101 lines
5.1 KiB
Markdown
101 lines
5.1 KiB
Markdown
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---
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base_model: Telugu-LLM-Labs/Indic-gemma-7b-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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license: other
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license_link: https://ai.google.dev/gemma/terms
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license_name: gemma-terms-of-use
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quantized_by: mradermacher
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---
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## About
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<!-- ### quantize_version: 2 -->
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<!-- ### output_tensor_quantised: 1 -->
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<!-- ### convert_type: hf -->
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<!-- ### vocab_type: -->
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<!-- ### tags: nicoboss -->
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static quants of https://huggingface.co/Telugu-LLM-Labs/Indic-gemma-7b-finetuned-sft-Navarasa-2.0
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<!-- provided-files -->
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weighted/imatrix quants are available at https://huggingface.co/mradermacher/Indic-gemma-7b-finetuned-sft-Navarasa-2.0-i1-GGUF
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## Usage
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If you are unsure how to use GGUF files, refer to one of [TheBloke's
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READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
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more details, including on how to concatenate multi-part files.
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## Provided Quants
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(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
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| Link | Type | Size/GB | Notes |
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|:-----|:-----|--------:|:------|
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| [GGUF](https://huggingface.co/mradermacher/Indic-gemma-7b-finetuned-sft-Navarasa-2.0-GGUF/resolve/main/Indic-gemma-7b-finetuned-sft-Navarasa-2.0.Q2_K.gguf) | Q2_K | 3.6 | |
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| [GGUF](https://huggingface.co/mradermacher/Indic-gemma-7b-finetuned-sft-Navarasa-2.0-GGUF/resolve/main/Indic-gemma-7b-finetuned-sft-Navarasa-2.0.Q3_K_S.gguf) | Q3_K_S | 4.1 | |
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| [GGUF](https://huggingface.co/mradermacher/Indic-gemma-7b-finetuned-sft-Navarasa-2.0-GGUF/resolve/main/Indic-gemma-7b-finetuned-sft-Navarasa-2.0.Q3_K_M.gguf) | Q3_K_M | 4.5 | lower quality |
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| [GGUF](https://huggingface.co/mradermacher/Indic-gemma-7b-finetuned-sft-Navarasa-2.0-GGUF/resolve/main/Indic-gemma-7b-finetuned-sft-Navarasa-2.0.Q3_K_L.gguf) | Q3_K_L | 4.8 | |
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| [GGUF](https://huggingface.co/mradermacher/Indic-gemma-7b-finetuned-sft-Navarasa-2.0-GGUF/resolve/main/Indic-gemma-7b-finetuned-sft-Navarasa-2.0.IQ4_XS.gguf) | IQ4_XS | 4.9 | |
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| [GGUF](https://huggingface.co/mradermacher/Indic-gemma-7b-finetuned-sft-Navarasa-2.0-GGUF/resolve/main/Indic-gemma-7b-finetuned-sft-Navarasa-2.0.Q4_0_4_4.gguf) | Q4_0_4_4 | 5.1 | fast on arm, low quality |
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| [GGUF](https://huggingface.co/mradermacher/Indic-gemma-7b-finetuned-sft-Navarasa-2.0-GGUF/resolve/main/Indic-gemma-7b-finetuned-sft-Navarasa-2.0.Q4_K_S.gguf) | Q4_K_S | 5.1 | fast, recommended |
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| [GGUF](https://huggingface.co/mradermacher/Indic-gemma-7b-finetuned-sft-Navarasa-2.0-GGUF/resolve/main/Indic-gemma-7b-finetuned-sft-Navarasa-2.0.Q4_K_M.gguf) | Q4_K_M | 5.4 | fast, recommended |
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| [GGUF](https://huggingface.co/mradermacher/Indic-gemma-7b-finetuned-sft-Navarasa-2.0-GGUF/resolve/main/Indic-gemma-7b-finetuned-sft-Navarasa-2.0.Q5_K_S.gguf) | Q5_K_S | 6.1 | |
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| [GGUF](https://huggingface.co/mradermacher/Indic-gemma-7b-finetuned-sft-Navarasa-2.0-GGUF/resolve/main/Indic-gemma-7b-finetuned-sft-Navarasa-2.0.Q5_K_M.gguf) | Q5_K_M | 6.2 | |
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| [GGUF](https://huggingface.co/mradermacher/Indic-gemma-7b-finetuned-sft-Navarasa-2.0-GGUF/resolve/main/Indic-gemma-7b-finetuned-sft-Navarasa-2.0.Q6_K.gguf) | Q6_K | 7.1 | very good quality |
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| [GGUF](https://huggingface.co/mradermacher/Indic-gemma-7b-finetuned-sft-Navarasa-2.0-GGUF/resolve/main/Indic-gemma-7b-finetuned-sft-Navarasa-2.0.Q8_0.gguf) | Q8_0 | 9.2 | fast, best quality |
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| [GGUF](https://huggingface.co/mradermacher/Indic-gemma-7b-finetuned-sft-Navarasa-2.0-GGUF/resolve/main/Indic-gemma-7b-finetuned-sft-Navarasa-2.0.f16.gguf) | f16 | 17.2 | 16 bpw, overkill |
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Here is a handy graph by ikawrakow comparing some lower-quality quant
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types (lower is better):
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And here are Artefact2's thoughts on the matter:
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https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
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## FAQ / Model Request
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See https://huggingface.co/mradermacher/model_requests for some answers to
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questions you might have and/or if you want some other model quantized.
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## Thanks
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I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
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me use its servers and providing upgrades to my workstation to enable
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this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
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