base_model: anakin87/gemma-2-2b-neogenesis-ita
datasets:
- efederici/capybara-claude-15k-ita
- anakin87/fine-instructions-ita-70k
- mii-llm/argilla-math-preferences-it
- ruggsea/wsdm2024-cot-dataset
- anakin87/evol-dpo-ita-reranked
- anakin87/gemma-vs-gemma-preferences
- mlabonne/orpo-dpo-mix-40k
language:
- it
- en
library_name: transformers
license: gemma
quantized_by: mradermacher
About
static quants of https://huggingface.co/anakin87/gemma-2-2b-neogenesis-ita
weighted/imatrix quants are available at https://huggingface.co/mradermacher/gemma-2-2b-neogenesis-ita-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 | 1.3 | |
| GGUF | Q3_K_S | 1.5 | |
| GGUF | Q3_K_M | 1.6 | lower quality |
| GGUF | Q3_K_L | 1.7 | |
| GGUF | IQ4_XS | 1.7 | |
| GGUF | Q4_K_S | 1.7 | fast, recommended |
| GGUF | Q4_K_M | 1.8 | fast, recommended |
| GGUF | Q5_K_S | 2.0 | |
| GGUF | Q5_K_M | 2.0 | |
| GGUF | Q6_K | 2.3 | very good quality |
| GGUF | Q8_0 | 2.9 | fast, best quality |
| GGUF | f16 | 5.3 | 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.