base_model: HPAI-BSC/Qwen2.5-Aloe-Beta-72B
datasets:
- HPAI-BSC/Aloe-Beta-General-Collection
- HPAI-BSC/chain-of-diagnosis
- HPAI-BSC/MedS-Ins
- HPAI-BSC/ultramedical
- HPAI-BSC/pubmedqa-cot-llama31
- HPAI-BSC/medqa-cot-llama31
- HPAI-BSC/medmcqa-cot-llama31
- HPAI-BSC/headqa-cot-llama31
- HPAI-BSC/MMLU-medical-cot-llama31
- HPAI-BSC/Polymed-QA
- HPAI-BSC/Aloe-Beta-General-Collection
- HPAI-BSC/Aloe-Beta-General-Collection
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- biology
- medical
- healthcare
About
static quants of https://huggingface.co/HPAI-BSC/Qwen2.5-Aloe-Beta-72B
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Qwen2.5-Aloe-Beta-72B-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 | 29.9 | |
| GGUF | Q3_K_S | 34.6 | |
| GGUF | Q3_K_M | 37.8 | lower quality |
| GGUF | Q3_K_L | 39.6 | |
| GGUF | IQ4_XS | 40.3 | |
| GGUF | Q4_K_S | 44.0 | fast, recommended |
| GGUF | Q4_K_M | 47.5 | fast, recommended |
| PART 1 PART 2 | Q5_K_S | 51.5 | |
| PART 1 PART 2 | Q5_K_M | 54.5 | |
| PART 1 PART 2 | Q6_K | 64.4 | very good quality |
| PART 1 PART 2 | Q8_0 | 77.4 | 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.