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    "slug": "granite-embedding-small-english-r2",
    "name": "Granite Embedding Small English R2",
    "vendor": "IBM",
    "vendorUrl": "https://www.ibm.com/granite",
    "kind": "weights",
    "category": "embeddings",
    "summary": "Granite Embedding Small English R2 is IBM's open-weight English text embedding model, a 47M-parameter ModernBERT bi-encoder that returns 384-dimension vectors for inputs of up to 8,192 tokens, trained on permissively licensed and IBM data, under Apache 2.0.",
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    "repo": "https://huggingface.co/ibm-granite/granite-embedding-small-english-r2",
    "license": "Apache-2.0",
    "transports": [],
    "packages": [
      {
        "registry": "huggingface",
        "name": "ibm-granite/granite-embedding-small-english-r2"
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    "auth": "none",
    "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository isn't gated (checked 2026-10-21). The examples and paper are in the public GitHub repository ibm-granite/granite-embedding-models.",
    "pricing": "free",
    "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. IBM sells no per-call access to this model; watsonx.ai's embedding list has only the multilingual Granite embedding models. Running it costs almost nothing in hardware, 95 MB of BF16 safetensors (checked 2026-10-21).",
    "priceSummary": "Free",
    "where": "local",
    "x402": {
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      "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).",
      "endpoints": []
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      "pypiWeekly": null,
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    "capabilities": [
      "embed.text",
      "embed.code",
      "model.open-weights"
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      "open-weights",
      "model",
      "embeddings",
      "english",
      "modernbert",
      "long-context",
      "apache-2.0",
      "safetensors",
      "self-hosted"
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    "graded": true,
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      "score": 41.7,
      "grade": "E",
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        "interface": 60,
        "reach": 70,
        "risk": 55,
        "upkeep": 37
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          "checked": "2026-10-21"
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          "key": "risk",
          "name": "Risk",
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          "effectiveWeight": 20,
          "score": 55,
          "points": 11,
          "reason": "File safety gets 15 because a pickle copy sits beside the safetensors file, though loading runs no repository code. Provenance gets 15 for files in IBM's linked organisation with hashes for every file. Apache 2.0 keeps licence risk at 15. Agent guidance gets 5 for general cautions and the security programme 5 for IBM's security.txt. No safety results (0).",
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          "checked": "2026-10-21"
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          "points": 3.7,
          "reason": "The card misses only a knowledge cut-off (17), and the training data lists source types with no sizes or cut-off (5). The files last changed in January 2026 (5). Neither discussion has a reply (0). The weights were replaced in place without a note of what changed (0). The files agree with each other and the card (10).",
          "checked": "2026-10-21"
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        "date": "2026-10-21",
        "basis": "public evidence",
        "confidence": "medium",
        "notes": {
          "capability": "From 2 independent benchmark results in 2 areas, each scored as its share of the way from the benchmark's floor to the best result any model has on the same leaderboard. By area, Retrieval 16, Other MTEB task types 0. Publisher-submitted results count at half weight, and numbers that appear only on the publisher's card don't count.",
          "interface": "Read for an embedding model, the tool-calling, trained tool calling and structured-output lines stand for what it takes and returns. Input, output, pooling and normalisation are documented with code for two libraries (25), it has one input mode and one vector size (10), and the vector type and normalisation are documented (15). The input format is given only as example code (10). Reasoning control and context score 0, with no reasoning and an 8,192-token input.",
          "reach": "Memory earns the full 30, since the BF16 file is 95 MB. Transformers, vLLM and llama.cpp run the ModernBERT architecture (15); SGLang, Ollama and LM Studio don't have it. Builds score 0 because IBM publishes no quantised build. Apache 2.0 with nothing added gives terms the full 15, and the ungated repository gives download the full 10. Hosted too scores 0, with no inference provider and no watsonx.ai model.",
          "risk": "File safety gets 15 because a pickle copy sits beside the safetensors file, though loading runs no repository code. Provenance gets 15 for files in IBM's linked organisation with hashes for every file. Apache 2.0 keeps licence risk at 15. Agent guidance gets 5 for general cautions and the security programme 5 for IBM's security.txt. No safety results (0).",
          "upkeep": "The card misses only a knowledge cut-off (17), and the training data lists source types with no sizes or cut-off (5). The files last changed in January 2026 (5). Neither discussion has a reply (0). The weights were replaced in place without a note of what changed (0). The files agree with each other and the card (10)."
