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Indic-gemma-2b-finetuned-sft-Navarasa-2.0-GGUF
tensorblock/Indic-gemma-2b-finetuned-sft-Navarasa-2.0-GGUF
Indic-gemma-2b-finetuned-sft-Navarasa-2.0-GGUF is a text generation model from tensorblock. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as other.
<div style="width: auto; margin-left: auto; margin-right: auto" <img src="https://i.imgur.com/jC7kdl8.jpeg" alt="TensorBlock" style="width: 100%; min-width: 400px; display: block; margin: auto;" </div
All-time downloads
2.8K
Public
Repo size
20.2 GB
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0
Public
Repo makeup
Click a slice to open those files.
.gguf2.5 GB · 100%
At a glance
- Task
- Text Generation
- Library
- transformers
- License
- other
- Access
- Public
- Created
- Dec 12, 2024
- Updated
- Jan 27, 2026
- SHA
- 27edca41
Base models
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
Model card
From the Hugging Face model README
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/jC7kdl8.jpeg" alt="TensorBlock" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
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.
The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4242.
Our projects
<table border="1" cellspacing="0" cellpadding="10"> <tr> <th colspan="2" style="font-size: 25px;">Forge</th> </tr> <tr> <th colspan="2"> <img src="https://imgur.com/faI5UKh.jpeg" alt="Forge Project" width="900"/> </th> </tr> <tr> <th colspan="2">An OpenAI-compatible multi-provider routing layer.</th> </tr> <tr> <th colspan="2"> <a href="https://github.com/TensorBlock/forge" target="_blank" style=" display: inline-block; padding: 8px 16px; background-color: #FF7F50; color: white; text-decoration: none; border-radius: 6px; font-weight: bold; font-family: sans-serif; ">🚀 Try it now! 🚀</a> </th> </tr> <tr> <th style="font-size: 25px;">Awesome MCP Servers</th> <th style="font-size: 25px;">TensorBlock Studio</th> </tr> <tr> <th><img src="https://imgur.com/2Xov7B7.jpeg" alt="MCP Servers" width="450"/></th> <th><img src="https://imgur.com/pJcmF5u.jpeg" alt="Studio" width="450"/></th> </tr> <tr> <th>A comprehensive collection of Model Context Protocol (MCP) servers.</th> <th>A lightweight, open, and extensible multi-LLM interaction studio.</th> </tr> <tr> <th> <a href="https://github.com/TensorBlock/awesome-mcp-servers" target="_blank" style=" display: inline-block; padding: 8px 16px; background-color: #FF7F50; color: white; text-decoration: none; border-radius: 6px; font-weight: bold; font-family: sans-serif; ">👀 See what we built 👀</a> </th> <th> <a href="https://github.com/TensorBlock/TensorBlock-Studio" target="_blank" style=" display: inline-block; padding: 8px 16px; background-color: #FF7F50; color: white; text-decoration: none; border-radius: 6px; font-weight: bold; font-family: sans-serif; ">👀 See what we built 👀</a> </th> </tr> </table> ## Prompt templateModel file specification
| Filename | Quant type | File Size | Description |
|---|---|---|---|
| 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 | Q3_K_S | 1.288 GB | very small, high quality loss |
| 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 | Q3_K_L | 1.466 GB | small, substantial quality loss |
| 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 | Q4_K_S | 1.560 GB | small, greater quality loss |
| 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 | 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 | Q5_K_S | 1.799 GB | large, low quality loss - recommended |
| 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 | Q6_K | 2.062 GB | very large, extremely low quality loss |
| 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
pip install -U "huggingface_hub[cli]"
Then, downoad the individual model file the a local directory
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:
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'
- Task
- Text Generation
- Library
- transformers
- License
- other
- Languages
- te, en, ta, ml, mr, hi
- Created
- Dec 12, 2024
- Updated
- Jan 27, 2026