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--- |
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base_model: Salesforce/xLAM-8x22b-r |
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datasets: |
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- Salesforce/xlam-function-calling-60k |
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extra_gated_button_content: Agree and access repository |
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extra_gated_fields: |
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Affiliation: text |
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Country: country |
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First Name: text |
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Last Name: text |
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extra_gated_heading: Acknowledge to follow corresponding license to access the repository |
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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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- function-calling |
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- LLM Agent |
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- tool-use |
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- mistral |
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- pytorch |
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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: --> |
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static quants of https://huggingface.co/Salesforce/xLAM-8x22b-r |
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<!-- provided-files --> |
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weighted/imatrix quants are available at https://huggingface.co/mradermacher/xLAM-8x22b-r-i1-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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| [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 | | |
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| [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 | | |
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| [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 | |
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| [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 | | |
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| [PART 1](https://huggingface.co/mradermacher/xLAM-8x22b-r-GGUF/resolve/main/xLAM-8x22b-r.IQ4_XS.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/xLAM-8x22b-r-GGUF/resolve/main/xLAM-8x22b-r.IQ4_XS.gguf.part2of2) | IQ4_XS | 76.5 | | |
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| [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 | |
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| [PART 1](https://huggingface.co/mradermacher/xLAM-8x22b-r-GGUF/resolve/main/xLAM-8x22b-r.Q4_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/xLAM-8x22b-r-GGUF/resolve/main/xLAM-8x22b-r.Q4_K_M.gguf.part2of2) | Q4_K_M | 85.7 | fast, recommended | |
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| [PART 1](https://huggingface.co/mradermacher/xLAM-8x22b-r-GGUF/resolve/main/xLAM-8x22b-r.Q5_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/xLAM-8x22b-r-GGUF/resolve/main/xLAM-8x22b-r.Q5_K_S.gguf.part2of2) | Q5_K_S | 97.1 | | |
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| [PART 1](https://huggingface.co/mradermacher/xLAM-8x22b-r-GGUF/resolve/main/xLAM-8x22b-r.Q5_K_M.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/xLAM-8x22b-r-GGUF/resolve/main/xLAM-8x22b-r.Q5_K_M.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/xLAM-8x22b-r-GGUF/resolve/main/xLAM-8x22b-r.Q5_K_M.gguf.part3of3) | Q5_K_M | 100.1 | | |
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| [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 | |
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| [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 | |
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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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