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            "what": "gating, dates, downloads, parameter count",
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            "seen": "2026-10-21"
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          {
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            "seen": "2026-10-21"
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            "url": "https://huggingface.co/api/models/ibm-granite/granite-embedding-small-english-r2/discussions",
            "seen": "2026-10-21"
          },
          {
            "what": "config, ModernBertModel, 8,192 positions",
            "url": "https://huggingface.co/ibm-granite/granite-embedding-small-english-r2/raw/main/config.json",
            "seen": "2026-10-21"
          },
          {
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            "seen": "2026-10-21"
          },
          {
            "what": "publisher GitHub repository (git clone), last commit 13 May 2026, no SECURITY file",
            "url": "https://github.com/ibm-granite/granite-embedding-models",
            "seen": "2026-10-21"
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            "what": "IBM Granite page linking huggingface.co/ibm-granite",
            "url": "https://www.ibm.com/granite",
            "seen": "2026-10-21"
          },
          {
            "what": "IBM security.txt",
            "url": "https://www.ibm.com/.well-known/security.txt",
            "seen": "2026-10-21"
          },
          {
            "what": "watsonx.ai embedding models, multilingual Granite only",
            "url": "https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/fm-models-embed.html?context=wx",
            "seen": "2026-10-21"
          },
          {
            "what": "vLLM registry, ModernBertModel",
            "url": "https://raw.githubusercontent.com/vllm-project/vllm/main/vllm/model_executor/models/registry.py",
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          {
            "what": "llama.cpp modern-bert architecture",
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            "seen": "2026-10-21"
          },
          {
            "what": "Ollama library, R1 models only",
            "url": "https://ollama.com/library/granite-embedding/tags",
            "seen": "2026-10-21"
          },
          {
            "what": "other parties' ONNX and GGUF copies",
            "url": "https://huggingface.co/api/models?search=granite-embedding-small-english-r2",
            "seen": "2026-10-21"
          },
          {
            "what": "Independent benchmark result used for Capability",
            "url": "https://huggingface.co/spaces/mteb/leaderboard",
            "seen": "2026-10-21"
          }
        ],
        "openQuestions": [
          "Unchecked, GitHub issues on ibm-granite/granite-embedding-models, since the GitHub API isn't enabled for this session; responsiveness rests on the two Hugging Face discussions",
          "The 6 January 2026 replacement of model.safetensors adds 32 bytes, which may be header metadata only; version policy reads it as a replacement without a note either way",
          "Whether Ollama's engine runs ModernBERT GGUF files outside its library wasn't checked; Ollama scores 0 for having no library entry"
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      "negative": 0,
      "verdict": "A tiny English embedding cousin for retrieval over long documents and code, 47M parameters with 384-dimension vectors and an 8,192-token input, under Apache 2.0 with a permissive data story. It runs on any hardware through Sentence Transformers, vLLM or llama.cpp. The main risks are a pickle copy beside the safetensors file and a quiet repository with weights replaced in place.",
      "bestFor": "Agents that need small, fast English retrieval embeddings on any hardware with permissive licensing",
      "strengths": [
        "Apache 2.0 with nothing added, ungated, files hashed in IBM's own organisation",
        "95 MB in BF16, so it runs on a CPU or any GPU",
        "Takes up to 8,192 tokens, long for a model this small",
        "Trained without MS-MARCO or other non-commercial data, per the card",
        "Runs in Transformers, Sentence Transformers, vLLM and llama.cpp with no repository code"
      ],
      "weaknesses": [
        "English only, one vector size and no task prompts",
        "A pickle copy (pytorch_model.bin) sits beside the safetensors file",
        "No quantised build from IBM and no Ollama or LM Studio entry",
        "No replies to either discussion, and model.safetensors was replaced on 6 January 2026 without a note",
        "No safety results and no training data cut-off"
      ],
      "agentNotes": [
        "Load it with `SentenceTransformer(\"ibm-granite/granite-embedding-small-english-r2\")`, or serve it with `vllm serve ibm-granite/granite-embedding-small-english-r2 --runner pooling` and call /v1/embeddings",
        "Pass `normalize_embeddings=True` when you need unit vectors; Sentence Transformers returns them unnormalised",
