base_model: Alfitaria/Q25-1.5B-VeoLu
datasets:
- allura-org/fujin-cleaned-stage-1
- Dampfinchen/Creative_Writing_Multiturn
- ToastyPigeon/SpringDragon
- allura-org/medquad_sharegpt
- allura-org/scienceqa_sharegpt
- Alignment-Lab-AI/orcamath-sharegpt
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- mergekit
- merge
- llama-factory
- lora
About
static quants of https://huggingface.co/Alfitaria/Q25-1.5B-VeoLu
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Q25-1.5B-VeoLu-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 | 0.9 | |
| GGUF | Q3_K_S | 1.0 | |
| GGUF | Q3_K_M | 1.0 | lower quality |
| GGUF | Q3_K_L | 1.1 | |
| GGUF | IQ4_XS | 1.1 | |
| GGUF | Q4_0_4_4 | 1.2 | fast on arm, low quality |
| GGUF | Q4_K_S | 1.2 | fast, recommended |
| GGUF | Q4_K_M | 1.2 | fast, recommended |
| GGUF | Q5_K_S | 1.4 | |
| GGUF | Q5_K_M | 1.4 | |
| GGUF | Q6_K | 1.6 | very good quality |
| GGUF | Q8_0 | 2.0 | fast, best quality |
| GGUF | f16 | 3.7 | 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.