base_model: meditsolutions/Llama-3.2-SUN-1B-chat
datasets:
- argilla/OpenHermesPreferences
- argilla/magpie-ultra-v0.1
- argilla/Capybara-Preferences-Filtered
- mlabonne/open-perfectblend
- HuggingFaceTB/everyday-conversations-llama3.1-2k
- WizardLMTeam/WizardLM_evol_instruct_V2_196k
- ProlificAI/social-reasoning-rlhf
language:
- en
library_name: transformers
license: llama3.2
quantized_by: mradermacher
About
weighted/imatrix quants of https://huggingface.co/meditsolutions/Llama-3.2-SUN-1B-chat
static quants are available at https://huggingface.co/mradermacher/Llama-3.2-SUN-1B-chat-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 | i1-IQ2_M | 0.7 | |
| GGUF | i1-Q2_K | 0.8 | IQ3_XXS probably better |
| GGUF | i1-IQ3_XXS | 0.8 | lower quality |
| GGUF | i1-IQ3_M | 0.9 | |
| GGUF | i1-Q3_K_M | 0.9 | IQ3_S probably better |
| GGUF | i1-Q4_K_S | 1.0 | optimal size/speed/quality |
| GGUF | i1-Q4_K_M | 1.1 | fast, recommended |
| GGUF | i1-Q6_K | 1.3 | practically like static Q6_K |
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.