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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
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<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
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static quants of https://huggingface.co/Salesforce/xLAM-8x22b-r
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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.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 |
| [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):
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.
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