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--- |
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base_model: perlthoughts/Starling-LM-alpha-8x7B-MoE |
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datasets: |
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- berkeley-nest/Nectar |
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language: |
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- en |
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library_name: transformers |
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license: cc-by-nc-4.0 |
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quantized_by: mradermacher |
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tags: |
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- reward model |
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- RLHF |
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- RLAIF |
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- moe |
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--- |
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## About |
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static quants of https://huggingface.co/perlthoughts/Starling-LM-alpha-8x7B-MoE |
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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/Starling-LM-alpha-8x7B-MoE-GGUF/resolve/main/Starling-LM-alpha-8x7B-MoE.Q2_K.gguf) | Q2_K | 17.6 | | |
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| [GGUF](https://huggingface.co/mradermacher/Starling-LM-alpha-8x7B-MoE-GGUF/resolve/main/Starling-LM-alpha-8x7B-MoE.IQ3_XS.gguf) | IQ3_XS | 19.5 | | |
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| [GGUF](https://huggingface.co/mradermacher/Starling-LM-alpha-8x7B-MoE-GGUF/resolve/main/Starling-LM-alpha-8x7B-MoE.IQ3_S.gguf) | IQ3_S | 20.7 | beats Q3_K* | |
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| [GGUF](https://huggingface.co/mradermacher/Starling-LM-alpha-8x7B-MoE-GGUF/resolve/main/Starling-LM-alpha-8x7B-MoE.Q3_K_S.gguf) | Q3_K_S | 20.7 | | |
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| [GGUF](https://huggingface.co/mradermacher/Starling-LM-alpha-8x7B-MoE-GGUF/resolve/main/Starling-LM-alpha-8x7B-MoE.IQ3_M.gguf) | IQ3_M | 21.7 | | |
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| [GGUF](https://huggingface.co/mradermacher/Starling-LM-alpha-8x7B-MoE-GGUF/resolve/main/Starling-LM-alpha-8x7B-MoE.Q3_K_M.gguf) | Q3_K_M | 22.8 | lower quality | |
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| [GGUF](https://huggingface.co/mradermacher/Starling-LM-alpha-8x7B-MoE-GGUF/resolve/main/Starling-LM-alpha-8x7B-MoE.Q3_K_L.gguf) | Q3_K_L | 24.4 | | |
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| [GGUF](https://huggingface.co/mradermacher/Starling-LM-alpha-8x7B-MoE-GGUF/resolve/main/Starling-LM-alpha-8x7B-MoE.IQ4_XS.gguf) | IQ4_XS | 25.6 | | |
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| [GGUF](https://huggingface.co/mradermacher/Starling-LM-alpha-8x7B-MoE-GGUF/resolve/main/Starling-LM-alpha-8x7B-MoE.Q4_K_S.gguf) | Q4_K_S | 27.0 | fast, recommended | |
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| [GGUF](https://huggingface.co/mradermacher/Starling-LM-alpha-8x7B-MoE-GGUF/resolve/main/Starling-LM-alpha-8x7B-MoE.Q4_K_M.gguf) | Q4_K_M | 28.7 | fast, recommended | |
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| [GGUF](https://huggingface.co/mradermacher/Starling-LM-alpha-8x7B-MoE-GGUF/resolve/main/Starling-LM-alpha-8x7B-MoE.Q5_K_S.gguf) | Q5_K_S | 32.5 | | |
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| [GGUF](https://huggingface.co/mradermacher/Starling-LM-alpha-8x7B-MoE-GGUF/resolve/main/Starling-LM-alpha-8x7B-MoE.Q5_K_M.gguf) | Q5_K_M | 33.5 | | |
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| [GGUF](https://huggingface.co/mradermacher/Starling-LM-alpha-8x7B-MoE-GGUF/resolve/main/Starling-LM-alpha-8x7B-MoE.Q6_K.gguf) | Q6_K | 38.6 | very good quality | |
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| [PART 1](https://huggingface.co/mradermacher/Starling-LM-alpha-8x7B-MoE-GGUF/resolve/main/Starling-LM-alpha-8x7B-MoE.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Starling-LM-alpha-8x7B-MoE-GGUF/resolve/main/Starling-LM-alpha-8x7B-MoE.Q8_0.gguf.part2of2) | Q8_0 | 49.8 | 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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