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--- |
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license: other |
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license_name: gemma-terms-of-use |
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license_link: https://ai.google.dev/gemma/terms |
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base_model: Telugu-LLM-Labs/Indic-gemma-2b-finetuned-sft-Navarasa-2.0 |
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datasets: |
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- ravithejads/samvaad-hi-filtered |
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- Telugu-LLM-Labs/telugu_teknium_GPTeacher_general_instruct_filtered_romanized |
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- Telugu-LLM-Labs/telugu_alpaca_yahma_cleaned_filtered_romanized |
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- Telugu-LLM-Labs/sindhi_alpaca_yahma_cleaned_filtered |
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- Telugu-LLM-Labs/urdu_alpaca_yahma_cleaned_filtered |
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- Telugu-LLM-Labs/marathi_alpaca_yahma_cleaned_filtered |
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- Telugu-LLM-Labs/assamese_alpaca_yahma_cleaned_filtered |
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- Telugu-LLM-Labs/konkani_alpaca_yahma_cleaned_filtered |
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- Telugu-LLM-Labs/nepali_alpaca_yahma_cleaned_filtered |
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- abhinand/tamil-alpaca |
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- Tensoic/airoboros-3.2_kn |
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- Tensoic/gpt-teacher_kn |
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- VishnuPJ/Alpaca_Instruct_Malayalam |
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- Tensoic/Alpaca-Gujarati |
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- HydraIndicLM/punjabi_alpaca_52K |
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- HydraIndicLM/bengali_alpaca_dolly_67k |
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- OdiaGenAI/Odia_Alpaca_instructions_52k |
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- yahma/alpaca-cleaned |
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language: |
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- te |
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- en |
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- ta |
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- ml |
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- mr |
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- hi |
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- kn |
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- sd |
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- ne |
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- ur |
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- as |
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- gu |
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- bn |
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- pa |
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- or |
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library_name: transformers |
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pipeline_tag: text-generation |
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tags: |
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- TensorBlock |
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- GGUF |
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--- |
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<div style="width: auto; margin-left: auto; margin-right: auto"> |
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<img src="https://i.imgur.com/jC7kdl8.jpeg" alt="TensorBlock" style="width: 100%; min-width: 400px; display: block; margin: auto;"> |
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</div> |
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<div style="display: flex; justify-content: space-between; width: 100%;"> |
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<div style="display: flex; flex-direction: column; align-items: flex-start;"> |
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<p style="margin-top: 0.5em; margin-bottom: 0em;"> |
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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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</p> |
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</div> |
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</div> |
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|
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## Telugu-LLM-Labs/Indic-gemma-2b-finetuned-sft-Navarasa-2.0 - GGUF |
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|
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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). |
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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). |
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|
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<div style="text-align: left; margin: 20px 0;"> |
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<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;"> |
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Run them on the TensorBlock client using your local machine ↗ |
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</a> |
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</div> |
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## Prompt template |
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``` |
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``` |
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## Model file specification |
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| Filename | Quant type | File Size | Description | |
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| -------- | ---------- | --------- | ----------- | |
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| [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 | |
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| [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 | |
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| [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 | |
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| [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 | |
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| [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 | |
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| [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 | |
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| [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 | |
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| [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 | |
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| [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 | |
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| [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 | |
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| [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 | |
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| [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 | |
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## Downloading instruction |
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### Command line |
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|
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Firstly, install Huggingface Client |
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|
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```shell |
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pip install -U "huggingface_hub[cli]" |
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``` |
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Then, downoad the individual model file the a local directory |
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|
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```shell |
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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 |
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``` |
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|
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If you wanna download multiple model files with a pattern (e.g., `*Q4_K*gguf`), you can try: |
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|
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```shell |
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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' |
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``` |
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|
