base_model: nvidia/Minitron-4B-Base
language:
- en
library_name: transformers
license: other
license_link: >-
https://developer.download.nvidia.com/licenses/nvidia-open-model-license-agreement-june-2024.pdf
license_name: nvidia-open-model-license
quantized_by: mradermacher
tags:
- nvidia
- llama-3
- pytorch
About
static quants of https://huggingface.co/nvidia/Minitron-4B-Base
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Minitron-4B-Base-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 | 2.0 | |
| GGUF | IQ3_XS | 2.2 | |
| GGUF | IQ3_S | 2.2 | beats Q3_K* |
| GGUF | Q3_K_S | 2.2 | |
| GGUF | IQ3_M | 2.3 | |
| GGUF | Q3_K_M | 2.4 | lower quality |
| GGUF | Q3_K_L | 2.6 | |
| GGUF | IQ4_XS | 2.6 | |
| GGUF | Q4_K_S | 2.7 | fast, recommended |
| GGUF | Q4_K_M | 2.8 | fast, recommended |
| GGUF | Q5_K_S | 3.1 | |
| GGUF | Q5_K_M | 3.2 | |
| GGUF | Q6_K | 3.5 | very good quality |
| GGUF | Q8_0 | 4.6 | fast, best quality |
| GGUF | f16 | 8.5 | 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.