auto-patch README.md

README.md

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+---
+base_model: mostlyai/datallm-v2-mixtral-8x7b-v0.1
+datasets:
+- mostlyai/datallm-instructs-v2
+language:
+- en
+library_name: transformers
+license: apache-2.0
+quantized_by: mradermacher
+---
+## About
+
 <!-- ### quantize_version: 2 -->
 <!-- ### output_tensor_quantised: 1 -->
 <!-- ### convert_type: hf -->
 <!-- ### vocab_type:  -->
 <!-- ### tags: nicoboss -->
 weighted/imatrix quants of https://huggingface.co/mostlyai/datallm-v2-mixtral-8x7b-v0.1
+
+<!-- provided-files -->
+static quants are available at https://huggingface.co/mradermacher/datallm-v2-mixtral-8x7b-v0.1-GGUF
+## Usage
+
+If you are unsure how to use GGUF files, refer to one of [TheBloke's
+READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
+more details, including on how to concatenate multi-part files.
+
+## Provided Quants
+
+(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
+
+| Link | Type | Size/GB | Notes |
+|:-----|:-----|--------:|:------|
+| [GGUF](https://huggingface.co/mradermacher/datallm-v2-mixtral-8x7b-v0.1-i1-GGUF/resolve/main/datallm-v2-mixtral-8x7b-v0.1.i1-IQ1_S.gguf) | i1-IQ1_S | 9.9 | for the desperate |
+| [GGUF](https://huggingface.co/mradermacher/datallm-v2-mixtral-8x7b-v0.1-i1-GGUF/resolve/main/datallm-v2-mixtral-8x7b-v0.1.i1-IQ1_M.gguf) | i1-IQ1_M | 10.9 | mostly desperate |
+| [GGUF](https://huggingface.co/mradermacher/datallm-v2-mixtral-8x7b-v0.1-i1-GGUF/resolve/main/datallm-v2-mixtral-8x7b-v0.1.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 12.7 |  |
+| [GGUF](https://huggingface.co/mradermacher/datallm-v2-mixtral-8x7b-v0.1-i1-GGUF/resolve/main/datallm-v2-mixtral-8x7b-v0.1.i1-IQ2_XS.gguf) | i1-IQ2_XS | 14.0 |  |
+| [GGUF](https://huggingface.co/mradermacher/datallm-v2-mixtral-8x7b-v0.1-i1-GGUF/resolve/main/datallm-v2-mixtral-8x7b-v0.1.i1-IQ2_S.gguf) | i1-IQ2_S | 14.2 |  |
+| [GGUF](https://huggingface.co/mradermacher/datallm-v2-mixtral-8x7b-v0.1-i1-GGUF/resolve/main/datallm-v2-mixtral-8x7b-v0.1.i1-IQ2_M.gguf) | i1-IQ2_M | 15.6 |  |
+| [GGUF](https://huggingface.co/mradermacher/datallm-v2-mixtral-8x7b-v0.1-i1-GGUF/resolve/main/datallm-v2-mixtral-8x7b-v0.1.i1-Q2_K.gguf) | i1-Q2_K | 17.4 | IQ3_XXS probably better |
+| [GGUF](https://huggingface.co/mradermacher/datallm-v2-mixtral-8x7b-v0.1-i1-GGUF/resolve/main/datallm-v2-mixtral-8x7b-v0.1.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 18.3 | lower quality |
+| [GGUF](https://huggingface.co/mradermacher/datallm-v2-mixtral-8x7b-v0.1-i1-GGUF/resolve/main/datallm-v2-mixtral-8x7b-v0.1.i1-IQ3_XS.gguf) | i1-IQ3_XS | 19.5 |  |
