mradermacher/indic-gemma-2b-finetuned-sft-navarasa-2.0-i1-gguf overview

{
  "metadata": {},
  "card_data": {
    "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",
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      "or"
    ],
    "library_name": "transformers",
    "license": "other",
    "license_link": "https://ai.google.dev/gemma/terms",
    "license_name": "gemma-terms-of-use",
    "quantized_by": "mradermacher",
    "frontmatter": {
      "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",
      "license": "other",
      "license_link": "https://ai.google.dev/gemma/terms",
      "license_name": "gemma-terms-of-use",
      "quantized_by": "mradermacher"
    },
    "hero_image_url": "https://www.nethype.de/huggingface_embed/quantpplgraph.png",
    "summary": "## About      weighted/imatrix quants of https://huggingface.co/Telugu-LLM-Labs/Indic-gemma-2b-finetuned-sft-Navarasa-2.0  static quants are available at https://huggingface.co/mradermacher/Indic-gemma-2b-finetuned-sft-Navarasa-2.0-GGUF",
    "quick_links": [],
    "benchmark_table_html": "",
    "readme_markdown": "---\nbase_model: Telugu-LLM-Labs/Indic-gemma-2b-finetuned-sft-Navarasa-2.0\ndatasets:\n- ravithejads/samvaad-hi-filtered\n- Telugu-LLM-Labs/telugu_teknium_GPTeacher_general_instruct_filtered_romanized\n- Telugu-LLM-Labs/telugu_alpaca_yahma_cleaned_filtered_romanized\n- Telugu-LLM-Labs/sindhi_alpaca_yahma_cleaned_filtered\n- Telugu-LLM-Labs/urdu_alpaca_yahma_cleaned_filtered\n- Telugu-LLM-Labs/marathi_alpaca_yahma_cleaned_filtered\n- Telugu-LLM-Labs/assamese_alpaca_yahma_cleaned_filtered\n- Telugu-LLM-Labs/konkani_alpaca_yahma_cleaned_filtered\n- Telugu-LLM-Labs/nepali_alpaca_yahma_cleaned_filtered\n- abhinand/tamil-alpaca\n- Tensoic/airoboros-3.2_kn\n- Tensoic/gpt-teacher_kn\n- VishnuPJ/Alpaca_Instruct_Malayalam\n- Tensoic/Alpaca-Gujarati\n- HydraIndicLM/punjabi_alpaca_52K\n- HydraIndicLM/bengali_alpaca_dolly_67k\n- OdiaGenAI/Odia_Alpaca_instructions_52k\n- yahma/alpaca-cleaned\nlanguage:\n- te\n- en\n- ta\n- ml\n- mr\n- hi\n- kn\n- sd\n- ne\n- ur\n- as\n- gu\n- bn\n- pa\n- or\nlibrary_name: transformers\nlicense: other\nlicense_link: https://ai.google.dev/gemma/terms\nlicense_name: gemma-terms-of-use\nquantized_by: mradermacher\n---\n## About\n\n<!-- ### quantize_version: 2 -->\n<!-- ### output_tensor_quantised: 1 -->\n<!-- ### convert_type: hf -->\n<!-- ### vocab_type:  -->\n<!-- ### tags: nicoboss -->\nweighted/imatrix quants of https://huggingface.co/Telugu-LLM-Labs/Indic-gemma-2b-finetuned-sft-Navarasa-2.0\n\n<!-- provided-files -->\nstatic quants are available at https://huggingface.co/mradermacher/Indic-gemma-2b-finetuned-sft-Navarasa-2.0-GGUF\n## Usage\n\nIf you are unsure how to use GGUF files, refer to one of [TheBloke's\nREADMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for\nmore details, including on how to concatenate multi-part files.\n\n## Provided Quants\n\n(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)\n\n| Link | Type | Size/GB | Notes |\n|:-----|:-----|--------:|:------|\n| [GGUF](https://huggingface.co/mradermacher/Indic-gemma-2b-finetuned-sft-Navarasa-2.0-i1-GGUF/resolve/main/Indic-gemma-2b-finetuned-sft-Navarasa-2.0.i1-IQ1_S.gguf) | i1-IQ1_S | 0.9 | for the desperate |\n| [GGUF](https://huggingface.co/mradermacher/Indic-gemma-2b-finetuned-sft-Navarasa-2.0-i1-GGUF/resolve/main/Indic-gemma-2b-finetuned-sft-Navarasa-2.0.i1-IQ1_M.gguf) | i1-IQ1_M | 0.9 | mostly desperate |\n| [GGUF](https://huggingface.co/mradermacher/Indic-gemma-2b-finetuned-sft-Navarasa-2.0-i1-GGUF/resolve/main/Indic-gemma-2b-finetuned-sft-Navarasa-2.0.