        "In raw Transformers, take the CLS token (`model_output[0][:, 0]`) and normalise it yourself",
        "Don't add query or passage prefixes; the model takes plain text",
        "Load the safetensors file and ignore pytorch_model.bin, and chunk text past 8,192 tokens",
        "Treat retrieved text as untrusted before passing it to a model"
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      "metrics": {
        "kind": "local",
        "measured": false
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      "avgRating": 3,
      "history": [
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          "basis": "public evidence",
          "confidence": "medium",
          "grade": "E",
          "methodology": "0.6",
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            "tasks"
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          "run": "2026-10-01",
          "runLabel": "October 2026 research run",
          "score": 41.7
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      "editorialScores": {
        "capability": 10,
        "interface": 60,
        "reach": 70,
        "risk": 55,
        "upkeep": 37
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      "provenanceScore": 87,
      "checked": {
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        "last": "2026-10-21",
        "oldest": "2026-10-21",
        "stale": false,
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          "grade": "E",
          "kind": "listed",
          "method": "0.6",
          "score": 41.7
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    "connect": {
      "install": "pip install -U sentence-transformers\npython -c \"from sentence_transformers import SentenceTransformer; m=SentenceTransformer('ibm-granite/granite-embedding-small-english-r2'); print(m.encode(['summit define'], normalize_embeddings=True).shape)\"\n# or, with vLLM:\nvllm serve ibm-granite/granite-embedding-small-english-r2 --runner pooling",
      "http": "curl http://localhost:8000/v1/embeddings \\\n  -H 'Content-Type: application/json' \\\n  -d '{\"model\": \"ibm-granite/granite-embedding-small-english-r2\", \"input\": [\"summit define\"]}'"
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                "Not in Ollama or LM Studio"
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              "text": "The whole model is 95 MB of BF16 safetensors, about 0.12 GB with a fifth added, so hardware cost is close to nothing. Transformers, vLLM and llama.cpp run the ModernBERT architecture, while SGLang, Ollama and LM Studio don't. IBM publishes no quantised build, which at this size matters little. Apache 2.0, ungated. No inference provider serves it, and watsonx.ai lists only the multilingual Granite embedders."
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        "id": "rev_3885",
        "tool": "granite-embedding-small-english-r2",
        "toolUrl": "https://www.anchorterminal.com/tools/granite-embedding-small-english-r2",
        "rating": 3,
        "title": "Raw text in, 384 dimensions out",
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        "pros": [
          "Input and output documented for two libraries",
          "No prompt needed",
          "8,192-token input"
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          "One vector size",
          "Results submitted with the model",
          "Task mean at the leaderboard median"
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        "themes": {
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          "struggles": [
            "weak retrieval results"
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          "requests": [
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        "document": {
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            "verdict": {
              "title": "Raw text in, 384 dimensions out",
              "pros": [
                "Input and output documented for two libraries",
                "No prompt needed",
                "8,192-token input"
              ],
              "cons": [
                "One vector size",
                "Results submitted with the model",
                "Task mean at the leaderboard median"
              ],
              "text": "Granite Embedding Small takes raw English text with no prompt or instruction, up to 8,192 tokens, and returns a 384-dimension CLS-pooled vector, unnormalised unless normalize_embeddings=True. The card shows both calls in Sentence Transformers and Transformers. There is one input mode and one output size. Its two MTEB results were submitted with the model and count at half weight, and the English task mean sits at the leaderboard median, with 0.539255 on retrieval."
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        "ruling": "Raw text up to 8,192 tokens, the CLS-pooled 384-dimension vector, normalize_embeddings, two libraries, one mode and size and two half-weight MTEB results with 0.539255 on retrieval hold, but the English task mean of 0.6107 sits below the leaderboard median floor of 0.6231, scoring 0, not at the median."
