base_model: jiangchengchengNLP/Llama-4-Scout-17B-16E-Instruct-abliterated
language:
- ar
- de
- en
- es
- fr
- hi
- id
- it
- pt
- th
- tl
- vi
library_name: transformers
license: llama4
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- facebook
- meta
- pytorch
- llama
- llama4
About
static quants of https://huggingface.co/jiangchengchengNLP/Llama-4-Scout-17B-16E-Instruct-abliterated
For a convenient overview and download list, visit our model page for this model.
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Llama-4-Scout-17B-16E-Instruct-abliterated-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 | mmproj-Q8_0 | 1.1 | multi-modal supplement |
| GGUF | mmproj-f16 | 1.9 | multi-modal supplement |
| GGUF | Q2_K | 39.7 | |
| GGUF | Q3_K_S | 46.8 | |
| PART 1 PART 2 | Q3_K_M | 51.9 | lower quality |
| PART 1 PART 2 | Q3_K_L | 56.1 | |
| PART 1 PART 2 | IQ4_XS | 58.4 | |
| PART 1 PART 2 | Q4_K_S | 61.6 | fast, recommended |
| PART 1 PART 2 | Q4_K_M | 65.5 | fast, recommended |
| PART 1 PART 2 | Q5_K_S | 74.4 | |
| PART 1 PART 2 | Q5_K_M | 76.6 | |
| PART 1 PART 2 | Q6_K | 88.5 | very good quality |
| PART 1 PART 2 PART 3 | Q8_0 | 114.6 | 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. Additional thanks to @nicoboss for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.