base_model: drwlf/Medra
datasets:
- qiaojin/PubMedQA
- Mreeb/Dermatology-Question-Answer-Dataset-For-Fine-Tuning
- lavita/MedQuAD
language:
- en
- ro
library_name: transformers
license: apache-2.0
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- text-generation
- medical-ai
- question-answering
- summarization
- dermatology
- gemma-3
- qlora
- unsloth
- fine-tuned
About
static quants of https://huggingface.co/drwlf/Medra
For a convenient overview and download list, visit our model page for this model.
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Medra-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 | 0.7 | multi-modal supplement |
| GGUF | mmproj-f16 | 1.0 | multi-modal supplement |
| GGUF | Q2_K | 1.8 | |
| GGUF | Q3_K_S | 2.0 | |
| GGUF | Q3_K_M | 2.2 | lower quality |
| GGUF | Q3_K_L | 2.3 | |
| GGUF | IQ4_XS | 2.4 | |
| GGUF | Q4_K_S | 2.5 | fast, recommended |
| GGUF | Q4_K_M | 2.6 | fast, recommended |
| GGUF | Q5_K_S | 2.9 | |
| GGUF | Q5_K_M | 2.9 | |
| GGUF | Q6_K | 3.3 | very good quality |
| GGUF | Q8_0 | 4.2 | fast, best quality |
| GGUF | f16 | 7.9 | 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.