metadata
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
Feedback and support: TensorBlock's Twitter/X, Telegram Group and Discord server
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.
Prompt template
Model 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'