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This PR body was truncated due to platform limits.

This PR contains the following updates:

Package Change Age Confidence Type Update Pending
huggingface-hub 1.19.0 → 1.29.0 age confidence project.dependencies minor 1.30.0
locust 2.44.1 → 2.46.4 age confidence dependency-groups minor 2.46.5
mypy (changelog) 2.1.0 → 2.3.1 age confidence dependency-groups minor
numpy (changelog) 2.4.6 → 2.5.2 age confidence project.dependencies minor 2.5.3
onnxruntime 1.26.0 → 1.29.0 age confidence project.optional-dependencies minor
onnxruntime-gpu 1.26.0 → 1.29.0 age confidence project.optional-dependencies minor
onnxruntime-migraphx 1.25.0 → 1.27.1 age confidence project.optional-dependencies minor
opencv-python-headless 4.13.0.92 → 4.14.0.94 age confidence project.dependencies minor
orjson (changelog) 3.11.9 → 3.12.0 age confidence project.dependencies minor
pydantic (changelog) 2.13.4 → 2.13.5 age confidence project.dependencies patch
pytest (changelog) 9.0.3 → 9.1.1 age confidence dependency-groups minor
python (source) f82c964 → 9d2e555 stage digest
python (source) e2d3af7 → 528257d stage digest
python (source) 20ec607 → 35d3a4a stage digest
rapidocr (changelog) 3.8.1 → 3.9.2 age confidence project.dependencies minor
types-pyyaml (changelog) 6.0.12.20260518 → 6.0.12.20260815 age confidence dependency-groups patch 6.0.12.20260906
types-requests (changelog) 2.33.0.20260518 → 2.33.0.20260712 age confidence dependency-groups patch 2.33.0.20260906

Warning

Some dependencies could not be looked up. Check the Dependency Dashboard for more information.

Release Notes

huggingface/huggingface_hub (huggingface-hub)

v1.29.0: [v1.29.0] Fix Xet downloads rate limits, bucket visibility updates, and security fixes

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⚡ Xet downloads no longer make one API call per file

Since v1.19.0, downloading a repository with hf_xet eagerly requested a xet read token for every single file, because each download group was built without cached connection info. On repos with many files this quickly added up — a 77k-file repo made ~1,500 Hub API calls per minute — and eventually hit the rate limiter, leaving snapshot_download appearing stalled for minutes before failing with a 429 Too Many Requests. This release restores the Python-side connection info cache so the endpoint and token are reused across download groups, skipping the eager per-file token request entirely. Large downloads are both faster and far less likely to be rate-limited.

🪣 Change bucket visibility after creation

Bucket visibility used to be a create-time-only setting: once a bucket existed, there was no way to flip it between private and public. You can now update it with the new HfApi.update_bucket_settings() method (also exported as update_bucket_settings) or from the CLI with hf buckets settings, which takes either --private or --public.

>>> from huggingface_hub import update_bucket_settings

# Make a bucket private
>>> update_bucket_settings("username/my-bucket", private=True)

# Make it public again
>>> update_bucket_settings("username/my-bucket", private=False)
# Make a bucket private
>>> hf buckets settings username/my-bucket --private
✓ Bucket settings updated
  bucket_id: username/my-bucket
  private: True

# Make it public again
>>> hf buckets settings username/my-bucket --public

📚 Documentation: Buckets guide

🔒 Security fixes

Two security-relevant fixes land in this release. First, the path-traversal guard introduced earlier for CVE-2026-15717 is now extended to hf buckets sync / sync_bucket(): when downloading from a bucket, server-supplied file keys were joined straight onto the local destination without validation, so a malicious or compromised bucket could return anchored or traversing keys (/etc/cron.d/evil, ../../../../etc/passwd, Windows drive-absolute or UNC paths) that escape the chosen directory and write arbitrary files. Remote paths are now validated the same way as in the original fix. Second, load_state_dict_from_file could fall back to pickle deserialization for a shard named exactly .safetensors: Path.suffix returns an empty string for extension-only filenames, so a file that passed sharded-checkpoint validation (which uses str.endswith) was still routed to torch.load(weights_only=False). A shared _is_safetensors() helper now guarantees both code paths use the same matching semantics, so an index-declared "safetensors" checkpoint can never be loaded with pickle.

