base_model: Telugu-LLM-Labs/Indic-gemma-7b-finetuned-sft-Navarasa-2.0
datasets:
- ravithejads/samvaad-hi-filtered
- Telugu-LLM-Labs/telugu_teknium_GPTeacher_general_instruct_filtered_romanized
- Telugu-LLM-Labs/telugu_alpaca_yahma_cleaned_filtered_romanized
- Telugu-LLM-Labs/sindhi_alpaca_yahma_cleaned_filtered
- Telugu-LLM-Labs/urdu_alpaca_yahma_cleaned_filtered
- Telugu-LLM-Labs/marathi_alpaca_yahma_cleaned_filtered
- Telugu-LLM-Labs/assamese_alpaca_yahma_cleaned_filtered
- Telugu-LLM-Labs/konkani_alpaca_yahma_cleaned_filtered
- Telugu-LLM-Labs/nepali_alpaca_yahma_cleaned_filtered
- abhinand/tamil-alpaca
- Tensoic/airoboros-3.2_kn
- Tensoic/gpt-teacher_kn
- VishnuPJ/Alpaca_Instruct_Malayalam
- Tensoic/Alpaca-Gujarati
- HydraIndicLM/punjabi_alpaca_52K
- HydraIndicLM/bengali_alpaca_dolly_67k
- OdiaGenAI/Odia_Alpaca_instructions_52k
- yahma/alpaca-cleaned
language:
- te
- en
- ta
- ml
- mr
- hi
- kn
- sd
- ne
- ur
- as
- gu
- bn
- pa
- or
library_name: transformers
license: other
license_link: https://ai.google.dev/gemma/terms
license_name: gemma-terms-of-use
quantized_by: mradermacher
About
weighted/imatrix quants of https://huggingface.co/Telugu-LLM-Labs/Indic-gemma-7b-finetuned-sft-Navarasa-2.0
static quants are available at https://huggingface.co/mradermacher/Indic-gemma-7b-finetuned-sft-Navarasa-2.0-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-IQ1_S | 2.3 | for the desperate |
| GGUF | i1-IQ1_M | 2.4 | mostly desperate |
| GGUF | i1-IQ2_XXS | 2.7 | |
| GGUF | i1-IQ2_XS | 2.9 | |
| GGUF | i1-IQ2_S | 3.0 | |
| GGUF | i1-IQ2_M | 3.2 | |
| GGUF | i1-Q2_K_S | 3.3 | very low quality |
| GGUF | i1-Q2_K | 3.6 | IQ3_XXS probably better |
| GGUF | i1-IQ3_XXS | 3.6 | lower quality |
| GGUF | i1-IQ3_XS | 3.9 | |
| GGUF | i1-IQ3_S | 4.1 | beats Q3_K* |
| GGUF | i1-Q3_K_S | 4.1 | IQ3_XS probably better |
| GGUF | i1-IQ3_M | 4.2 | |
| GGUF | i1-Q3_K_M | 4.5 | IQ3_S probably better |
| GGUF | i1-Q3_K_L | 4.8 | IQ3_M probably better |
| GGUF | i1-IQ4_XS | 4.9 | |
| GGUF | i1-Q4_0_4_4 | 5.1 | fast on arm, low quality |
| GGUF | i1-Q4_0_4_8 | 5.1 | fast on arm+i8mm, low quality |
| GGUF | i1-Q4_0_8_8 | 5.1 | fast on arm+sve, low quality |
| GGUF | i1-Q4_0 | 5.1 | fast, low quality |
| GGUF | i1-Q4_K_S | 5.1 | optimal size/speed/quality |
| GGUF | i1-Q4_K_M | 5.4 | fast, recommended |
| GGUF | i1-Q5_K_S | 6.1 | |
| GGUF | i1-Q5_K_M | 6.2 | |
| GGUF | i1-Q6_K | 7.1 | 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.