+| [GGUF](https://huggingface.co/mradermacher/datallm-v2-mixtral-8x7b-v0.1-i1-GGUF/resolve/main/datallm-v2-mixtral-8x7b-v0.1.i1-IQ3_S.gguf) | i1-IQ3_S | 20.5 | beats Q3_K* |
+| [GGUF](https://huggingface.co/mradermacher/datallm-v2-mixtral-8x7b-v0.1-i1-GGUF/resolve/main/datallm-v2-mixtral-8x7b-v0.1.i1-Q3_K_S.gguf) | i1-Q3_K_S | 20.5 | IQ3_XS probably better |
+| [GGUF](https://huggingface.co/mradermacher/datallm-v2-mixtral-8x7b-v0.1-i1-GGUF/resolve/main/datallm-v2-mixtral-8x7b-v0.1.i1-IQ3_M.gguf) | i1-IQ3_M | 21.5 |  |
+| [GGUF](https://huggingface.co/mradermacher/datallm-v2-mixtral-8x7b-v0.1-i1-GGUF/resolve/main/datallm-v2-mixtral-8x7b-v0.1.i1-Q3_K_M.gguf) | i1-Q3_K_M | 22.6 | IQ3_S probably better |
+| [GGUF](https://huggingface.co/mradermacher/datallm-v2-mixtral-8x7b-v0.1-i1-GGUF/resolve/main/datallm-v2-mixtral-8x7b-v0.1.i1-Q3_K_L.gguf) | i1-Q3_K_L | 24.3 | IQ3_M probably better |
+| [GGUF](https://huggingface.co/mradermacher/datallm-v2-mixtral-8x7b-v0.1-i1-GGUF/resolve/main/datallm-v2-mixtral-8x7b-v0.1.i1-IQ4_XS.gguf) | i1-IQ4_XS | 25.2 |  |
+| [GGUF](https://huggingface.co/mradermacher/datallm-v2-mixtral-8x7b-v0.1-i1-GGUF/resolve/main/datallm-v2-mixtral-8x7b-v0.1.i1-Q4_0.gguf) | i1-Q4_0 | 26.7 | fast, low quality |
+| [GGUF](https://huggingface.co/mradermacher/datallm-v2-mixtral-8x7b-v0.1-i1-GGUF/resolve/main/datallm-v2-mixtral-8x7b-v0.1.i1-Q4_K_S.gguf) | i1-Q4_K_S | 26.8 | optimal size/speed/quality |
+| [GGUF](https://huggingface.co/mradermacher/datallm-v2-mixtral-8x7b-v0.1-i1-GGUF/resolve/main/datallm-v2-mixtral-8x7b-v0.1.i1-Q4_K_M.gguf) | i1-Q4_K_M | 28.5 | fast, recommended |
+| [GGUF](https://huggingface.co/mradermacher/datallm-v2-mixtral-8x7b-v0.1-i1-GGUF/resolve/main/datallm-v2-mixtral-8x7b-v0.1.i1-Q5_K_S.gguf) | i1-Q5_K_S | 32.3 |  |
+| [GGUF](https://huggingface.co/mradermacher/datallm-v2-mixtral-8x7b-v0.1-i1-GGUF/resolve/main/datallm-v2-mixtral-8x7b-v0.1.i1-Q5_K_M.gguf) | i1-Q5_K_M | 33.3 |  |
+| [GGUF](https://huggingface.co/mradermacher/datallm-v2-mixtral-8x7b-v0.1-i1-GGUF/resolve/main/datallm-v2-mixtral-8x7b-v0.1.i1-Q6_K.gguf) | i1-Q6_K | 38.5 | practically like static Q6_K |
+
+Here is a handy graph by ikawrakow comparing some lower-quality quant
+types (lower is better):
+
+![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png)
+
+And here are Artefact2's thoughts on the matter:
+https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
+
+## FAQ / Model Request
+
+See https://huggingface.co/mradermacher/model_requests for some answers to
+questions you might have and/or if you want some other model quantized.
+
+## Thanks
+
+I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
+me use its servers and providing upgrades to my workstation to enable
+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.
+
+<!-- end -->