---
base_model: Telugu-LLM-Labs/Indic-gemma-2b-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

<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type:  -->
<!-- ### tags:  -->
static quants of https://huggingface.co/Telugu-LLM-Labs/Indic-gemma-2b-finetuned-sft-Navarasa-2.0

<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Indic-gemma-2b-finetuned-sft-Navarasa-2.0-i1-GGUF
## Usage

If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) 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](https://huggingface.co/mradermacher/Indic-gemma-2b-finetuned-sft-Navarasa-2.0-GGUF/resolve/main/Indic-gemma-2b-finetuned-sft-Navarasa-2.0.Q2_K.gguf) | Q2_K | 1.3 |  |
| [GGUF](https://huggingface.co/mradermacher/Indic-gemma-2b-finetuned-sft-Navarasa-2.0-GGUF/resolve/main/Indic-gemma-2b-finetuned-sft-Navarasa-2.0.Q3_K_S.gguf) | Q3_K_S | 1.4 |  |
| [GGUF](https://huggingface.co/mradermacher/Indic-gemma-2b-finetuned-sft-Navarasa-2.0-GGUF/resolve/main/Indic-gemma-2b-finetuned-sft-Navarasa-2.0.Q3_K_M.gguf) | Q3_K_M | 1.5 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Indic-gemma-2b-finetuned-sft-Navarasa-2.0-GGUF/resolve/main/Indic-gemma-2b-finetuned-sft-Navarasa-2.0.Q3_K_L.gguf) | Q3_K_L | 1.6 |  |
| [GGUF](https://huggingface.co/mradermacher/Indic-gemma-2b-finetuned-sft-Navarasa-2.0-GGUF/resolve/main/Indic-gemma-2b-finetuned-sft-Navarasa-2.0.IQ4_XS.gguf) | IQ4_XS | 1.6 |  |
| [GGUF](https://huggingface.co/mradermacher/Indic-gemma-2b-finetuned-sft-Navarasa-2.0-GGUF/resolve/main/Indic-gemma-2b-finetuned-sft-Navarasa-2.0.Q4_K_S.gguf) | Q4_K_S | 1.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Indic-gemma-2b-finetuned-sft-Navarasa-2.0-GGUF/resolve/main/Indic-gemma-2b-finetuned-sft-Navarasa-2.0.Q4_K_M.gguf) | Q4_K_M | 1.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Indic-gemma-2b-finetuned-sft-Navarasa-2.0-GGUF/resolve/main/Indic-gemma-2b-finetuned-sft-Navarasa-2.0.Q5_K_S.gguf) | Q5_K_S | 1.9 |  |
| [GGUF](https://huggingface.co/mradermacher/Indic-gemma-2b-finetuned-sft-Navarasa-2.0-GGUF/resolve/main/Indic-gemma-2b-finetuned-sft-Navarasa-2.0.Q5_K_M.gguf) | Q5_K_M | 1.9 |  |
| [GGUF](https://huggingface.co/mradermacher/Indic-gemma-2b-finetuned-sft-Navarasa-2.0-GGUF/resolve/main/Indic-gemma-2b-finetuned-sft-Navarasa-2.0.Q6_K.gguf) | Q6_K | 2.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Indic-gemma-2b-finetuned-sft-Navarasa-2.0-GGUF/resolve/main/Indic-gemma-2b-finetuned-sft-Navarasa-2.0.Q8_0.gguf) | Q8_0 | 2.8 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Indic-gemma-2b-finetuned-sft-Navarasa-2.0-GGUF/resolve/main/Indic-gemma-2b-finetuned-sft-Navarasa-2.0.f16.gguf) | f16 | 5.1 | 16 bpw, overkill |

Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):

![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png)

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](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.

<!-- end -->