metadata
base_model:
- ConvexAI/Luminex-34B-v0.2
- fblgit/UNA-34BeagleSimpleMath-32K-v1
exported_from: louisbrulenaudet/Maxine-34B-stock
language:
- en
library_name: transformers
license: apache-2.0
no_imatrix: 'GGML_ASSERT: llama.cpp/ggml-quants.c:11239: grid_index >= 0'
quantized_by: mradermacher
tags:
- merge
- mergekit
- ConvexAI/Luminex-34B-v0.2
- fblgit/UNA-34BeagleSimpleMath-32K-v1
- chemistry
- biology
- math
About
static quants of https://huggingface.co/louisbrulenaudet/Maxine-34B-stock
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 | 12.9 | |
GGUF | IQ3_XS | 14.3 | |
GGUF | Q3_K_S | 15.1 | |
GGUF | IQ3_S | 15.1 | beats Q3_K* |
GGUF | IQ3_M | 15.7 | |
GGUF | Q3_K_M | 16.8 | lower quality |
GGUF | Q3_K_L | 18.2 | |
GGUF | IQ4_XS | 18.7 | |
GGUF | Q4_K_S | 19.7 | fast, recommended |
GGUF | Q4_K_M | 20.8 | fast, recommended |
GGUF | Q5_K_S | 23.8 | |
GGUF | Q5_K_M | 24.4 | |
GGUF | Q6_K | 28.3 | very good quality |
GGUF | Q8_0 | 36.6 | 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
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.