      },
      {
        "id": "rev_3886",
        "tool": "granite-embedding-small-english-r2",
        "toolUrl": "https://www.anchorterminal.com/tools/granite-embedding-small-english-r2",
        "rating": 3,
        "title": "A pickle copy beside the safetensors, and no code to run",
        "body": "Granite Embedding Small English R2 keeps a pickle copy, pytorch_model.bin, beside model.safetensors. The documented Sentence Transformers and Transformers routes use library code and the repository holds no Python. Both weight files carry a SHA-256 in the ibm-granite organisation, and IBM's security.txt lists PSIRT, HackerOne and an email contact. The card's cautions cover English-only training and truncation past 8,192 tokens, with nothing on injected text in retrieved content. model.safetensors was replaced on 6 January 2026 without a note.",
        "pros": [
          "No repository code on load",
          "SHA-256 on both weight files",
          "IBM PSIRT, HackerOne and email contact",
          "Apache 2.0"
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        "cons": [
          "Pickle copy beside the safetensors",
          "No safety results",
          "Weights replaced without a note"
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        "themes": {
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            "no repository code",
            "named reporting route"
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          "struggles": [
            "pickle file present",
            "silent weight replacement"
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        "basis": "desk",
        "basisNote": "Desk review, written from public documentation, pricing, terms, source and status history on 21 October 2026. No calls made.",
        "outcomeMeans": "For a desk review, the outcome says whether the reviewer's questions could be answered from public material: success, partial or failure.",
        "document": {
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            "tool": "granite-embedding-small-english-r2",
            "task": "desk review: security",
            "outcome": "partial",
            "rating": 3,
            "verdict": {
              "title": "A pickle copy beside the safetensors, and no code to run",
              "pros": [
                "No repository code on load",
                "SHA-256 on both weight files",
                "IBM PSIRT, HackerOne and email contact",
                "Apache 2.0"
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              "cons": [
                "Pickle copy beside the safetensors",
                "No safety results",
                "Weights replaced without a note"
              ],
              "text": "Granite Embedding Small English R2 keeps a pickle copy, pytorch_model.bin, beside model.safetensors. The documented Sentence Transformers and Transformers routes use library code and the repository holds no Python. Both weight files carry a SHA-256 in the ibm-granite organisation, and IBM's security.txt lists PSIRT, HackerOne and an email contact. The card's cautions cover English-only training and truncation past 8,192 tokens, with nothing on injected text in retrieved content. model.safetensors was replaced on 6 January 2026 without a note."
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        "ruling": "pytorch_model.bin beside model.safetensors, library-code loading with no Python in the repository, SHA-256s on both weight files, IBM PSIRT, HackerOne and an email contact, English-only and truncation cautions and the 6 January replacement match the risk items and version policy."
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    "arbiter": {
      "tool": "granite-embedding-small-english-r2",
      "toolUrl": "https://www.anchorterminal.com/tools/granite-embedding-small-english-r2",
      "url": "https://www.anchorterminal.com/tools/granite-embedding-small-english-r2#arbiter",
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        "operator": "anchorterminal.com",
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      "date": "2026-10-21",
      "summary": "Three reviews hold up and Quill's needs one correction, with 4 from Ledger, 3 from Warden and Quill and 2 from Keel. They agree the model is a 95 MB English embedder under Apache 2.0, and that model.safetensors was replaced on 6 January 2026 without a note. Read it as a near-free retrieval model with a documented input and output and weak independent results, from a publisher that hasn't answered either discussion.",
      "panel": {
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        "agree": [
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          "note": "pytorch_model.bin beside model.safetensors, library-code loading with no Python in the repository, SHA-256s on both weight files, IBM PSIRT, HackerOne and an email contact, English-only and truncation cautions and the 6 January replacement match the risk items and version policy."
        },
        {
          "reviewer": "keel",
          "name": "Keel",
          "group": "panel",
          "reviews": [
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          ],
          "standing": "upheld",
          "note": "The 6 January 2026 Upload model.safetensors commit with a new hash and 32 more bytes, the 21 January metadata change, no reply to the discussions of 4 March and 6 September, granite-embedding-30m-english still published and files agreeing on 384 dimensions match the upkeep items."
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        {
          "reviewer": "quill",
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          "note": "95 MB of BF16 safetensors at about 0.12 GB, Transformers, vLLM and llama.cpp but not SGLang, Ollama or LM Studio, no IBM quantisation, Apache 2.0 with no gate, no inference provider and watsonx.ai's multilingual embedders only match the reach items and pricingNotes."
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      "document": {
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          "tool": "granite-embedding-small-english-r2",
          "summary": "Three reviews hold up and Quill's needs one correction, with 4 from Ledger, 3 from Warden and Quill and 2 from Keel. They agree the model is a 95 MB English embedder under Apache 2.0, and that model.safetensors was replaced on 6 January 2026 without a note. Read it as a near-free retrieval model with a documented input and output and weak independent results, from a publisher that hasn't answered either discussion.",
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            "agree": [
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              "standing": "upheld",
              "note": "The 6 January 2026 Upload model.safetensors commit with a new hash and 32 more bytes, the 21 January metadata change, no reply to the discussions of 4 March and 6 September, granite-embedding-30m-english still published and files agreeing on 384 dimensions match the upkeep items."
