mradermacher/llama-xlam-2-70b-fc-r-gguf overview

{
  "metadata": {},
  "card_data": {
    "base_model": "Salesforce/Llama-xLAM-2-70b-fc-r",
    "datasets": [
      "Salesforce/APIGen-MT-5k",
      "Salesforce/xlam-function-calling-60k"
    ],
    "language": [
      "en"
    ],
    "library_name": "transformers",
    "license": "cc-by-nc-4.0",
    "mradermacher": {
      "readme_rev": 1
    },
    "quantized_by": "mradermacher",
    "tags": [
      "function-calling",
      "LLM Agent",
      "tool-use",
      "llama",
      "qwen",
      "pytorch",
      "LLaMA-factory"
    ],
    "frontmatter": {
      "base_model": "Salesforce/Llama-xLAM-2-70b-fc-r",
      "datasets": [
        "Salesforce/APIGen-MT-5k",
        "Salesforce/xlam-function-calling-60k"
      ],
      "language": [
        "en"
      ],
      "library_name": "transformers",
      "license": "cc-by-nc-4.0",
      "mradermacher": [],
      "quantized_by": "mradermacher",
      "tags": [
        "function-calling",
        "LLM Agent",
        "tool-use",
        "llama",
        "qwen",
        "pytorch",
        "LLaMA-factory"
      ]
    },
    "hero_image_url": "https://www.nethype.de/huggingface_embed/quantpplgraph.png",
    "summary": "## About         static quants of https://huggingface.co/Salesforce/Llama-xLAM-2-70b-fc-r  ***For a convenient overview and download list, visit our model page for this model.*** weighted/imatrix quants are available at https://huggingface.co/mradermacher/Llama-xLAM-2-70b-fc-r-i1-GGUF",
    "quick_links": [],
    "benchmark_table_html": "",
    "readme_markdown": "---\nbase_model: Salesforce/Llama-xLAM-2-70b-fc-r\ndatasets:\n- Salesforce/APIGen-MT-5k\n- Salesforce/xlam-function-calling-60k\nlanguage:\n- en\nlibrary_name: transformers\nlicense: cc-by-nc-4.0\nmradermacher:\n  readme_rev: 1\nquantized_by: mradermacher\ntags:\n- function-calling\n- LLM Agent\n- tool-use\n- llama\n- qwen\n- pytorch\n- LLaMA-factory\n---\n## About\n\n<!-- ### quantize_version: 2 -->\n<!-- ### output_tensor_quantised: 1 -->\n<!-- ### convert_type: hf -->\n<!-- ### vocab_type:  -->\n<!-- ### tags:  -->\n<!-- ### quants:  x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->\n<!-- ### quants_skip:  -->\n<!-- ### skip_mmproj:  -->\nstatic quants of https://huggingface.co/Salesforce/Llama-xLAM-2-70b-fc-r\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#Llama-xLAM-2-70b-fc-r-GGUF).***\n\nweighted/imatrix quants are available at https://huggingface.co/mradermacher/Llama-xLAM-2-70b-fc-r-i1-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/Llama-xLAM-2-70b-fc-r-GGUF/resolve/main/Llama-xLAM-2-70b-fc-r.Q2_K.gguf) | Q2_K | 26.5 |  |\n| [GGUF](https://huggingface.co/mradermacher/Llama-xLAM-2-70b-fc-r-GGUF/resolve/main/Llama-xLAM-2-70b-fc-r.Q3_K_S.gguf) | Q3_K_S | 31.0 |  |\n| [GGUF](https://huggingface.co/mradermacher/Llama-xLAM-2-70b-fc-r-GGUF/resolve/main/Llama-xLAM-2-70b-fc-r.Q3_K_M.gguf) | Q3_K_M | 34.4 | lower quality |\n| [GGUF](https://huggingface.co/mradermacher/Llama-xLAM-2-70b-fc-r-GGUF/resolve/main/Llama-xLAM-2-70b-fc-r.Q3_K_L.gguf) | Q3_K_L | 37.2 |  |\n| [GGUF](https://huggingface.co/mradermacher/Llama-xLAM-2-70b-fc-r-GGUF/resolve/main/Llama-xLAM-2-70b-fc-r.IQ4_XS.gguf) | IQ4_XS | 38.4 |  |\n| [GGUF](https://huggingface.co/mradermacher/Llama-xLAM-2-70b-fc-r-GGUF/resolve/main/Llama-xLAM-2-70b-fc-r.Q4_K_S.gguf) | Q4_K_S | 40.4 | fast, recommended |\n| [GGUF](https://huggingface.co/mradermacher/Llama-xLAM-2-70b-fc-r-GGUF/resolve/main/Llama-xLAM-2-70b-fc-r.Q4_K_M.gguf) | Q4_K_M | 42.6 | fast, recommended |\n| [GGUF](https://huggingface.co/mradermacher/Llama-xLAM-2-70b-fc-r-GGUF/resolve/main/Llama-xLAM-2-70b-fc-r.Q5_K_S.gguf) | Q5_K_S | 48.8 |  |\n| [GGUF](https://huggingface.co/mradermacher/Llama-xLAM-2-70b-fc-r-GGUF/resolve/main/Llama-xLAM-2-70b-fc-r.Q5_K_M.gguf) | Q5_K_M | 50.0 |  |\n| [GGUF](https://huggingface.co/mradermacher/Llama-xLAM-2-70b-fc-r-GGUF/resolve/main/Llama-xLAM-2-70b-fc-r.Q6_K.gguf) | Q6_K | 58.0 | very good quality |\n| [GGUF](https://huggingface.co/mradermacher/Llama-xLAM-2-70b-fc-r-GGUF/resolve/main/Llama-xLAM-2-70b-fc-r.Q8_0.gguf) | Q8_0 | 75.1 | fast, best quality |\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.\n\n<!-- end -->\n",
    "related_quantizations": []
  },
  "tags": [
    "transformers",
    "gguf",
    "function-calling",
    "LLM Agent",
    "tool-use",
    "llama",
    "qwen",
    "pytorch",
    "LLaMA-factory",
    "en",
    "dataset:Salesforce/APIGen-MT-5k",
    "dataset:Salesforce/xlam-function-calling-60k",
    "base_model:Salesforce/Llama-xLAM-2-70b-fc-r",
    "base_model:quantized:Salesforce/Llama-xLAM-2-70b-fc-r",
    "license:cc-by-nc-4.0",
    "endpoints_compatible",
    "region:us",
    "conversational"
  ],
  "likes": 0,
  "downloads": 163,
  "gated": false,
  "private": false,
  "last_modified": "2026-01-09T01:08:10.000Z",
  "created_at": "2026-01-08T17:51:09.000Z",
  "pipeline_tag": "",
  "library_name": "transformers"
}

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