0_X_X quants will have a substantial speedup. Check out Q4_0_4_4 speed comparisons on the original pull request To check which one would work best for your ARM chip, you can check AArch64 SoC features(thanks EloyOn!). 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.
번역 대기 중 · 원문 표시
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。
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。
기본 정보 등록 · 상세 정보 보완 예정
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。
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。
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。
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: You can either specify a new local-dir (granite-3.1-1b-a400m-instruct-Q8_0) or download them all in place (./)
번역 대기 중 · 원문 표시
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: You can either specify a new local-dir (granite-3.1-2b-instruct-Q8_0) or download them all in place (./)
번역 대기 중 · 원문 표시
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。
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: You can either specify a new local-dir (granite-3.1-8b-instruct-Q8_0) or download them all in place (./)
번역 대기 중 · 원문 표시
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。
기본 정보 등록 · 상세 정보 보완 예정
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。
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。
**Model details:** - Parameter count: 9B - Input support: text - Speculative decoding: no - imatrix: yes - details **Don't know which to choose?** Grab Q4_K_M (5.54GB) - usually a good mix of size and performance. Download instructions available here These quants were made with llama.cpp release b10603 - if this model's architecture is newly supported, you'll need that release or newer to run them.
번역 대기 중 · 원문 표시
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: You can either specify a new local-dir (granite-embedding-107m-multilingual-Q8_0) or download them all in place (./)
번역 대기 중 · 원문 표시
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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