Downloads · 30 days
17
28% of all-time downloads
mradermacher/l3-stack-choco-1-GGUF
l3-stack-choco-1-GGUF is a machine learning model from mradermacher. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers.
static quants of https://huggingface.co/CuriousCat29/l3-stack-choco-1
Downloads · 30 days
17
28% of all-time downloads
All-time downloads
60
Public
Repo size
829 GB
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0
Public
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.part1of2282 GB · 34%
From the Hugging Face model README
static quants of https://huggingface.co/CuriousCat29/l3-stack-choco-1
<!-- provided-files -->For a convenient overview and download list, visit our model page for this model.
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.
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.
(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 | 45.2 | |
| PART 1 PART 2 | Q3_K_S | 52.9 | |
| PART 1 PART 2 | Q3_K_M | 58.9 | lower quality |
| PART 1 PART 2 | Q3_K_L | 64.1 | |
| PART 1 PART 2 | IQ4_XS | 66.0 | |
| PART 1 PART 2 | Q4_K_S | 69.5 | fast, recommended |
| PART 1 PART 2 | Q4_K_M | 73.3 | fast, recommended |
| PART 1 PART 2 | Q5_K_S | 84.1 | |
| PART 1 PART 2 | Q5_K_M | 86.3 | |
| PART 1 PART 2 PART 3 | Q6_K | 100.1 | very good quality |
| PART 1 PART 2 PART 3 | Q8_0 | 129.6 | 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
See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized.
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.
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