🤖 Inference

🖥️ CLI

🔧 Other QoL Improvements

  • [MCP] Set upper bound mcp<2.0 for optional extra dependency huggingface_hub[mcp] by @​Wauplin in #​4735
  • Remove leftover deprecated labels/tags from docstrings and signature by @​Wauplin in #​4745

🐛 Bug and typo fixes

📖 Documentation

  • Docs: mark Sandboxes as experimental and drop the "HF token never enters the sandbox" claim by @​moon-bot-app[bot] in #​4734

🏗️ Internal

v1.28.0: [v1.28.0] Hardware discovery and managed engine images for Inference Endpoints and more

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🔎 Discover deployable hardware with hf endpoints hardware

Deploying an Inference Endpoint requires five hardware flags (--vendor, --region, --accelerator, --instance-type, --instance-size) whose valid values depend on each other, and until now there was no way to learn them from the CLI. The new hf endpoints hardware command lists the valid combinations along with the price per replica per hour and your namespace's accelerator quota, filtered by default to the hardware you can deploy on right now. The same data is available in the SDK via list_inference_endpoints_hardware(), which flattens the API response into InferenceEndpointHardware objects you can filter programmatically.

>>> hf endpoints hardware --vendor aws --region eu-west-1
VENDOR REGION    ACCELERATOR INSTANCE_TYPE INSTANCE_SIZE MEMORY_GB GPU_MEMORY_GB PRICE_PER_HOUR QUOTA STATUS
------ --------- ----------- ------------- ------------- --------- ------------- -------------- ----- ---------
aws    eu-west-1 cpu         intel-spr     x1                  2.0                        0.033 0/60  available
aws    eu-west-1 cpu         intel-spr     x2                  4.0                        0.067 0/60  available
aws    eu-west-1 gpu         nvidia-a10g   x1                 30.0            24            1.0 0/16  available
aws    eu-west-1 gpu         nvidia-t4     x1                 15.0            16            0.5 1/30  available

🚀 Managed engine images and multi-accelerator parallelism for Inference Endpoints

custom_image now accepts the engine-specific container types supported by the API: key the dictionary with the engine name (vLLM, sGLang, tgi, tei, llamacpp, hfServe, ...) instead of leaving it flat, and each engine takes the usual container fields plus its own tuning options. Any dict without a top-level url is forwarded to the API untouched, so engines added to the API later will work without upgrading huggingface_hub, and update_inference_endpoint now handles the same payload shapes as create_inference_endpoint. On the CLI, hf endpoints deploy and hf endpoints update gain --engine, --tensor-parallel-size and --data-parallel-size, and update also accepts --custom-image, --health-route and --port. This matters because vLLM and SGLang default to a single accelerator while an endpoint is allocated every accelerator of its instance — the API now rejects that misconfiguration, and these flags are how you set things right.

$ hf endpoints deploy gpt-oss-120b-vllm --repo openai/gpt-oss-120b --framework custom \
    --accelerator gpu --instance-size x8 --instance-type nvidia-h200 --region us-east-1 --vendor aws \
    --engine vllm --custom-image vllm/vllm-openai:v0.23.0 --tensor-parallel-size 8

# Retune a running endpoint
$ hf endpoints update gpt-oss-120b-vllm --tensor-parallel-size 4 --data-parallel-size 2

💔 Breaking change: huggingface_hub.constants.INFERENCE_ENDPOINT_IMAGE_KEYS is removed. It was never exported at the package root nor documented, but code reading it directly will now get an AttributeError.

🤖 Inference

🖥️ CLI

🐛 Bug and typo fixes

📖 Documentation

🏗️ Internal

v1.27.0: [v1.27.0] Automatic hf-cli skill install, engine flags for Inference Endpoints & more

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🤖 The hf-cli skill installs itself and stays in sync

The hf-cli skill teaches AI agents how to use the hf CLI, but until now you had to know it existed and install it by hand. The standalone installers (bash and PowerShell) now install it globally by default, pass --exclude-skill / -ExcludeSkill to skip — and hf update refreshes it afterwards, without ever bringing it back if you opted out or removed it. Any hf command also hints, at most once a day, when the skill is missing or was generated by another hf version. The hint is purely local, never installs anything on its own, and is silenced by HF_HUB_DISABLE_UPDATE_CHECK=1.

# The installer sets up the skill for you...
>>> curl -LsSf https://hf.co/cli/install.sh | bash -s
[INFO] Installing the hf-cli skill for AI agents...
Installed 'hf-cli' to central location: ~/.agents/skills/hf-cli
[INFO] Pass --exclude-skill to skip it.

