mradermacher/mistral-nemo-nt-ko-12b-sft-i1-gguf overview

{
  "metadata": {},
  "card_data": {
    "base_model": "werty1248/Mistral-Nemo-NT-Ko-12B-sft",
    "datasets": [
      "4DR1455/finance_questions",
      "Aratako/Synthetic-JP-Conversations-Magpie-Nemotron-4-10k",
      "Aratako/Synthetic-JP-EN-Coding-Dataset-Magpie-69k",
      "Aratako/Synthetic-Japanese-Roleplay-NSFW-Claude-3.5s-10.5k-formatted",
      "BCCard/BCCard-Finance-Kor-QnA",
      "CarrotAI/ko-code-alpaca-QA",
      "ChuGyouk/AI_healthcare_QA_samples_Sonnet3.5",
      "DavidLanz/medical_instruction",
      "Dusker/lawyer-llama",
      "Gryphe/Sonnet3.5-Charcard-Roleplay",
      "HAERAE-HUB/qarv-instruct-ko",
      "HachiML/alpaca_jp_math",
      "Magpie-Align/Magpie-Llama-3.1-Pro-MT-300K-v0.1",
      "Magpie-Align/Magpie-Qwen2-Pro-200K-Chinese",
      "beomi/KoAlpaca-v1.1a",
      "codefuse-ai/Evol-instruction-66k",
      "frankminors123/belle-math-zh",
      "gbharti/wealth-alpaca_lora",
      "iam-ajaymeena/Self-Instruct-Japanese-Elzya-13B",
      "jihye-moon/LawQA-Ko",
      "jondurbin/gutenberg-dpo-v0.1",
      "junyeong-nero/kin_med_100K_edited",
      "kyujinpy/KOR-OpenOrca-Platypus-v3",
      "lavita/medical-qa-datasets",
      "microsoft/orca-math-word-problems-200k",
      "neural-bridge/rag-dataset-12000",
      "p1atdev/ichikara-instruction",
      "qiaojin/PubMedQA",
      "shibing624/roleplay-zh-sharegpt-gpt4-data",
      "team-hatakeyama-phase2/AutoMultiTurnByCalm3-22B-Corrected-reformatted",
      "ymoslem/Law-StackExchange",
      "zzunyang/LawQA_LawSee"
    ],
    "language": [
      "en",
      "ko",
      "ja",
      "zh"
    ],
    "library_name": "transformers",
    "license": "apache-2.0",
    "quantized_by": "mradermacher",
    "frontmatter": {
      "base_model": "werty1248/Mistral-Nemo-NT-Ko-12B-sft",
      "datasets": [
        "4DR1455/finance_questions",
        "Aratako/Synthetic-JP-Conversations-Magpie-Nemotron-4-10k",
        "Aratako/Synthetic-JP-EN-Coding-Dataset-Magpie-69k",
        "Aratako/Synthetic-Japanese-Roleplay-NSFW-Claude-3.5s-10.5k-formatted",
        "BCCard/BCCard-Finance-Kor-QnA",
        "CarrotAI/ko-code-alpaca-QA",
        "ChuGyouk/AI_healthcare_QA_samples_Sonnet3.5",
        "DavidLanz/medical_instruction",
        "Dusker/lawyer-llama",
        "Gryphe/Sonnet3.5-Charcard-Roleplay",
        "HAERAE-HUB/qarv-instruct-ko",
        "HachiML/alpaca_jp_math",
        "Magpie-Align/Magpie-Llama-3.1-Pro-MT-300K-v0.1",
        "Magpie-Align/Magpie-Qwen2-Pro-200K-Chinese",
        "beomi/KoAlpaca-v1.1a",
        "codefuse-ai/Evol-instruction-66k",
        "frankminors123/belle-math-zh",
        "gbharti/wealth-alpaca_lora",
        "iam-ajaymeena/Self-Instruct-Japanese-Elzya-13B",
        "jihye-moon/LawQA-Ko",
        "jondurbin/gutenberg-dpo-v0.1",
        "junyeong-nero/kin_med_100K_edited",
        "kyujinpy/KOR-OpenOrca-Platypus-v3",
        "lavita/medical-qa-datasets",
