mradermacher/commandr-35b-cpt-sft-i1-gguf overview

{
  "metadata": {},
  "card_data": {
    "base_model": "ubitech-edg/commandr-35b-cpt-sft",
    "datasets": [
      "arxiv",
      "gov",
      "news",
      "wikipedia",
      "axolotl_deduplicated_synthetic_qa"
    ],
    "language": [
      "en"
    ],
    "library_name": "transformers",
    "license": "apache-2.0",
    "model_name": "commandr-35b-cpt-sft",
    "model_type": "AutoModelForCausalLM",
    "mradermacher": {
      "readme_rev": 1
    },
    "quantized_by": "mradermacher",
    "tags": [
      "text-generation",
      "causal-lm",
      "two-stage-training",
      "continual-pretraining",
      "supervised-fine-tuning",
      "synthetic-qa",
      "lora",
      "axolotl",
      "deepspeed",
      "transformers",
      "commandr",
      "cohere",
      "eu-hpc"
    ],
    "frontmatter": {
      "base_model": "ubitech-edg/commandr-35b-cpt-sft",
      "datasets": [
        "arxiv",
        "gov",
        "news",
        "wikipedia",
        "axolotl_deduplicated_synthetic_qa"
      ],
      "language": [
        "en"
      ],
      "library_name": "transformers",
      "license": "apache-2.0",
      "model_name": "commandr-35b-cpt-sft",
      "model_type": "AutoModelForCausalLM",
      "mradermacher": [],
      "quantized_by": "mradermacher",
      "tags": [
        "text-generation",
        "causal-lm",
        "two-stage-training",
        "continual-pretraining",
        "supervised-fine-tuning",
        "synthetic-qa",
        "lora",
        "axolotl",
        "deepspeed",
        "transformers",
        "commandr",
        "cohere",
        "eu-hpc"
      ]
    },
    "hero_image_url": "https://www.nethype.de/huggingface_embed/quantpplgraph.png",
    "summary": "## About         weighted/imatrix quants of https://huggingface.co/ubitech-edg/commandr-35b-cpt-sft  ***For a convenient overview and download list, visit our model page for this model.*** static quants are available at https://huggingface.co/mradermacher/commandr-35b-cpt-sft-GGUF",
    "quick_links": [],
    "benchmark_table_html": "",
    "readme_markdown": "---\nbase_model: ubitech-edg/commandr-35b-cpt-sft\ndatasets:\n- arxiv\n- gov\n- news\n- wikipedia\n- axolotl_deduplicated_synthetic_qa\nlanguage:\n- en\nlibrary_name: transformers\nlicense: apache-2.0\nmodel_name: commandr-35b-cpt-sft\nmodel_type: AutoModelForCausalLM\nmradermacher:\n  readme_rev: 1\nquantized_by: mradermacher\ntags:\n- text-generation\n- causal-lm\n- two-stage-training\n- continual-pretraining\n- supervised-fine-tuning\n- synthetic-qa\n- lora\n- axolotl\n- deepspeed\n- transformers\n- commandr\n- cohere\n- eu-hpc\n---\n## About\n\n<!-- ### quantize_version: 2 -->\n<!-- ### output_tensor_quantised: 1 -->\n<!-- ### convert_type: hf -->\n<!-- ### vocab_type:  -->\n<!-- ### tags: nicoboss -->\n<!-- ### quants:  Q2_K IQ3_M Q4_K_S IQ3_XXS Q3_K_M small-IQ4_NL Q4_K_M IQ2_M Q6_K IQ4_XS Q2_K_S IQ1_M Q3_K_S IQ2_XXS Q3_K_L IQ2_XS Q5_K_S IQ2_S IQ1_S Q5_K_M Q4_0 IQ3_XS Q4_1 IQ3_S -->\n<!-- ### quants_skip:  -->\n<!-- ### skip_mmproj:  -->\nweighted/imatrix quants of https://huggingface.co/ubitech-edg/commandr-35b-cpt-sft\n\n<!-- provided-files -->\n\n***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#commandr-35b-cpt-sft-i1-GGUF).***\n\nstatic quants are available at https://huggingface.co/mradermacher/commandr-35b-cpt-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/commandr-35b-cpt-sft-i1-GGUF/resolve/main/commandr-35b-cpt-sft.imatrix.gguf) | imatrix | 0.1 | imatrix file (for creating your own quants) |\n| [GGUF](https://huggingface.co/mradermacher/commandr-35b-cpt-sft-i1-GGUF/resolve/main/commandr-35b-cpt-sft.i1-IQ1_S.gguf) | i1-IQ1_S | 8.6 | for the desperate |\n| [GGUF](https://huggingface.co/mradermacher/commandr-35b-cpt-sft-i1-GGUF/resolve/main/commandr-35b-cpt-sft.i1-IQ1_M.gguf) | i1-IQ1_M | 9.2 | mostly desperate |\n| [GGUF](https://huggingface.co/mradermacher/commandr-35b-cpt-sft-i1-GGUF/resolve/main/commandr-35b-cpt-sft.