# ...or skip it entirely
>>> curl -LsSf https://hf.co/cli/install.sh | bash -s -- --exclude-skill
[INFO] Skipping the hf-cli skill (--exclude-skill)
  • [CLI] Install & refresh the hf-cli skill (installer, update, hints) by @​Wauplin in #​4608

⚙️ Engine flags for Inference Endpoints, at deploy time and after

--container-command / --container-args no longer require --custom-image. That gate was conservative CLI scoping, not an API constraint: model.command and model.args are top-level fields of the endpoint payload and apply to managed engine images too, which is how the vLLM engine docs recommend passing engine flags. They can now also be changed after deploy — hf endpoints update gained both flags, and HfApi.update_inference_endpoint / InferenceEndpoint.update the matching container_command / container_args parameters. Values replace rather than append: pass an empty string to reset to the image default, or omit the flag to leave it untouched. --health-route and --port still require --custom-image, since they only exist on the custom image payload.

# Engine flags at deploy time, no custom image required anymore
>>> hf endpoints deploy my-endpoint --repo gpt2 --framework pytorch \
      --accelerator cpu --instance-size x2 --instance-type intel-icl \
      --region us-east-1 --vendor aws \
      --container-args "--max-model-len 8192"

# Change engine flags on an existing endpoint (previously UI / raw API only)
>>> hf endpoints update my-endpoint --container-args "--enable-auto-tool-choice --tool-call-parser lfm2"

# Reset to the image defaults
>>> hf endpoints update my-endpoint --container-args ""
  • [Inference Endpoints] Allow container command/args without custom image + support them in update by @​gary149 in #​4628

🚀 Baseten joins the inference providers

Baseten is now supported for the conversational task. It serves an OpenAI-compatible chat completions API, so there are no provider-specific quirks: target it with provider="baseten" and your own key, or let auto-routing pick it for any model already mapped on the Hub.

>>> from huggingface_hub import InferenceClient

>>> client = InferenceClient(provider="baseten", api_key="<BASETEN_API_KEY>")
>>> out = client.chat_completion(
...     model="zai-org/GLM-5.2",
...     messages=[{"role": "user", "content": "Hello!"}],
... )
>>> print(out.choices[0].message.content)

🔧 Other QoL Improvements

  • [HfApi] Add region to ExpandSpaceProperty_T by @​hanouticelina in #​4641 — the Hub added region as an expandable property for Spaces; it is now accepted by space_info / list_spaces and typed on SpaceInfo as Literal["us", "eu"] | None — docs
  • [Xet] Bump minimum hf-xet to 1.5.2 by @​hanouticelina in #​4640 — 1.5.2 fixes possible hangs on poor networks, but the floor was still 1.5.1, so fresh installs could land on the buggy version
  • Serialize model first in conversational payloads by @​moon-bot-app[bot] in #​4618 — routers can now resolve the provider from a small prefix instead of buffering a whole payload of base64 images. The resulting dicts are equal, only the key order changes

🐛 Bug and typo fixes

  • [HfFileSystem] Fix bucket prefix collisions by @​lewtun in #​4630 — the Buckets API applies prefix lexically, so in a bucket holding logs_existing/ but no logs/, exists(".../logs/new.txt") raised KeyError and ls(".../logs") could return the unrelated sibling. Listings are now filtered on path-component boundaries
  • [Cache] Stop deleting snapshot files twice when deleting a revision by @​hanouticelina in #​4639 — snapshot files that aren't symlinks into blobs/ (Windows copies, or files created by the user inside a snapshot dir) were deleted a second time as blobs, logging a FileNotFoundError traceback each. Reported freed size is unchanged, and per-path delete lines moved to debug
  • [Download] Don't retain caller frames when falling back to cache after a failed HEAD call by @​Wauplin in #​4614 — the swallowed HEAD exception kept its traceback, and with it the whole caller stack, alive until the next gc.collect(); vLLM had to monkey-patch this. Also fixes a v1.0 regression where http_backoff retried on an httpx client already closed by a previous ConnectError

🏗️ Internal

v1.26.1

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v1.26.0: [v1.26.0] Resolve revisions only once, security hardening, and resource groups for Jobs & Collections

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📌 Pin a revision once with resolve_revision

Libraries that download many files one by one (config, weights, tokenizer, processor, ...) had to resolve revision="main" into a commit hash on every call — costing one HTTP request per file and risking two calls landing on two different commits if the repo is updated in between. The new HfApi.resolve_revision resolves the revision once and returns a ResolvedRevision: a str subclass whose value stays the user-facing revision (so error messages keep saying "main") while its .resolved attribute holds the commit hash. Download helpers (hf_hub_download, snapshot_download, get_cached_repo_tree) detect it and use the commit hash directly, guaranteeing every file comes from the same commit. The mapping is also written to the refs/ folder of the cache, so later runs in offline mode transparently fall back to the cached value.

