base_model: RLHF-And-Friends/FedPPO-Pythia-70M-a0
datasets: trl-internal-testing/descriptiveness-sentiment-trl-style
language:
- en
library_name: transformers
model_name: FedPPO-Pythia-70M-a0
quantized_by: mradermacher
tags:
- generated_from_trainer
About
static quants of https://huggingface.co/RLHF-And-Friends/FedPPO-Pythia-70M-a0
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 | 0.1 | |
| GGUF | Q3_K_S | 0.1 | |
| GGUF | Q3_K_M | 0.1 | lower quality |
| GGUF | Q3_K_L | 0.1 | |
| GGUF | IQ4_XS | 0.1 | |
| GGUF | Q4_K_S | 0.1 | fast, recommended |
| GGUF | Q4_K_M | 0.1 | fast, recommended |
| GGUF | Q5_K_S | 0.2 | |
| GGUF | Q5_K_M | 0.2 | |
| GGUF | Q6_K | 0.2 | very good quality |
| GGUF | Q8_0 | 0.2 | fast, best quality |
| GGUF | f16 | 0.2 | 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.