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--- |
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base_model: grimulkan/Goliath-longLORA-120b-rope8-32k-fp16 |
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language: |
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- en |
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library_name: transformers |
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license: llama2 |
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quantized_by: mradermacher |
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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: --> |
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<!-- ### vocab_type: --> |
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static quants of https://huggingface.co/grimulkan/Goliath-longLORA-120b-rope8-32k-fp16 |
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<!-- provided-files --> |
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weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. |
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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/Goliath-longLORA-120b-rope8-32k-fp16-GGUF/resolve/main/Goliath-longLORA-120b-rope8-32k-fp16.Q2_K.gguf) | Q2_K | 43.3 | | |
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| [GGUF](https://huggingface.co/mradermacher/Goliath-longLORA-120b-rope8-32k-fp16-GGUF/resolve/main/Goliath-longLORA-120b-rope8-32k-fp16.IQ3_XS.gguf) | IQ3_XS | 48.2 | | |
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| [PART 1](https://huggingface.co/mradermacher/Goliath-longLORA-120b-rope8-32k-fp16-GGUF/resolve/main/Goliath-longLORA-120b-rope8-32k-fp16.Q3_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Goliath-longLORA-120b-rope8-32k-fp16-GGUF/resolve/main/Goliath-longLORA-120b-rope8-32k-fp16.Q3_K_S.gguf.part2of2) | Q3_K_S | 50.8 | | |
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| [PART 1](https://huggingface.co/mradermacher/Goliath-longLORA-120b-rope8-32k-fp16-GGUF/resolve/main/Goliath-longLORA-120b-rope8-32k-fp16.IQ3_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Goliath-longLORA-120b-rope8-32k-fp16-GGUF/resolve/main/Goliath-longLORA-120b-rope8-32k-fp16.IQ3_S.gguf.part2of2) | IQ3_S | 51.0 | beats Q3_K* | |
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| [PART 1](https://huggingface.co/mradermacher/Goliath-longLORA-120b-rope8-32k-fp16-GGUF/resolve/main/Goliath-longLORA-120b-rope8-32k-fp16.IQ3_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Goliath-longLORA-120b-rope8-32k-fp16-GGUF/resolve/main/Goliath-longLORA-120b-rope8-32k-fp16.IQ3_M.gguf.part2of2) | IQ3_M | 52.7 | | |
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| [PART 1](https://huggingface.co/mradermacher/Goliath-longLORA-120b-rope8-32k-fp16-GGUF/resolve/main/Goliath-longLORA-120b-rope8-32k-fp16.Q3_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Goliath-longLORA-120b-rope8-32k-fp16-GGUF/resolve/main/Goliath-longLORA-120b-rope8-32k-fp16.Q3_K_M.gguf.part2of2) | Q3_K_M | 56.7 | lower quality | |
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| [PART 1](https://huggingface.co/mradermacher/Goliath-longLORA-120b-rope8-32k-fp16-GGUF/resolve/main/Goliath-longLORA-120b-rope8-32k-fp16.Q3_K_L.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Goliath-longLORA-120b-rope8-32k-fp16-GGUF/resolve/main/Goliath-longLORA-120b-rope8-32k-fp16.Q3_K_L.gguf.part2of2) | Q3_K_L | 61.8 | | |
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| [PART 1](https://huggingface.co/mradermacher/Goliath-longLORA-120b-rope8-32k-fp16-GGUF/resolve/main/Goliath-longLORA-120b-rope8-32k-fp16.IQ4_XS.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Goliath-longLORA-120b-rope8-32k-fp16-GGUF/resolve/main/Goliath-longLORA-120b-rope8-32k-fp16.IQ4_XS.gguf.part2of2) | IQ4_XS | 63.5 | | |
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| [PART 1](https://huggingface.co/mradermacher/Goliath-longLORA-120b-rope8-32k-fp16-GGUF/resolve/main/Goliath-longLORA-120b-rope8-32k-fp16.Q4_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Goliath-longLORA-120b-rope8-32k-fp16-GGUF/resolve/main/Goliath-longLORA-120b-rope8-32k-fp16.Q4_K_S.gguf.part2of2) | Q4_K_S | 66.9 | fast, recommended | |
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| [PART 1](https://huggingface.co/mradermacher/Goliath-longLORA-120b-rope8-32k-fp16-GGUF/resolve/main/Goliath-longLORA-120b-rope8-32k-fp16.Q4_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Goliath-longLORA-120b-rope8-32k-fp16-GGUF/resolve/main/Goliath-longLORA-120b-rope8-32k-fp16.Q4_K_M.gguf.part2of2) | Q4_K_M | 70.7 | fast, recommended | |
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| [PART 1](https://huggingface.co/mradermacher/Goliath-longLORA-120b-rope8-32k-fp16-GGUF/resolve/main/Goliath-longLORA-120b-rope8-32k-fp16.Q5_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Goliath-longLORA-120b-rope8-32k-fp16-GGUF/resolve/main/Goliath-longLORA-120b-rope8-32k-fp16.Q5_K_S.gguf.part2of2) | Q5_K_S | 81.1 | | |
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| [PART 1](https://huggingface.co/mradermacher/Goliath-longLORA-120b-rope8-32k-fp16-GGUF/resolve/main/Goliath-longLORA-120b-rope8-32k-fp16.Q5_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Goliath-longLORA-120b-rope8-32k-fp16-GGUF/resolve/main/Goliath-longLORA-120b-rope8-32k-fp16.Q5_K_M.gguf.part2of2) | Q5_K_M | 83.3 | | |
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| [PART 1](https://huggingface.co/mradermacher/Goliath-longLORA-120b-rope8-32k-fp16-GGUF/resolve/main/Goliath-longLORA-120b-rope8-32k-fp16.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Goliath-longLORA-120b-rope8-32k-fp16-GGUF/resolve/main/Goliath-longLORA-120b-rope8-32k-fp16.Q6_K.gguf.part2of2) | Q6_K | 96.7 | very good quality | |
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| [PART 1](https://huggingface.co/mradermacher/Goliath-longLORA-120b-rope8-32k-fp16-GGUF/resolve/main/Goliath-longLORA-120b-rope8-32k-fp16.Q8_0.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/Goliath-longLORA-120b-rope8-32k-fp16-GGUF/resolve/main/Goliath-longLORA-120b-rope8-32k-fp16.Q8_0.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/Goliath-longLORA-120b-rope8-32k-fp16-GGUF/resolve/main/Goliath-longLORA-120b-rope8-32k-fp16.Q8_0.gguf.part3of3) | Q8_0 | 125.2 | fast, best quality | |
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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. |
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