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--- |
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base_model: cloudyu/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO |
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language: |
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- en |
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library_name: transformers |
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license: apache-2.0 |
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quantized_by: mradermacher |
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tags: |
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- yi |
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- moe |
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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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weighted/imatrix quants of https://huggingface.co/cloudyu/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO |
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<!-- provided-files --> |
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static quants are available at https://huggingface.co/mradermacher/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO-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/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO-i1-GGUF/resolve/main/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO.i1-IQ1_S.gguf) | i1-IQ1_S | 12.9 | for the desperate | |
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| [GGUF](https://huggingface.co/mradermacher/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO-i1-GGUF/resolve/main/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO.i1-IQ1_M.gguf) | i1-IQ1_M | 14.2 | mostly desperate | |
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| [GGUF](https://huggingface.co/mradermacher/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO-i1-GGUF/resolve/main/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 16.3 | | |
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| [GGUF](https://huggingface.co/mradermacher/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO-i1-GGUF/resolve/main/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO.i1-IQ2_XS.gguf) | i1-IQ2_XS | 18.1 | | |
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| [GGUF](https://huggingface.co/mradermacher/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO-i1-GGUF/resolve/main/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO.i1-IQ2_S.gguf) | i1-IQ2_S | 18.8 | | |
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| [GGUF](https://huggingface.co/mradermacher/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO-i1-GGUF/resolve/main/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO.i1-IQ2_M.gguf) | i1-IQ2_M | 20.5 | | |
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| [GGUF](https://huggingface.co/mradermacher/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO-i1-GGUF/resolve/main/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO.i1-Q2_K.gguf) | i1-Q2_K | 22.5 | IQ3_XXS probably better | |
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| [GGUF](https://huggingface.co/mradermacher/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO-i1-GGUF/resolve/main/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 23.6 | lower quality | |
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| [GGUF](https://huggingface.co/mradermacher/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO-i1-GGUF/resolve/main/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO.i1-IQ3_XS.gguf) | i1-IQ3_XS | 25.1 | | |
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| [GGUF](https://huggingface.co/mradermacher/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO-i1-GGUF/resolve/main/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO.i1-Q3_K_S.gguf) | i1-Q3_K_S | 26.4 | IQ3_XS probably better | |
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| [GGUF](https://huggingface.co/mradermacher/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO-i1-GGUF/resolve/main/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO.i1-IQ3_S.gguf) | i1-IQ3_S | 26.5 | beats Q3_K* | |
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| [GGUF](https://huggingface.co/mradermacher/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO-i1-GGUF/resolve/main/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO.i1-IQ3_M.gguf) | i1-IQ3_M | 27.2 | | |
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| [GGUF](https://huggingface.co/mradermacher/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO-i1-GGUF/resolve/main/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO.i1-Q3_K_M.gguf) | i1-Q3_K_M | 29.3 | IQ3_S probably better | |
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| [GGUF](https://huggingface.co/mradermacher/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO-i1-GGUF/resolve/main/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO.i1-Q3_K_L.gguf) | i1-Q3_K_L | 31.9 | IQ3_M probably better | |
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| [GGUF](https://huggingface.co/mradermacher/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO-i1-GGUF/resolve/main/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO.i1-IQ4_XS.gguf) | i1-IQ4_XS | 32.6 | | |
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| [GGUF](https://huggingface.co/mradermacher/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO-i1-GGUF/resolve/main/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO.i1-Q4_0.gguf) | i1-Q4_0 | 34.6 | fast, low quality | |
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| [GGUF](https://huggingface.co/mradermacher/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO-i1-GGUF/resolve/main/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO.i1-Q4_K_S.gguf) | i1-Q4_K_S | 34.7 | optimal size/speed/quality | |
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| [GGUF](https://huggingface.co/mradermacher/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO-i1-GGUF/resolve/main/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO.i1-Q4_K_M.gguf) | i1-Q4_K_M | 36.8 | fast, recommended | |
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| [GGUF](https://huggingface.co/mradermacher/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO-i1-GGUF/resolve/main/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO.i1-Q5_K_S.gguf) | i1-Q5_K_S | 42.0 | | |
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| [GGUF](https://huggingface.co/mradermacher/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO-i1-GGUF/resolve/main/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO.i1-Q5_K_M.gguf) | i1-Q5_K_M | 43.2 | | |
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| [GGUF](https://huggingface.co/mradermacher/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO-i1-GGUF/resolve/main/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO.i1-Q6_K.gguf) | i1-Q6_K | 50.0 | practically like static Q6_K | |
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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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