base_model: argonne-private/AuroraGPT-IT-v4-0125
datasets:
- open-phi/textbooks
- open-phi/programming_books_llama
- openchat/openchat_sharegpt4_dataset
- nvidia/ChatQA-Training-Data
- jeffmeloy/sonnet3.5_science_conversations
- HuggingFaceH4/ultrachat_200k
- microsoft/orca-math-word-problems-200k
- m-a-p/CodeFeedback-Filtered-Instruction
- teknium/OpenHermes-2.5
- openbmb/UltraInteract_sft
language:
- en
library_name: transformers
quantized_by: mradermacher
tags: []
About
static quants of https://huggingface.co/argonne-private/AuroraGPT-IT-v4-0125
weighted/imatrix quants are available at https://huggingface.co/mradermacher/AuroraGPT-IT-v4-0125-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 | 2.3 | |
GGUF | Q3_K_S | 2.7 | |
GGUF | Q3_K_M | 3.0 | lower quality |
GGUF | Q3_K_L | 3.2 | |
GGUF | IQ4_XS | 3.3 | |
GGUF | Q4_K_S | 3.5 | fast, recommended |
GGUF | Q4_K_M | 3.7 | fast, recommended |
GGUF | Q5_K_S | 4.2 | |
GGUF | Q5_K_M | 4.3 | |
GGUF | Q6_K | 5.0 | very good quality |
GGUF | Q8_0 | 6.4 | fast, best quality |
GGUF | f16 | 12.0 | 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. 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.