base_model: HiTZ/latxa-7b-v1.2
datasets:
- HiTZ/latxa-corpus-v1.1
language:
- eu
- en
library_name: transformers
license: llama2
quantized_by: mradermacher
About
static quants of https://huggingface.co/HiTZ/latxa-7b-v1.2
weighted/imatrix quants are available at https://huggingface.co/mradermacher/latxa-7b-v1.2-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 | IQ3_XS | 2.9 | |
| GGUF | Q3_K_L | 3.7 | |
| GGUF | IQ4_XS | 3.7 | |
| GGUF | Q4_K_M | 4.2 | fast, recommended |
| GGUF | Q5_K_S | 4.8 | |
| GGUF | Q5_K_M | 4.9 | |
| PART 1 PART 2 | Q2_K | 5.2 | |
| PART 1 PART 2 | IQ3_S | 6.0 | beats Q3_K* |
| PART 1 PART 2 | Q3_K_S | 6.0 | |
| PART 1 PART 2 | IQ3_M | 6.3 | |
| PART 1 PART 2 | Q3_K_M | 6.7 | lower quality |
| PART 1 PART 2 | Q4_K_S | 7.8 | fast, recommended |
| PART 1 PART 2 | Q6_K | 11.2 | very good quality |
| PART 1 PART 2 | Q8_0 | 14.4 | fast, best quality |
| PART 1 PART 2 | f16 | 27.1 | 16 bpw, overkill |
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.