---
license: other
license_name: gemma-terms-of-use
license_link: https://ai.google.dev/gemma/terms
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
pipeline_tag: text-generation
tags:
- TensorBlock
- GGUF
---

<div style="width: auto; margin-left: auto; margin-right: auto">
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</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
    <div style="display: flex; flex-direction: column; align-items: flex-start;">
        <p style="margin-top: 0.5em; margin-bottom: 0em;">
            Feedback and support: TensorBlock's  <a href="https://x.com/tensorblock_aoi">Twitter/X</a>, <a href="https://t.me/TensorBlock">Telegram Group</a> and <a href="https://x.com/tensorblock_aoi">Discord server</a>
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## Telugu-LLM-Labs/Indic-gemma-2b-finetuned-sft-Navarasa-2.0 - GGUF

This repo contains GGUF format model files for [Telugu-LLM-Labs/Indic-gemma-2b-finetuned-sft-Navarasa-2.0](https://huggingface.co/Telugu-LLM-Labs/Indic-gemma-2b-finetuned-sft-Navarasa-2.0).

The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4242](https://github.com/ggerganov/llama.cpp/commit/a6744e43e80f4be6398fc7733a01642c846dce1d).

<div style="text-align: left; margin: 20px 0;">
    <a href="https://tensorblock.co/waitlist/client" style="display: inline-block; padding: 10px 20px; background-color: #007bff; color: white; text-decoration: none; border-radius: 5px; font-weight: bold;">
        Run them on the TensorBlock client using your local machine ↗
    </a>
</div>

## Prompt template

```

```

## Model file specification

| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [Indic-gemma-2b-finetuned-sft-Navarasa-2.0-Q2_K.gguf](https://huggingface.co/tensorblock/Indic-gemma-2b-finetuned-sft-Navarasa-2.0-GGUF/blob/main/Indic-gemma-2b-finetuned-sft-Navarasa-2.0-Q2_K.gguf) | Q2_K | 1.158 GB | smallest, significant quality loss - not recommended for most purposes |
| [Indic-gemma-2b-finetuned-sft-Navarasa-2.0-Q3_K_S.gguf](https://huggingface.co/tensorblock/Indic-gemma-2b-finetuned-sft-Navarasa-2.0-GGUF/blob/main/Indic-gemma-2b-finetuned-sft-Navarasa-2.0-Q3_K_S.gguf) | Q3_K_S | 1.288 GB | very small, high quality loss |
| [Indic-gemma-2b-finetuned-sft-Navarasa-2.0-Q3_K_M.gguf](https://huggingface.co/tensorblock/Indic-gemma-2b-finetuned-sft-Navarasa-2.0-GGUF/blob/main/Indic-gemma-2b-finetuned-sft-Navarasa-2.0-Q3_K_M.gguf) | Q3_K_M | 1.384 GB | very small, high quality loss |
| [Indic-gemma-2b-finetuned-sft-Navarasa-2.0-Q3_K_L.gguf](https://huggingface.co/tensorblock/Indic-gemma-2b-finetuned-sft-Navarasa-2.0-GGUF/blob/main/Indic-gemma-2b-finetuned-sft-Navarasa-2.0-Q3_K_L.gguf) | Q3_K_L | 1.466 GB | small, substantial quality loss |
| [Indic-gemma-2b-finetuned-sft-Navarasa-2.0-Q4_0.gguf](https://huggingface.co/tensorblock/Indic-gemma-2b-finetuned-sft-Navarasa-2.0-GGUF/blob/main/Indic-gemma-2b-finetuned-sft-Navarasa-2.0-Q4_0.gguf) | Q4_0 | 1.551 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [Indic-gemma-2b-finetuned-sft-Navarasa-2.0-Q4_K_S.gguf](https://huggingface.co/tensorblock/Indic-gemma-2b-finetuned-sft-Navarasa-2.0-GGUF/blob/main/Indic-gemma-2b-finetuned-sft-Navarasa-2.0-Q4_K_S.gguf) | Q4_K_S | 1.560 GB | small, greater quality loss |
| [Indic-gemma-2b-finetuned-sft-Navarasa-2.0-Q4_K_M.gguf](https://huggingface.co/tensorblock/Indic-gemma-2b-finetuned-sft-Navarasa-2.0-GGUF/blob/main/Indic-gemma-2b-finetuned-sft-Navarasa-2.0-Q4_K_M.gguf) | Q4_K_M | 1.630 GB | medium, balanced quality - recommended |
| [Indic-gemma-2b-finetuned-sft-Navarasa-2.0-Q5_0.gguf](https://huggingface.co/tensorblock/Indic-gemma-2b-finetuned-sft-Navarasa-2.0-GGUF/blob/main/Indic-gemma-2b-finetuned-sft-Navarasa-2.0-Q5_0.gguf) | Q5_0 | 1.799 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [Indic-gemma-2b-finetuned-sft-Navarasa-2.0-Q5_K_S.gguf](https://huggingface.co/tensorblock/Indic-gemma-2b-finetuned-sft-Navarasa-2.0-GGUF/blob/main/Indic-gemma-2b-finetuned-sft-Navarasa-2.0-Q5_K_S.gguf) | Q5_K_S | 1.799 GB | large, low quality loss - recommended |
| [Indic-gemma-2b-finetuned-sft-Navarasa-2.0-Q5_K_M.gguf](https://huggingface.co/tensorblock/Indic-gemma-2b-finetuned-sft-Navarasa-2.0-GGUF/blob/main/Indic-gemma-2b-finetuned-sft-Navarasa-2.0-Q5_K_M.gguf) | Q5_K_M | 1.840 GB | large, very low quality loss - recommended |
| [Indic-gemma-2b-finetuned-sft-Navarasa-2.0-Q6_K.gguf](https://huggingface.co/tensorblock/Indic-gemma-2b-finetuned-sft-Navarasa-2.0-GGUF/blob/main/Indic-gemma-2b-finetuned-sft-Navarasa-2.0-Q6_K.gguf) | Q6_K | 2.062 GB | very large, extremely low quality loss |
| [Indic-gemma-2b-finetuned-sft-Navarasa-2.0-Q8_0.gguf](https://huggingface.co/tensorblock/Indic-gemma-2b-finetuned-sft-Navarasa-2.0-GGUF/blob/main/Indic-gemma-2b-finetuned-sft-Navarasa-2.0-Q8_0.gguf) | Q8_0 | 2.669 GB | very large, extremely low quality loss - not recommended |


## Downloading instruction

### Command line

Firstly, install Huggingface Client

```shell
pip install -U "huggingface_hub[cli]"
```

Then, downoad the individual model file the a local directory

```shell
huggingface-cli download tensorblock/Indic-gemma-2b-finetuned-sft-Navarasa-2.0-GGUF --include "Indic-gemma-2b-finetuned-sft-Navarasa-2.0-Q2_K.gguf" --local-dir MY_LOCAL_DIR
```

If you wanna download multiple model files with a pattern (e.g., `*Q4_K*gguf`), you can try:

```shell
huggingface-cli download tensorblock/Indic-gemma-2b-finetuned-sft-Navarasa-2.0-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```