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The HF Models resource mounts a Hugging Face Model repo at some prefix such as /m/. You can inspect configs, tokenizers, and READMEs without downloading the weights, weights stream only when you actually cat them. For credential setup, see HF Models Setup.

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Config

import os

from mirage import MountMode, Workspace
from mirage.resource.hf_models import HfModelsConfig, HfModelsResource

config = HfModelsConfig(
    repo_id=os.environ["HF_MODEL_REPO"],   # "namespace/model-name"
    token=os.environ.get("HF_TOKEN"),
    # Optional:
    # endpoint="https://huggingface.co",
    # revision="main",
)
resource = HfModelsResource(config)
ws = Workspace({"/m": resource}, mode=MountMode.READ)

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Filesystem Layout

Maps model repo files (config, tokenizer, weights, etc.) to virtual paths. For example, sapientinc/HRM-Text-1B mounted at /m/ exposes: 
/m/
  README.md
  config.json
  tokenizer.json
  tokenizer_config.json
  model.safetensors        ← never downloaded unless you cat it

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Example

import asyncio

from mirage import MountMode, Workspace
from mirage.resource.hf_models import HfModelsConfig, HfModelsResource

config = HfModelsConfig(repo_id="sapientinc/HRM-Text-1B")
resource = HfModelsResource(config)


async def main() -> None:
    ws = Workspace({"/m": resource}, mode=MountMode.READ)

    # ls is cheap: one HTTP list call
    r = await ws.execute("ls -lh /m/")
    print(await r.stdout_str())

    # Read the config (small, fast)
    r = await ws.execute("cat /m/config.json")
    print(await r.stdout_str())

    # Stat the weights without downloading them
    r = await ws.execute("stat /m/model.safetensors")
    print(await r.stdout_str())


if __name__ == "__main__":
    asyncio.run(main())

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Shell Commands

Same set as HF Buckets.

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Use Cases

  • Model card inspection: Read configs, tokenizers, READMEs without pulling multi-GB weight files
  • Compatibility checks: jq over config.json to verify architecture before committing to a download
  • Pinned revisions: Mount a specific commit/tag for reproducibility