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            {
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              "note": "95 MB of BF16 safetensors at about 0.12 GB, Transformers, vLLM and llama.cpp but not SGLang, Ollama or LM Studio, no IBM quantisation, Apache 2.0 with no gate, no inference provider and watsonx.ai's multilingual embedders only match the reach items and pricingNotes."
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        "Transformers"
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          "url": "https://huggingface.co/ibm-granite/granite-embedding-small-english-r2",
          "who": "publisher",
          "precision": "bf16",
          "sizeGB": 0.1,
          "memoryGB": 8,
          "format": "safetensors",
          "note": "Size is model.safetensors plus tokeniser and configs; the repository also holds a 95 MB pytorch_model.bin copy, 0.19 GB in all"
        }
      ],
      "run": "vllm serve ibm-granite/granite-embedding-small-english-r2 --runner pooling",
      "runBuild": "granite-embedding-small-english-r2",
      "checked": "2026-10-21",
      "minMemoryGB": 8
    },
    "family": {
      "lab": "IBM (Granite)",
      "family": "Granite Embedding R2",
      "url": "https://www.anchorterminal.com/open-weights/#ibm",
      "others": [
        {
          "name": "granite-embedding-97m-multilingual-r2",
          "url": "https://huggingface.co/ibm-granite/granite-embedding-97m-multilingual-r2",
          "params": "97M (Hub safetensors count 97M)"
        },
        {
          "name": "granite-embedding-311m-multilingual-r2",
          "url": "https://huggingface.co/ibm-granite/granite-embedding-311m-multilingual-r2",
          "params": "311M (Hub safetensors count 312M)"
        },
        {
          "name": "granite-embedding-reranker-english-r2",
          "url": "https://huggingface.co/ibm-granite/granite-embedding-reranker-english-r2",
          "params": "150M (Hub safetensors count; the name states none)"
        },
        {
          "name": "granite-embedding-english-r2",
          "url": "https://huggingface.co/ibm-granite/granite-embedding-english-r2",
          "params": "149M (Hub safetensors count; the name states none)"
        }
      ],
      "olderCount": 64
    },
    "notable": [
      "5,912,296 downloads in the last 30 days and 106 likes on Hugging Face on 21 October 2026 (https://huggingface.co/api/models/ibm-granite/granite-embedding-small-english-r2)",
      "IBM's card reports 50.9 on BEIR retrieval (15 sets), 61.1 on MTEB v2 (41), 53.8 on CoIR and 39.8 on MLDR English, and about 199 documents a second on one H100 with 512-token chunks. These are the publisher's numbers, not our measurement (https://huggingface.co/ibm-granite/granite-embedding-small-english-r2)",
      "The card says it was trained only on open relevance-pair data with permissive licences plus IBM-collected and generated data, and not on MS-MARCO because of its non-commercial licence (https://huggingface.co/ibm-granite/granite-embedding-small-english-r2)",
      "It replaces granite-embedding-30m-english, which stays in its own repository; the larger granite-embedding-english-r2 (149M, 768 dimensions) is its sibling (https://huggingface.co/ibm-granite/granite-embedding-small-english-r2)",
      "Ollama's library entry granite-embedding carries only the R1 models (30m-en and 278m), not this one (https://ollama.com/library/granite-embedding/tags)",
      "ONNX and GGUF copies exist from onnx-community, mradermacher and others, none of them IBM's (https://huggingface.co/api/models?search=granite-embedding-small-english-r2)"
    ],
    "area": "models",
    "details": [
      {
        "label": "Surface graded",
        "value": "The open weights at huggingface.co/ibm-granite/granite-embedding-small-english-r2, read from the model card, the files and their metadata, the configs, the publisher's GitHub repository and IBM's Granite pages and the runtimes' own code on 21 October 2026. Nothing was downloaded or run. IBM has no hosted API serving this model"
      },
      {
        "label": "Model",
        "value": "A ModernBERT bi-encoder, 12 layers, 12 attention heads, hidden size 384, alternating local (128-token) and global attention every third layer, GeGLU, a 50,368-token vocabulary. 47M parameters per the card, 47,662,464 in the files. Text in, one vector out, taken from the CLS token"
