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QLiz-70B-GGUF
mradermacher/QLiz-70B-GGUF
QLiz-70B-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/Noodlz/QLiz-70B
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.gguf431 GB · 77%
At a glance
- Library
- transformers
- Access
- Public
- Created
- Apr 2, 2024
- Updated
- May 13, 2024
- SHA
- d5a32f8e
Base models
Model card
From the Hugging Face model README
About
<!-- ### convert_type: --> <!-- ### vocab_type: -->static quants of https://huggingface.co/Noodlz/QLiz-70B
This model seems badly borked.
<!-- provided-files -->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 | 25.9 | |
| GGUF | IQ3_XS | 28.4 | |
| GGUF | IQ3_S | 30.3 | beats Q3_K* |
| GGUF | Q3_K_S | 30.3 | |
| GGUF | IQ3_M | 31.0 | |
| GGUF | Q3_K_M | 33.7 | lower quality |
| GGUF | Q3_K_L | 36.6 | |
| GGUF | IQ4_XS | 37.3 | |
| GGUF | Q4_K_S | 39.7 | fast, recommended |
| GGUF | Q4_K_M | 41.8 | fast, recommended |
| GGUF | Q5_K_S | 47.9 | |
| GGUF | Q5_K_M | 49.2 | |
| PART 1 PART 2 | Q6_K | 57.0 | very good quality |
| PART 1 PART 2 | Q8_0 | 73.6 | 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
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.
<!-- end -->- Library
- transformers
- Languages
- en
- Created
- Apr 2, 2024
- Updated
- May 13, 2024