        "microsoft/orca-math-word-problems-200k",
        "neural-bridge/rag-dataset-12000",
        "p1atdev/ichikara-instruction",
        "qiaojin/PubMedQA",
        "shibing624/roleplay-zh-sharegpt-gpt4-data",
        "team-hatakeyama-phase2/AutoMultiTurnByCalm3-22B-Corrected-reformatted",
        "ymoslem/Law-StackExchange",
        "zzunyang/LawQA_LawSee"
      ],
      "language": [
        "en",
        "ko",
        "ja",
        "zh"
      ],
      "library_name": "transformers",
      "license": "apache-2.0",
      "quantized_by": "mradermacher"
    },
    "hero_image_url": "https://www.nethype.de/huggingface_embed/quantpplgraph.png",
    "summary": "## About      weighted/imatrix quants of https://huggingface.co/werty1248/Mistral-Nemo-NT-Ko-12B-sft  static quants are available at https://huggingface.co/mradermacher/Mistral-Nemo-NT-Ko-12B-sft-GGUF",
    "quick_links": [],
    "benchmark_table_html": "",
    "readme_markdown": "---\nbase_model: werty1248/Mistral-Nemo-NT-Ko-12B-sft\ndatasets:\n- 4DR1455/finance_questions\n- Aratako/Synthetic-JP-Conversations-Magpie-Nemotron-4-10k\n- Aratako/Synthetic-JP-EN-Coding-Dataset-Magpie-69k\n- Aratako/Synthetic-Japanese-Roleplay-NSFW-Claude-3.5s-10.5k-formatted\n- BCCard/BCCard-Finance-Kor-QnA\n- CarrotAI/ko-code-alpaca-QA\n- ChuGyouk/AI_healthcare_QA_samples_Sonnet3.5\n- DavidLanz/medical_instruction\n- Dusker/lawyer-llama\n- Gryphe/Sonnet3.5-Charcard-Roleplay\n- HAERAE-HUB/qarv-instruct-ko\n- HachiML/alpaca_jp_math\n- Magpie-Align/Magpie-Llama-3.1-Pro-MT-300K-v0.1\n- Magpie-Align/Magpie-Qwen2-Pro-200K-Chinese\n- beomi/KoAlpaca-v1.1a\n- codefuse-ai/Evol-instruction-66k\n- frankminors123/belle-math-zh\n- gbharti/wealth-alpaca_lora\n- iam-ajaymeena/Self-Instruct-Japanese-Elzya-13B\n- jihye-moon/LawQA-Ko\n- jondurbin/gutenberg-dpo-v0.1\n- junyeong-nero/kin_med_100K_edited\n- kyujinpy/KOR-OpenOrca-Platypus-v3\n- lavita/medical-qa-datasets\n- microsoft/orca-math-word-problems-200k\n- neural-bridge/rag-dataset-12000\n- p1atdev/ichikara-instruction\n- qiaojin/PubMedQA\n- shibing624/roleplay-zh-sharegpt-gpt4-data\n- team-hatakeyama-phase2/AutoMultiTurnByCalm3-22B-Corrected-reformatted\n- ymoslem/Law-StackExchange\n- zzunyang/LawQA_LawSee\nlanguage:\n- en\n- ko\n- ja\n- zh\nlibrary_name: transformers\nlicense: apache-2.0\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/werty1248/Mistral-Nemo-NT-Ko-12B-sft\n\n<!-- provided-files -->\nstatic quants are available at https://huggingface.co/mradermacher/Mistral-Nemo-NT-Ko-12B-sft-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/Mistral-Nemo-NT-Ko-12B-sft-i1-GGUF/resolve/main/Mistral-Nemo-NT-Ko-12B-sft.i1-IQ1_S.gguf) | i1-IQ1_S | 3.1 | for the desperate |\n| [GGUF](https://huggingface.co/mradermacher/Mistral-Nemo-NT-Ko-12B-sft-i1-GGUF/resolve/main/Mistral-Nemo-NT-Ko-12B-sft.i1-IQ1_M.gguf) | i1-IQ1_M | 3.3 | mostly desperate |\n| [GGUF](https://huggingface.co/mradermacher/Mistral-Nemo-NT-Ko-12B-sft-i1-GGUF/resolve/main/Mistral-Nemo-NT-Ko-12B-sft.