HF Models

uv add "mirage-ai[hf]"

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)

/m/
  README.md
  config.json
  tokenizer.json
  tokenizer_config.json
  model.safetensors        ← never downloaded unless you cat it

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())