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TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO-GGUF

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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: -->
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  static quants of https://huggingface.co/cloudyu/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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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+
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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: -->
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  static quants of https://huggingface.co/cloudyu/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO
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+
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+ <!-- provided-files -->
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+ weighted/imatrix quants are available at https://huggingface.co/mradermacher/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO-i1-GGUF
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+ ## Usage
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+
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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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+
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+ ## Provided Quants
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+
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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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+
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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-GGUF/resolve/main/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO.Q2_K.gguf) | Q2_K | 22.5 | |
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+ | [GGUF](https://huggingface.co/mradermacher/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO-GGUF/resolve/main/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO.IQ3_XS.gguf) | IQ3_XS | 25.1 | |
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+ | [GGUF](https://huggingface.co/mradermacher/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO-GGUF/resolve/main/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO.Q3_K_S.gguf) | Q3_K_S | 26.4 | |
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+ | [GGUF](https://huggingface.co/mradermacher/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO-GGUF/resolve/main/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO.IQ3_S.gguf) | 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-GGUF/resolve/main/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO.IQ3_M.gguf) | IQ3_M | 27.2 | |
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+ | [GGUF](https://huggingface.co/mradermacher/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO-GGUF/resolve/main/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO.Q3_K_M.gguf) | Q3_K_M | 29.3 | lower quality |
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+ | [GGUF](https://huggingface.co/mradermacher/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO-GGUF/resolve/main/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO.Q3_K_L.gguf) | Q3_K_L | 31.9 | |
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+ | [GGUF](https://huggingface.co/mradermacher/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO-GGUF/resolve/main/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO.IQ4_XS.gguf) | IQ4_XS | 32.9 | |
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+ | [GGUF](https://huggingface.co/mradermacher/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO-GGUF/resolve/main/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO.Q4_K_S.gguf) | Q4_K_S | 34.7 | fast, recommended |
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+ | [GGUF](https://huggingface.co/mradermacher/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO-GGUF/resolve/main/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO.Q4_K_M.gguf) | 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-GGUF/resolve/main/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO.Q5_K_S.gguf) | Q5_K_S | 42.0 | |
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+ | [GGUF](https://huggingface.co/mradermacher/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO-GGUF/resolve/main/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO.Q5_K_M.gguf) | Q5_K_M | 43.2 | |
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+ | [GGUF](https://huggingface.co/mradermacher/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO-GGUF/resolve/main/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO.Q6_K.gguf) | Q6_K | 50.0 | very good quality |
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+ | [PART 1](https://huggingface.co/mradermacher/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO-GGUF/resolve/main/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO-GGUF/resolve/main/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO.Q8_0.gguf.part2of2) | Q8_0 | 64.7 | fast, best quality |
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+
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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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+
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+ ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png)
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+
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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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+
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+ ## FAQ / Model Request
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+
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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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+
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+ ## Thanks
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+
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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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+
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+ <!-- end -->