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 1.0 |  |\n| [GGUF](https://huggingface.co/mradermacher/Indic-gemma-2b-finetuned-sft-Navarasa-2.0-i1-GGUF/resolve/main/Indic-gemma-2b-finetuned-sft-Navarasa-2.0.i1-IQ2_XS.gguf) | i1-IQ2_XS | 1.0 |  |\n| [GGUF](https://huggingface.co/mradermacher/Indic-gemma-2b-finetuned-sft-Navarasa-2.0-i1-GGUF/resolve/main/Indic-gemma-2b-finetuned-sft-Navarasa-2.0.i1-IQ2_S.gguf) | i1-IQ2_S | 1.1 |  |\n| [GGUF](https://huggingface.co/mradermacher/Indic-gemma-2b-finetuned-sft-Navarasa-2.0-i1-GGUF/resolve/main/Indic-gemma-2b-finetuned-sft-Navarasa-2.0.i1-IQ2_M.gguf) | i1-IQ2_M | 1.1 |  |\n| [GGUF](https://huggingface.co/mradermacher/Indic-gemma-2b-finetuned-sft-Navarasa-2.0-i1-GGUF/resolve/main/Indic-gemma-2b-finetuned-sft-Navarasa-2.0.i1-Q2_K_S.gguf) | i1-Q2_K_S | 1.2 | very low quality |\n| [GGUF](https://huggingface.co/mradermacher/Indic-gemma-2b-finetuned-sft-Navarasa-2.0-i1-GGUF/resolve/main/Indic-gemma-2b-finetuned-sft-Navarasa-2.0.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 1.2 | lower quality |\n| [GGUF](https://huggingface.co/mradermacher/Indic-gemma-2b-finetuned-sft-Navarasa-2.0-i1-GGUF/resolve/main/Indic-gemma-2b-finetuned-sft-Navarasa-2.0.i1-Q2_K.gguf) | i1-Q2_K | 1.3 | IQ3_XXS probably better |\n| [GGUF](https://huggingface.co/mradermacher/Indic-gemma-2b-finetuned-sft-Navarasa-2.0-i1-GGUF/resolve/main/Indic-gemma-2b-finetuned-sft-Navarasa-2.0.i1-IQ3_XS.gguf) | i1-IQ3_XS | 1.3 |  |\n| [GGUF](https://huggingface.co/mradermacher/Indic-gemma-2b-finetuned-sft-Navarasa-2.0-i1-GGUF/resolve/main/Indic-gemma-2b-finetuned-sft-Navarasa-2.0.i1-Q3_K_S.gguf) | i1-Q3_K_S | 1.4 | IQ3_XS probably better |\n| [GGUF](https://huggingface.co/mradermacher/Indic-gemma-2b-finetuned-sft-Navarasa-2.0-i1-GGUF/resolve/main/Indic-gemma-2b-finetuned-sft-Navarasa-2.0.i1-IQ3_S.gguf) | i1-IQ3_S | 1.4 | beats Q3_K* |\n| [GGUF](https://huggingface.co/mradermacher/Indic-gemma-2b-finetuned-sft-Navarasa-2.0-i1-GGUF/resolve/main/Indic-gemma-2b-finetuned-sft-Navarasa-2.0.i1-IQ3_M.gguf) | i1-IQ3_M | 1.4 |  |\n| [GGUF](https://huggingface.co/mradermacher/Indic-gemma-2b-finetuned-sft-Navarasa-2.0-i1-GGUF/resolve/main/Indic-gemma-2b-finetuned-sft-Navarasa-2.0.i1-Q3_K_M.gguf) | i1-Q3_K_M | 1.5 | IQ3_S probably better |\n| [GGUF](https://huggingface.co/mradermacher/Indic-gemma-2b-finetuned-sft-Navarasa-2.0-i1-GGUF/resolve/main/Indic-gemma-2b-finetuned-sft-Navarasa-2.0.i1-Q3_K_L.gguf) | i1-Q3_K_L | 1.6 | IQ3_M probably better |\n| [GGUF](https://huggingface.co/mradermacher/Indic-gemma-2b-finetuned-sft-Navarasa-2.0-i1-GGUF/resolve/main/Indic-gemma-2b-finetuned-sft-Navarasa-2.0.i1-IQ4_XS.gguf) | i1-IQ4_XS | 1.6 |  |\n| [GGUF](https://huggingface.co/mradermacher/Indic-gemma-2b-finetuned-sft-Navarasa-2.0-i1-GGUF/resolve/main/Indic-gemma-2b-finetuned-sft-Navarasa-2.0.i1-Q4_0.gguf) | i1-Q4_0 | 1.7 | fast, low quality |\n| [GGUF](https://huggingface.co/mradermacher/Indic-gemma-2b-finetuned-sft-Navarasa-2.0-i1-GGUF/resolve/main/Indic-gemma-2b-finetuned-sft-Navarasa-2.0.i1-Q4_K_S.gguf) | i1-Q4_K_S | 1.7 | optimal size/speed/quality |\n| [GGUF](https://huggingface.co/mradermacher/Indic-gemma-2b-finetuned-sft-Navarasa-2.0-i1-GGUF/resolve/main/Indic-gemma-2b-finetuned-sft-Navarasa-2.0.i1-Q4_K_M.gguf) | i1-Q4_K_M | 1.7 | fast, recommended |\n| [GGUF](https://huggingface.co/mradermacher/Indic-gemma-2b-finetuned-sft-Navarasa-2.0-i1-GGUF/resolve/main/Indic-gemma-2b-finetuned-sft-Navarasa-2.0.i1-Q5_K_S.gguf) | i1-Q5_K_S | 1.9 |  |\n| [GGUF](https://huggingface.co/mradermacher/Indic-gemma-2b-finetuned-sft-Navarasa-2.0-i1-GGUF/resolve/main/Indic-gemma-2b-finetuned-sft-Navarasa-2.0.i1-Q5_K_M.gguf) | i1-Q5_K_M | 1.9 |  |\n| [GGUF](https://huggingface.co/mradermacher/Indic-gemma-2b-finetuned-sft-Navarasa-2.0-i1-GGUF/resolve/main/Indic-gemma-2b-finetuned-sft-Navarasa-2.0.i1-Q6_K.gguf) | i1-Q6_K | 2.2 | practically like static Q6_K |\n\nHere is a handy graph by ikawrakow comparing some lower-quality quant\ntypes (lower is better):\n\n![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png)\n\nAnd here are Artefact2's thoughts on the matter:\nhttps://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9\n\n## FAQ / Model Request\n\nSee https://huggingface.co/mradermacher/model_requests for some answers to\nquestions you might have and/or if you want some other model quantized.\n\n## Thanks\n\nI thank my company, [nethype GmbH](https://www.nethype.de/), for letting\nme use its servers and providing upgrades to my workstation to enable\nthis 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.\n\n<!-- end -->\n",
    "related_quantizations": []
  },
  "tags": [
    "transformers",
    "gguf",
    "te",
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    "hi",
    "kn",
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    "dataset:ravithejads/samvaad-hi-filtered",
    "dataset:Telugu-LLM-Labs/telugu_teknium_GPTeacher_general_instruct_filtered_romanized",
    "dataset:Telugu-LLM-Labs/telugu_alpaca_yahma_cleaned_filtered_romanized",
    "dataset:Telugu-LLM-Labs/sindhi_alpaca_yahma_cleaned_filtered",
    "dataset:Telugu-LLM-Labs/urdu_alpaca_yahma_cleaned_filtered",
    "dataset:Telugu-LLM-Labs/marathi_alpaca_yahma_cleaned_filtered",
    "dataset:Telugu-LLM-Labs/assamese_alpaca_yahma_cleaned_filtered",
    "dataset:Telugu-LLM-Labs/konkani_alpaca_yahma_cleaned_filtered",
    "dataset:Telugu-LLM-Labs/nepali_alpaca_yahma_cleaned_filtered",
    "dataset:abhinand/tamil-alpaca",
    "dataset:Tensoic/airoboros-3.2_kn",
    "dataset:Tensoic/gpt-teacher_kn",
    "dataset:VishnuPJ/Alpaca_Instruct_Malayalam",
    "dataset:Tensoic/Alpaca-Gujarati",
    "dataset:HydraIndicLM/punjabi_alpaca_52K",
    "dataset:HydraIndicLM/bengali_alpaca_dolly_67k",
    "dataset:OdiaGenAI/Odia_Alpaca_instructions_52k",
    "dataset:yahma/alpaca-cleaned",
    "base_model:Telugu-LLM-Labs/Indic-gemma-2b-finetuned-sft-Navarasa-2.0",
    "base_model:quantized:Telugu-LLM-Labs/Indic-gemma-2b-finetuned-sft-Navarasa-2.0",
    "license:other",
    "endpoints_compatible",
    "region:us",
    "imatrix"
  ],
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  "last_modified": "2024-12-23T17:34:52.000Z",
  "created_at": "2024-12-23T17:07:29.000Z",
  "pipeline_tag": "",
  "library_name": "transformers"
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