Search AI and machine learning models on the Hugging Face Hub by name, author or organizat

[
  {
    "scheme": "exact",
    "network": "eip155:8453",
    "payTo": "0x72f62dE8b70d6CFa8Cc2dF6f21F243f289F3396c",
    "asset": "0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913",
    "amount": "50000",
    "maxTimeoutSeconds": 300
  }
]

{
  "bazaar": {
    "info": {
      "input": {
        "method": "GET",
        "pathParams": {
          "query": ":query"
        },
        "queryParams": {
          "format": "json"
        },
        "type": "http"
      },
      "output": {
        "example": {
          "match_count": 1,
          "matches": [
            {
              "author": "nvidia",
              "downloads": 128429,
              "record_id": 12345,
              "source": "HUGGINGFACE",
              "task": "image-segmentation",
              "title": "nvidia/segformer-b5-finetuned-cityscapes-1024-1024"
            }
          ],
          "result": "MATCHES_FOUND",
          "search_evidence": {
            "_about": "Proof of work, on EVERY result including NO_MATCH: the terms actually searched, how many records were in scope per source, how fresh they are, whether the scan completed or was truncated, and on an empty result the per-term record counts plus a known-good control query re-measured live in the same request. Numbers shown are illustrative.",
            "corpus": {
              "newest_record_ingested_utc": "2026-08-01T15:47:55Z",
              "records_in_scope": 1558599
            },
            "result_confidence": "matches_found",
            "scan": {
              "candidate_set_complete": true,
              "candidates_examined": 35,
              "matches_omitted": 25
            }
          }
        },
        "type": "json"
      }
    },
    "routeTemplate": "/x402/models/search/:query",
    "schema": {
      "$schema": "https://json-schema.org/draft/2020-12/schema",
      "properties": {
        "input": {
          "additionalProperties": false,
          "properties": {
            "method": {
              "enum": [
                "GET",
                "HEAD",
                "DELETE"
              ],
              "type": "string"
            },
            "pathParams": {
              "properties": {
                "query": {
                  "description": "Keyword(s): model name, author or org, task, library, or license. E.g. 'image segmentation' or 'nvidia'.",
                  "type": "string"
                }
              },
              "required": [
                "query"
              ],
              "type": "object"
            },
            "queryParams": {
              "properties": {},
              "type": "object"
            },
            "type": {
              "const": "http",
              "type": "string"
            }
          },
          "required": [
            "type",
            "method"
          ],
          "type": "object"
        },
        "output": {
          "properties": {
            "example": {
              "type": "object"
            },
            "type": {
              "type": "string"
            }
          },
          "required": [
            "type"
          ],
          "type": "object"
        }
      },
      "required": [
        "input"
      ],
      "type": "object"
    }
  }
}

curl "https://api.osf-master-server.com/x402/models/search/:query"
# -> 402 Payment Required, accepts[] lists how to pay
# retry with a PAYMENT-SIGNATURE (or PAYMENT header) once paid

const res = await fetch("https://api.osf-master-server.com/x402/models/search/:query");
if (res.status === 402) {
  const { accepts } = await res.json();
  // pay one of accepts[] via an x402 client, then retry with the payment header
}

import httpx
res = httpx.get("https://api.osf-master-server.com/x402/models/search/:query")
if res.status_code == 402:
    accepts = res.json()["accepts"]
    # pay one of accepts[] via an x402 client, then retry with the payment header