base_model: nvidia/Llama-3.1-Minitron-4B-Width-Base
language:
- en
library_name: transformers
no_imatrix: 'cvs/llama.cpp/ggml/src/ggml.c:6399: GGML_ASSERT(c->ne[0] >= n_dims / 2) failed'
quantized_by: mradermacher
About
static quants of https://huggingface.co/nvidia/Llama-3.1-Minitron-4B-Width-Base
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.
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 | 1.9 | |
| GGUF | IQ3_XS | 2.1 | |
| GGUF | Q3_K_S | 2.2 | |
| GGUF | IQ3_S | 2.2 | beats Q3_K* |
| GGUF | IQ3_M | 2.3 | |
| GGUF | Q3_K_M | 2.4 | lower quality |
| GGUF | Q3_K_L | 2.6 | |
| GGUF | IQ4_XS | 2.7 | |
| GGUF | Q4_K_S | 2.8 | fast, recommended |
| GGUF | Q4_K_M | 2.9 | fast, recommended |
| GGUF | Q5_K_S | 3.3 | |
| GGUF | Q5_K_M | 3.3 | |
| GGUF | Q6_K | 3.8 | very good quality |
| GGUF | Q8_0 | 4.9 | fast, best quality |
| GGUF | f16 | 9.1 | 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.