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 3.7 |  |\n| [GGUF](https://huggingface.co/mradermacher/Mistral-Nemo-NT-Ko-12B-sft-i1-GGUF/resolve/main/Mistral-Nemo-NT-Ko-12B-sft.i1-IQ2_XS.gguf) | i1-IQ2_XS | 4.0 |  |\n| [GGUF](https://huggingface.co/mradermacher/Mistral-Nemo-NT-Ko-12B-sft-i1-GGUF/resolve/main/Mistral-Nemo-NT-Ko-12B-sft.i1-IQ2_S.gguf) | i1-IQ2_S | 4.2 |  |\n| [GGUF](https://huggingface.co/mradermacher/Mistral-Nemo-NT-Ko-12B-sft-i1-GGUF/resolve/main/Mistral-Nemo-NT-Ko-12B-sft.i1-IQ2_M.gguf) | i1-IQ2_M | 4.5 |  |\n| [GGUF](https://huggingface.co/mradermacher/Mistral-Nemo-NT-Ko-12B-sft-i1-GGUF/resolve/main/Mistral-Nemo-NT-Ko-12B-sft.i1-Q2_K.gguf) | i1-Q2_K | 4.9 | IQ3_XXS probably better |\n| [GGUF](https://huggingface.co/mradermacher/Mistral-Nemo-NT-Ko-12B-sft-i1-GGUF/resolve/main/Mistral-Nemo-NT-Ko-12B-sft.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 5.0 | lower quality |\n| [GGUF](https://huggingface.co/mradermacher/Mistral-Nemo-NT-Ko-12B-sft-i1-GGUF/resolve/main/Mistral-Nemo-NT-Ko-12B-sft.i1-IQ3_XS.gguf) | i1-IQ3_XS | 5.4 |  |\n| [GGUF](https://huggingface.co/mradermacher/Mistral-Nemo-NT-Ko-12B-sft-i1-GGUF/resolve/main/Mistral-Nemo-NT-Ko-12B-sft.i1-Q3_K_S.gguf) | i1-Q3_K_S | 5.6 | IQ3_XS probably better |\n| [GGUF](https://huggingface.co/mradermacher/Mistral-Nemo-NT-Ko-12B-sft-i1-GGUF/resolve/main/Mistral-Nemo-NT-Ko-12B-sft.i1-IQ3_S.gguf) | i1-IQ3_S | 5.7 | beats Q3_K* |\n| [GGUF](https://huggingface.co/mradermacher/Mistral-Nemo-NT-Ko-12B-sft-i1-GGUF/resolve/main/Mistral-Nemo-NT-Ko-12B-sft.i1-IQ3_M.gguf) | i1-IQ3_M | 5.8 |  |\n| [GGUF](https://huggingface.co/mradermacher/Mistral-Nemo-NT-Ko-12B-sft-i1-GGUF/resolve/main/Mistral-Nemo-NT-Ko-12B-sft.i1-Q3_K_M.gguf) | i1-Q3_K_M | 6.2 | IQ3_S probably better |\n| [GGUF](https://huggingface.co/mradermacher/Mistral-Nemo-NT-Ko-12B-sft-i1-GGUF/resolve/main/Mistral-Nemo-NT-Ko-12B-sft.i1-Q3_K_L.gguf) | i1-Q3_K_L | 6.7 | IQ3_M probably better |\n| [GGUF](https://huggingface.co/mradermacher/Mistral-Nemo-NT-Ko-12B-sft-i1-GGUF/resolve/main/Mistral-Nemo-NT-Ko-12B-sft.i1-IQ4_XS.gguf) | i1-IQ4_XS | 6.8 |  |\n| [GGUF](https://huggingface.co/mradermacher/Mistral-Nemo-NT-Ko-12B-sft-i1-GGUF/resolve/main/Mistral-Nemo-NT-Ko-12B-sft.i1-Q4_0_4_4.gguf) | i1-Q4_0_4_4 | 7.2 | fast on arm, low quality |\n| [GGUF](https://huggingface.co/mradermacher/Mistral-Nemo-NT-Ko-12B-sft-i1-GGUF/resolve/main/Mistral-Nemo-NT-Ko-12B-sft.i1-Q4_0_4_8.gguf) | i1-Q4_0_4_8 | 7.2 | fast on arm+i8mm, low quality |\n| [GGUF](https://huggingface.co/mradermacher/Mistral-Nemo-NT-Ko-12B-sft-i1-GGUF/resolve/main/Mistral-Nemo-NT-Ko-12B-sft.i1-Q4_0_8_8.gguf) | i1-Q4_0_8_8 | 7.2 | fast on arm+sve, low quality |\n| [GGUF](https://huggingface.co/mradermacher/Mistral-Nemo-NT-Ko-12B-sft-i1-GGUF/resolve/main/Mistral-Nemo-NT-Ko-12B-sft.i1-Q4_0.gguf) | i1-Q4_0 | 7.2 | fast, low quality |\n| [GGUF](https://huggingface.co/mradermacher/Mistral-Nemo-NT-Ko-12B-sft-i1-GGUF/resolve/main/Mistral-Nemo-NT-Ko-12B-sft.i1-Q4_K_S.gguf) | i1-Q4_K_S | 7.2 | optimal size/speed/quality |\n| [GGUF](https://huggingface.co/mradermacher/Mistral-Nemo-NT-Ko-12B-sft-i1-GGUF/resolve/main/Mistral-Nemo-NT-Ko-12B-sft.i1-Q4_K_M.gguf) | i1-Q4_K_M | 7.6 | fast, recommended |\n| [GGUF](https://huggingface.co/mradermacher/Mistral-Nemo-NT-Ko-12B-sft-i1-GGUF/resolve/main/Mistral-Nemo-NT-Ko-12B-sft.i1-Q5_K_S.gguf) | i1-Q5_K_S | 8.6 |  |\n| [GGUF](https://huggingface.co/mradermacher/Mistral-Nemo-NT-Ko-12B-sft-i1-GGUF/resolve/main/Mistral-Nemo-NT-Ko-12B-sft.i1-Q5_K_M.gguf) | i1-Q5_K_M | 8.8 |  |\n| [GGUF](https://huggingface.co/mradermacher/Mistral-Nemo-NT-Ko-12B-sft-i1-GGUF/resolve/main/Mistral-Nemo-NT-Ko-12B-sft.i1-Q6_K.gguf) | i1-Q6_K | 10.2 | 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",
    "en",
    "ko",
    "ja",
    "zh",
    "dataset:4DR1455/finance_questions",
    "dataset:Aratako/Synthetic-JP-Conversations-Magpie-Nemotron-4-10k",
    "dataset:Aratako/Synthetic-JP-EN-Coding-Dataset-Magpie-69k",
    "dataset:Aratako/Synthetic-Japanese-Roleplay-NSFW-Claude-3.5s-10.5k-formatted",
    "dataset:BCCard/BCCard-Finance-Kor-QnA",
    "dataset:CarrotAI/ko-code-alpaca-QA",
    "dataset:ChuGyouk/AI_healthcare_QA_samples_Sonnet3.5",
    "dataset:DavidLanz/medical_instruction",
    "dataset:Dusker/lawyer-llama",
    "dataset:Gryphe/Sonnet3.5-Charcard-Roleplay",
    "dataset:HAERAE-HUB/qarv-instruct-ko",
    "dataset:HachiML/alpaca_jp_math",
    "dataset:Magpie-Align/Magpie-Llama-3.1-Pro-MT-300K-v0.1",
    "dataset:Magpie-Align/Magpie-Qwen2-Pro-200K-Chinese",
    "dataset:beomi/KoAlpaca-v1.1a",
    "dataset:codefuse-ai/Evol-instruction-66k",
    "dataset:frankminors123/belle-math-zh",
    "dataset:gbharti/wealth-alpaca_lora",
    "dataset:iam-ajaymeena/Self-Instruct-Japanese-Elzya-13B",
    "dataset:jihye-moon/LawQA-Ko",
    "dataset:jondurbin/gutenberg-dpo-v0.1",
    "dataset:junyeong-nero/kin_med_100K_edited",
    "dataset:kyujinpy/KOR-OpenOrca-Platypus-v3",
    "dataset:lavita/medical-qa-datasets",
    "dataset:microsoft/orca-math-word-problems-200k",
    "dataset:neural-bridge/rag-dataset-12000",
    "dataset:p1atdev/ichikara-instruction",
    "dataset:qiaojin/PubMedQA",
    "dataset:shibing624/roleplay-zh-sharegpt-gpt4-data",
    "dataset:team-hatakeyama-phase2/AutoMultiTurnByCalm3-22B-Corrected-reformatted",
    "dataset:ymoslem/Law-StackExchange",
    "dataset:zzunyang/LawQA_LawSee",
    "base_model:werty1248/Mistral-Nemo-NT-Ko-12B-sft",
    "base_model:quantized:werty1248/Mistral-Nemo-NT-Ko-12B-sft",
    "license:apache-2.0",
    "endpoints_compatible",
    "region:us",
    "imatrix",
    "conversational"
  ],
  "likes": 0,
  "downloads": 2690,
  "gated": false,
  "private": false,
  "last_modified": "2024-09-20T08:36:05.000Z",
  "created_at": "2024-09-20T06:42:44.000Z",
  "pipeline_tag": "",
  "library_name": "transformers"
}

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