auto-patch README.md

README.md

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+---
+base_model: Salesforce/xLAM-8x22b-r
+datasets:
+- Salesforce/xlam-function-calling-60k
+extra_gated_button_content: Agree and access repository
+extra_gated_fields:
+  Affiliation: text
+  Country: country
+  First Name: text
+  Last Name: text
+extra_gated_heading: Acknowledge to follow corresponding license to access the repository
+language:
+- en
+library_name: transformers
+license: cc-by-nc-4.0
+quantized_by: mradermacher
+tags:
+- function-calling
+- LLM Agent
+- tool-use
+- mistral
+- pytorch
+---
+## About
+
 <!-- ### quantize_version: 2 -->
 <!-- ### output_tensor_quantised: 1 -->
 <!-- ### convert_type: hf -->
 <!-- ### vocab_type:  -->
 <!-- ### tags:  -->
 static quants of https://huggingface.co/Salesforce/xLAM-8x22b-r
+
+<!-- provided-files -->
+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.
+## 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 |
+|:-----|:-----|--------:|:------|
+| [PART 1](https://huggingface.co/mradermacher/xLAM-8x22b-r-GGUF/resolve/main/xLAM-8x22b-r.Q2_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/xLAM-8x22b-r-GGUF/resolve/main/xLAM-8x22b-r.Q2_K.gguf.part2of2) | Q2_K | 52.2 |  |
+| [PART 1](https://huggingface.co/mradermacher/xLAM-8x22b-r-GGUF/resolve/main/xLAM-8x22b-r.Q3_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/xLAM-8x22b-r-GGUF/resolve/main/xLAM-8x22b-r.Q3_K_S.gguf.part2of2) | Q3_K_S | 61.6 |  |
+| [PART 1](https://huggingface.co/mradermacher/xLAM-8x22b-r-GGUF/resolve/main/xLAM-8x22b-r.Q3_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/xLAM-8x22b-r-GGUF/resolve/main/xLAM-8x22b-r.Q3_K_M.gguf.part2of2) | Q3_K_M | 67.9 | lower quality |
+| [PART 1](https://huggingface.co/mradermacher/xLAM-8x22b-r-GGUF/resolve/main/xLAM-8x22b-r.Q3_K_L.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/xLAM-8x22b-r-GGUF/resolve/main/xLAM-8x22b-r.Q3_K_L.gguf.part2of2) | Q3_K_L | 72.7 |  |
+| [PART 1](https://huggingface.co/mradermacher/xLAM-8x22b-r-GGUF/resolve/main/xLAM-8x22b-r.Q4_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/xLAM-8x22b-r-GGUF/resolve/main/xLAM-8x22b-r.Q4_K_S.gguf.part2of2) | Q4_K_S | 80.6 | fast, recommended |
+| [PART 1](https://huggingface.co/mradermacher/xLAM-8x22b-r-GGUF/resolve/main/xLAM-8x22b-r.Q6_K.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/xLAM-8x22b-r-GGUF/resolve/main/xLAM-8x22b-r.Q6_K.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/xLAM-8x22b-r-GGUF/resolve/main/xLAM-8x22b-r.Q6_K.gguf.part3of3) | Q6_K | 115.6 | very good quality |
+| [PART 1](https://huggingface.co/mradermacher/xLAM-8x22b-r-GGUF/resolve/main/xLAM-8x22b-r.Q8_0.gguf.part1of4) [PART 2](https://huggingface.co/mradermacher/xLAM-8x22b-r-GGUF/resolve/main/xLAM-8x22b-r.Q8_0.gguf.part2of4) [PART 3](https://huggingface.co/mradermacher/xLAM-8x22b-r-GGUF/resolve/main/xLAM-8x22b-r.Q8_0.gguf.part3of4) [PART 4](https://huggingface.co/mradermacher/xLAM-8x22b-r-GGUF/resolve/main/xLAM-8x22b-r.Q8_0.gguf.part4of4) | Q8_0 | 149.5 | fast, best quality |
+
+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 -->