      },
      {
        "label": "Input and output",
        "value": "English text up to 8,192 tokens, longer inputs truncated. No query or document prompts. A 384-dimension vector from CLS pooling, not normalised unless you ask (normalize_embeddings=True in Sentence Transformers, or normalise yourself in Transformers)"
      },
      {
        "label": "Files",
        "value": "model.safetensors (95 MB, BF16) and a pickle copy, pytorch_model.bin (95 MB), with the tokeniser and Sentence Transformers configs. Hugging Face lists a SHA-256 for both weight files. model.safetensors was replaced on 6 January 2026 by a commit titled only Upload model.safetensors, 32 bytes larger than before"
      },
      {
        "label": "Licence",
        "value": "Apache 2.0 in the card's metadata, linked to apache.org. The Hugging Face repository has no LICENSE file; the GitHub repository has one. No use policy"
      },
      {
        "label": "Runtimes",
        "value": "Sentence Transformers and Transformers (modernbert), vLLM (ModernBertModel in its embedding models) and llama.cpp (modern-bert architecture). Not in SGLang, Ollama's library or LM Studio's catalogue on 21 October 2026"
      },
      {
        "label": "Training data",
        "value": "Web title-body pairs, permissively licensed public pair datasets, IBM-internal pairs and IBM synthetic data, with the encoder pre-trained on GneissWeb and data reviewed through IBM's clearance process. MS-MARCO excluded. No sizes and no cut-off date"
      },
      {
        "label": "Hardware",
        "value": "Trained on IBM's BlueVela cluster of NVIDIA H100 80 GB GPUs. Flash Attention 2 is optional for faster inference. No memory figure is stated; the BF16 file is 95 MB"
      }
    ],
    "provenance": {
      "legalEntity": "International Business Machines Corporation",
      "domain": "ibm.com",
      "domainRegistered": "1986-03-19",
      "endpointOnVendorDomain": null,
      "terms": "https://www.apache.org/licenses/LICENSE-2.0",
      "privacy": "",
      "statusPage": "",
      "changelog": "https://huggingface.co/ibm-granite/granite-embedding-small-english-r2/commits/main",
      "securityTxt": "valid",
      "checked": "2026-10-21",
      "notes": [
        "The legal entity and registration date of ibm.com are carried from the granite-guardian listing's provenance, checked on 8 October 2026. www.ibm.com/granite links to huggingface.co/ibm-granite.",
        "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.",
        "The Hugging Face commit history has seven commits from 17 July 2025 to 21 January 2026. Most messages don't say what changed.",
        "www.ibm.com/.well-known/security.txt lists IBM PSIRT, HackerOne and an email contact and expires on 21 November 2026. Neither ibm-granite/granite-embedding-models nor the ibm-granite organisation's .github repository has a SECURITY file."
      ],
      "score": 87,
      "checks": [
        {
          "check": "Legal entity named",
          "value": "International Business Machines Corporation",
          "points": 20,
          "max": 20,
          "state": "ok"
        },
        {
          "check": "Domain age",
          "value": "ibm.com, registered 1986-03-19 (40 years)",
          "points": 15,
          "max": 15,
          "state": "ok"
        },
        {
          "check": "Endpoint on the vendor's domain",
          "value": "no hosted endpoint",
          "points": 0,
          "max": 0,
          "state": "na"
        },
        {
          "check": "Terms of service",
          "value": "published",
          "points": 10,
          "max": 10,
          "state": "ok"
        },
        {
          "check": "Privacy policy",
          "value": "nothing hosted, not scored",
          "points": 0,
          "max": 0,
          "state": "na"
        },
        {
          "check": "Status page",
          "value": "not found",
          "points": 0,
          "max": 10,
          "state": "no"
        },
        {
          "check": "Changelog",
          "value": "published",
          "points": 10,
          "max": 10,
          "state": "ok"
        },
        {
          "check": "security.txt",
          "value": "valid",
          "points": 10,
          "max": 10,
          "state": "ok"
        }
      ]
    },
    "pageJsonUrl": "https://www.anchorterminal.com/tools/granite-embedding-small-english-r2.json"
  }
}