hugging-bay

A thin, typed Python client for the Hugging Bay API: open-model discovery, byte/SHA-256 verification, server-computed safe-to-run verdicts, provenance passports, and offline lockfile checks. It does not itself judge model safety — it surfaces the server's verdicts and verifies bytes against published hashes.

The client is intentionally minimal — one method per endpoint, no client-side magic. Verdicts, fits, and identities are computed by the server and returned as parsed JSON.

Install

Install from PyPI:

python -m pip install hugging-bay

Or from a source checkout of this repository:

python -m pip install ./packages/python-sdk

Requires Python 3.9+. The only runtime dependency is httpx. The package is build-ready (python -m build produces a wheel + sdist); the actual PyPI upload is an owner release step.

Usage

from hugging_bay import Client

client = Client()  # defaults to https://huggingbay.xyz
# Optional auth: Client(token="sk-...") sends Authorization: Bearer <token>.
# The token is never printed in repr()/str() and never logged.

0. Get an API key (self-serve, optional)

Public discovery — search, resolve, download-plan, and hosted downloads — needs no token. You only authenticate for account workflows (saves, collections, watchlists). Minting a reader token is self-serve, with no signup and no prior auth:

from hugging_bay import Client

issued = Client().create_reader_token(display_name="my app", purpose="serving models")
token = issued["apiKey"]["token"]      # shown once — store it securely
client = Client(token=token)
print(client.me()["role"])             # validate the token

The full walkthrough (curl, JS/TS, MCP) is the one-screen quickstart at https://huggingbay.xyz/quickstart. A 401/403 from any authorized endpoint returns next.href = /quickstart#get-a-key, so there is no dead-end.

1. Find a commercial-safe model for a rig

from hugging_bay import Client

client = Client()
result = client.find_model(
    task="coding",
    gpu="rtx4090-24",
    ctx=8192,
    device_family="nvidia",
    commercial=True,
    limit=3,
)
best = result["bestPick"]
print(best["repo"], "-> safe-to-run:", best["safety"]["verdict"])

2. Verify a source URL

from hugging_bay import Client, HuggingBayError

client = Client()
try:
    resolved = client.resolve("https://huggingface.co/meta-llama/Llama-3.1-8B")
    print("resolved artifact:", resolved.get("artifactId") or resolved)
except HuggingBayError as err:
    print("could not resolve:", err.status, err.error)

3. Pull plan, then verify the lock against local files

from hugging_bay import Client, verify_lock

client = Client()
artifact_id = "art_123"

# The hosted download plan (signed manifest + per-file hashes).
plan = client.download_plan(artifact_id)

# Stream one hosted file, verify its exact plan hash, and record the completed
# pull. This stops the Hugging Bay redirect, follows the short-lived signed GCS
# URL without forwarding the API bearer, and only returns after attestation.
file = next(row for row in plan["files"] if row["path"] == "model.gguf")
client.download_file(
    artifact_id,
    file["path"],
    "./models/art_123/model.gguf",
    sha256=file["sha256"],
    size_bytes=file["sizeBytes"],
)

# Recompute local hashes and compare to the bay.lock document — pure stdlib,
# no network.
lock = client.lock(artifact_id)
report = verify_lock(lock, root_dir="./models/art_123")
if report["ok"]:
    print(f"verified {report['checked']} files")
else:
    for mismatch in report["mismatches"]:
        print("BAD:", mismatch["path"], mismatch["reason"])

4. Diagnose a failing run

from hugging_bay import Client

client = Client()
diagnosis = client.doctor(
    log_text="llama_model_load: error loading model: CUDA out of memory",
    runtime="llama.cpp",
)
if diagnosis["topFix"]:
    print("top fix:", diagnosis["topFix"])
for alt in diagnosis.get("smallerHostedAlternatives", []):
    print("smaller option:", alt)

Discovery → execution with Bay Run

BayRunClient runs the mirrored weights live on Bay Run (run.huggingbay.xyz, a separate origin). Take the next_call a catalog answer returns and run it — the client mints the anonymous $0 demo key for you and tolerates the edge's plain-text 429 "Rate exceeded.":

from hugging_bay import Client, BayRunClient

runnable = Client().find_runnable(task="embeddings", limit=1)
# hugging-bay.find-runnable.v1: the handoff lives at the top-level `next_call`
# (or under `recommendation.execution`); there is no `candidates` list.
next_call = runnable.get("next_call") or runnable["recommendation"]["execution"]

with BayRunClient() as run:            # mints a $0 demo key on first call
    out = run.run_next_call(next_call, input="text to embed")
    print(out)

# Or call the OpenAI-compatible surface directly:
with BayRunClient() as run:
    run.embeddings("BAAI/bge-small-en-v1.5", "hello world")
    run.rerank("BAAI/bge-reranker-base", query="q", documents=["a", "b"])
    run.run_pin("pin_abc123", input="...")

API surface

Errors

Every Hugging Bay error is the same typed envelope on every surface: {error, code, status, message, requestId, cause?, next}. Any response with status >= 400 raises HuggingBayError, which surfaces those fields directly — .error/.code (machine code), .message, .status, .request_id, .next (the {action, href} recovery step), and the raw .body. On a 503 with a Retry-After header the client waits once and retries a single time.

resolve() follows status-based error handling: a well-formed reference with no catalog match raises status 404; malformed/unsupported input raises 400 (invalid_query). Bay Run errors raise BayRunError, which is tolerant of the edge's non-JSON bodies and exposes .retry_after.

License

MIT