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 10.3 |  |\n| [GGUF](https://huggingface.co/mradermacher/commandr-35b-cpt-sft-i1-GGUF/resolve/main/commandr-35b-cpt-sft.i1-IQ2_XS.gguf) | i1-IQ2_XS | 11.2 |  |\n| [GGUF](https://huggingface.co/mradermacher/commandr-35b-cpt-sft-i1-GGUF/resolve/main/commandr-35b-cpt-sft.i1-IQ2_S.gguf) | i1-IQ2_S | 11.9 |  |\n| [GGUF](https://huggingface.co/mradermacher/commandr-35b-cpt-sft-i1-GGUF/resolve/main/commandr-35b-cpt-sft.i1-IQ2_M.gguf) | i1-IQ2_M | 12.8 |  |\n| [GGUF](https://huggingface.co/mradermacher/commandr-35b-cpt-sft-i1-GGUF/resolve/main/commandr-35b-cpt-sft.i1-Q2_K_S.gguf) | i1-Q2_K_S | 12.8 | very low quality |\n| [GGUF](https://huggingface.co/mradermacher/commandr-35b-cpt-sft-i1-GGUF/resolve/main/commandr-35b-cpt-sft.i1-Q2_K.gguf) | i1-Q2_K | 13.9 | IQ3_XXS probably better |\n| [GGUF](https://huggingface.co/mradermacher/commandr-35b-cpt-sft-i1-GGUF/resolve/main/commandr-35b-cpt-sft.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 13.9 | lower quality |\n| [GGUF](https://huggingface.co/mradermacher/commandr-35b-cpt-sft-i1-GGUF/resolve/main/commandr-35b-cpt-sft.i1-IQ3_XS.gguf) | i1-IQ3_XS | 15.2 |  |\n| [GGUF](https://huggingface.co/mradermacher/commandr-35b-cpt-sft-i1-GGUF/resolve/main/commandr-35b-cpt-sft.i1-IQ3_S.gguf) | i1-IQ3_S | 16.0 | beats Q3_K* |\n| [GGUF](https://huggingface.co/mradermacher/commandr-35b-cpt-sft-i1-GGUF/resolve/main/commandr-35b-cpt-sft.i1-Q3_K_S.gguf) | i1-Q3_K_S | 16.0 | IQ3_XS probably better |\n| [GGUF](https://huggingface.co/mradermacher/commandr-35b-cpt-sft-i1-GGUF/resolve/main/commandr-35b-cpt-sft.i1-IQ3_M.gguf) | i1-IQ3_M | 16.8 |  |\n| [GGUF](https://huggingface.co/mradermacher/commandr-35b-cpt-sft-i1-GGUF/resolve/main/commandr-35b-cpt-sft.i1-Q3_K_M.gguf) | i1-Q3_K_M | 17.7 | IQ3_S probably better |\n| [GGUF](https://huggingface.co/mradermacher/commandr-35b-cpt-sft-i1-GGUF/resolve/main/commandr-35b-cpt-sft.i1-Q3_K_L.gguf) | i1-Q3_K_L | 19.2 | IQ3_M probably better |\n| [GGUF](https://huggingface.co/mradermacher/commandr-35b-cpt-sft-i1-GGUF/resolve/main/commandr-35b-cpt-sft.i1-IQ4_XS.gguf) | i1-IQ4_XS | 19.3 |  |\n| [GGUF](https://huggingface.co/mradermacher/commandr-35b-cpt-sft-i1-GGUF/resolve/main/commandr-35b-cpt-sft.i1-Q4_0.gguf) | i1-Q4_0 | 20.4 | fast, low quality |\n| [GGUF](https://huggingface.co/mradermacher/commandr-35b-cpt-sft-i1-GGUF/resolve/main/commandr-35b-cpt-sft.i1-Q4_K_S.gguf) | i1-Q4_K_S | 20.5 | optimal size/speed/quality |\n| [GGUF](https://huggingface.co/mradermacher/commandr-35b-cpt-sft-i1-GGUF/resolve/main/commandr-35b-cpt-sft.i1-Q4_K_M.gguf) | i1-Q4_K_M | 21.6 | fast, recommended |\n| [GGUF](https://huggingface.co/mradermacher/commandr-35b-cpt-sft-i1-GGUF/resolve/main/commandr-35b-cpt-sft.i1-Q4_1.gguf) | i1-Q4_1 | 22.4 |  |\n| [GGUF](https://huggingface.co/mradermacher/commandr-35b-cpt-sft-i1-GGUF/resolve/main/commandr-35b-cpt-sft.i1-Q5_K_S.gguf) | i1-Q5_K_S | 24.4 |  |\n| [GGUF](https://huggingface.co/mradermacher/commandr-35b-cpt-sft-i1-GGUF/resolve/main/commandr-35b-cpt-sft.i1-Q5_K_M.gguf) | i1-Q5_K_M | 25.1 |  |\n| [GGUF](https://huggingface.co/mradermacher/commandr-35b-cpt-sft-i1-GGUF/resolve/main/commandr-35b-cpt-sft.i1-Q6_K.gguf) | i1-Q6_K | 28.8 | 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",
    "text-generation",
    "causal-lm",
    "two-stage-training",
    "continual-pretraining",
    "supervised-fine-tuning",
    "synthetic-qa",
    "lora",
    "axolotl",
    "deepspeed",
    "commandr",
    "cohere",
    "eu-hpc",
    "en",
    "dataset:arxiv",
    "dataset:gov",
    "dataset:news",
    "dataset:wikipedia",
    "dataset:axolotl_deduplicated_synthetic_qa",
    "base_model:ubitech-edg/commandr-35b-cpt-sft",
    "base_model:adapter:ubitech-edg/commandr-35b-cpt-sft",
    "license:apache-2.0",
    "endpoints_compatible",
    "region:us",
    "imatrix",
    "conversational"
  ],
  "likes": 0,
  "downloads": 305,
  "gated": false,
  "private": false,
  "last_modified": "2025-12-08T04:21:48.000Z",
  "created_at": "2025-10-20T21:20:53.000Z",
  "pipeline_tag": "text-generation",
  "library_name": "transformers"
}

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