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--- |
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base_model: Edentns/DataVortexS-10.7B-v1.0 |
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language: |
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- ko |
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library_name: transformers |
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license: cc-by-nc-sa-4.0 |
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quantized_by: mradermacher |
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tags: |
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- text-generation |
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--- |
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## About |
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<!-- ### quantize_version: 2 --> |
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<!-- ### output_tensor_quantised: 1 --> |
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<!-- ### convert_type: hf --> |
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<!-- ### vocab_type: --> |
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<!-- ### tags: nicoboss --> |
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static quants of https://huggingface.co/Edentns/DataVortexS-10.7B-v1.0 |
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<!-- provided-files --> |
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weighted/imatrix quants are available at https://huggingface.co/mradermacher/DataVortexS-10.7B-v1.0-i1-GGUF |
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## Usage |
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If you are unsure how to use GGUF files, refer to one of [TheBloke's |
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READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for |
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more details, including on how to concatenate multi-part files. |
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## Provided Quants |
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(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) |
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| Link | Type | Size/GB | Notes | |
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|:-----|:-----|--------:|:------| |
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| [GGUF](https://huggingface.co/mradermacher/DataVortexS-10.7B-v1.0-GGUF/resolve/main/DataVortexS-10.7B-v1.0.Q2_K.gguf) | Q2_K | 4.1 | | |
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| [GGUF](https://huggingface.co/mradermacher/DataVortexS-10.7B-v1.0-GGUF/resolve/main/DataVortexS-10.7B-v1.0.Q3_K_S.gguf) | Q3_K_S | 4.8 | | |
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| [GGUF](https://huggingface.co/mradermacher/DataVortexS-10.7B-v1.0-GGUF/resolve/main/DataVortexS-10.7B-v1.0.Q3_K_M.gguf) | Q3_K_M | 5.3 | lower quality | |
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| [GGUF](https://huggingface.co/mradermacher/DataVortexS-10.7B-v1.0-GGUF/resolve/main/DataVortexS-10.7B-v1.0.Q3_K_L.gguf) | Q3_K_L | 5.8 | | |
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| [GGUF](https://huggingface.co/mradermacher/DataVortexS-10.7B-v1.0-GGUF/resolve/main/DataVortexS-10.7B-v1.0.IQ4_XS.gguf) | IQ4_XS | 5.9 | | |
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| [GGUF](https://huggingface.co/mradermacher/DataVortexS-10.7B-v1.0-GGUF/resolve/main/DataVortexS-10.7B-v1.0.Q4_0_4_4.gguf) | Q4_0_4_4 | 6.2 | fast on arm, low quality | |
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| [GGUF](https://huggingface.co/mradermacher/DataVortexS-10.7B-v1.0-GGUF/resolve/main/DataVortexS-10.7B-v1.0.Q4_K_S.gguf) | Q4_K_S | 6.2 | fast, recommended | |
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| [GGUF](https://huggingface.co/mradermacher/DataVortexS-10.7B-v1.0-GGUF/resolve/main/DataVortexS-10.7B-v1.0.Q4_K_M.gguf) | Q4_K_M | 6.6 | fast, recommended | |
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| [GGUF](https://huggingface.co/mradermacher/DataVortexS-10.7B-v1.0-GGUF/resolve/main/DataVortexS-10.7B-v1.0.Q5_K_S.gguf) | Q5_K_S | 7.5 | | |
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| [GGUF](https://huggingface.co/mradermacher/DataVortexS-10.7B-v1.0-GGUF/resolve/main/DataVortexS-10.7B-v1.0.Q5_K_M.gguf) | Q5_K_M | 7.7 | | |
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| [GGUF](https://huggingface.co/mradermacher/DataVortexS-10.7B-v1.0-GGUF/resolve/main/DataVortexS-10.7B-v1.0.Q6_K.gguf) | Q6_K | 8.9 | very good quality | |
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| [GGUF](https://huggingface.co/mradermacher/DataVortexS-10.7B-v1.0-GGUF/resolve/main/DataVortexS-10.7B-v1.0.Q8_0.gguf) | Q8_0 | 11.5 | fast, best quality | |
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| [GGUF](https://huggingface.co/mradermacher/DataVortexS-10.7B-v1.0-GGUF/resolve/main/DataVortexS-10.7B-v1.0.f16.gguf) | f16 | 21.6 | 16 bpw, overkill | |
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Here is a handy graph by ikawrakow comparing some lower-quality quant |
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types (lower is better): |
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And here are Artefact2's thoughts on the matter: |
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https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 |
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## FAQ / Model Request |
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See https://huggingface.co/mradermacher/model_requests for some answers to |
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questions you might have and/or if you want some other model quantized. |
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## Thanks |
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I thank my company, [nethype GmbH](https://www.nethype.de/), for letting |
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me use its servers and providing upgrades to my workstation to enable |
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this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. |
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