base_model: Salesforce/xLAM-8x7b-r
datasets:
- Salesforce/xlam-function-calling-60k
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Country: country
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language:
- en
library_name: transformers
license: cc-by-nc-4.0
quantized_by: mradermacher
tags:
- function-calling
- LLM Agent
- tool-use
- mistral
- pytorch
About
static quants of https://huggingface.co/Salesforce/xLAM-8x7b-r
weighted/imatrix quants are available at https://huggingface.co/mradermacher/xLAM-8x7b-r-i1-GGUF
Usage
If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs 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 | Q2_K | 17.4 | |
| GGUF | Q3_K_S | 20.5 | |
| GGUF | Q3_K_M | 22.6 | lower quality |
| GGUF | Q3_K_L | 24.3 | |
| GGUF | IQ4_XS | 25.5 | |
| GGUF | Q4_K_S | 26.8 | fast, recommended |
| GGUF | Q4_K_M | 28.5 | fast, recommended |
| GGUF | Q5_K_S | 32.3 | |
| GGUF | Q5_K_M | 33.3 | |
| GGUF | Q6_K | 38.5 | very good quality |
| GGUF | Q8_0 | 49.7 | 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, for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.