>>> from huggingface_hub import resolve_revision, hf_hub_download
>>> revision = resolve_revision("openai-community/gpt2")
>>> revision
ResolvedRevision(initial=None, resolved='607a30d783dfa663caf39e06633721c8d4cfcd7e')
>>> revision == "main"  # readable error messages
True
>>> config = hf_hub_download("openai-community/gpt2", "config.json", revision=revision)
>>> weights = hf_hub_download("openai-community/gpt2", "model.safetensors", revision=revision)

📚 Documentation: Manage the cache — Pin a revision (advanced)

🔒 Security hardening for downloads and sandboxes

This release ships two security fixes. First, downloading or uploading to a --local-dir now rejects absolute, drive-relative, root-relative, UNC and ..-traversal filenames on all platforms, interpreting each name under both POSIX and Windows rules (refs CVE-2026-15717). Previously only a Windows-only ..\ check existed, so a malicious repo could write files outside the target directory on Windows clients — and even leak a NetNTLMv2 hash via UNC paths. Legitimate repo filenames never contain such segments, so real downloads are unaffected; note that exotic names like folder/..\..\..\file, previously tolerated on Linux, are now rejected everywhere. Second, Sandbox.create no longer injects your HF token into the job environment to download the sbx-server binary: the bucket is public, so the bootstrap now downloads it anonymously and no HF credential ever lands in the sandbox unless you explicitly opt in with forward_hf_token=True.

🗂️ Resource groups for Jobs and Collections

Organization resource groups are now supported across the client. For collections, create_collection accepts an optional resource_group_id, and the new update_collection_resource_group method wraps the dedicated Hub endpoint to assign a collection to a resource group afterwards (passing None removes it). For Jobs, run_job, run_uv_job and create_scheduled_job accept a resource_group_id parameter, mirrored by a --resource-group-id option on the hf jobs run, hf jobs uv run and hf jobs scheduled run commands. Beyond access control within an organization, resource groups are also used for cost attribution and per-group spending limits.

hf jobs run --resource-group-id <group-id> python:3.12 python train.py

📚 Documentation: Collections reference, CLI reference

📊 Job names, front and center in the CLI

Job names are now much easier to work with from the terminal. hf jobs ls (and hf jobs scheduled ls) display a dedicated NAME column, and a new --name filter acts as a shortcut for --label name=NAME. The name is also surfaced as a top-level field in hf jobs inspect and in command results, instead of only living inside labels — where it remains for compatibility.

$ hf jobs ls -a --name training-v2
JOB_ID      NAME         IMAGE/SPACE COMMAND      CREATED      STATUS    RUNTIME
----------- ------------ ----------- ------------ ------------ --------- -------
6a60b190... training-v2  python:3.12 python -c... 2026-07-2... COMPLETED 0s

📚 Documentation: Run and manage Jobs

📖 Documentation

  • Added Odia (or) translation of the index, installation and quick-start pages by @​indrajeetapache in #​4454
  • [Docs] Fix Odia (or) docs build, register it in CI, rename tm -> ta by @​Wauplin in #​4589 — note: Tamil docs URLs move from /tm/ to /ta/ (correct ISO 639-1 code)
  • [Docs] Fix HF_XET_SHARD_CACHE_SIZE_LIMIT default (4GB → 16GB) by @​rajatarya in #​4593 — docs
  • docs(jobs): mention cost attribution/spending-limit in resource_group_id docs by @​Pierrci in #​4597 — docs

🐛 Bug and typo fixes

  • [Download] Reject redacted Xet hashes from tree cache by @​seanses in #​4595 — fixes xet downloads failing with Unable to parse string as hex hash value on gated repos without content access
  • [Safetensors] Fix truncated header on 100kb boundary by @​Wauplin in #​4603 — headers of 99994–100000 bytes were silently truncated and failed with header is not json-encoded string
  • Reject token=False in create_inference_endpoint_from_catalog instead of silently ignoring it by @​ckarnell in #​4605
  • [CLI] Don't crash when stdout can't encode non-ASCII output by @​Wauplin in #​4610 — fixes UnicodeEncodeError on Windows when output is redirected or piped
  • [Core] Fix tilde expansion in CommitOperationAdd by @​Saniyagupte in #​4612 — paths like ~/model.bin no longer raise FileNotFoundError on upload

🏗️ Internal

v1.25.1

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v1.25.0: [v1.25.0] Auto-named Jobs, smarter progress bars & cache diagnostics

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🏷️ Auto-named Jobs on creation

Jobs now get an automatic name when you don't provide one explicitly, derived from the Docker image (or UV script) plus a short hash of the command line. This means reruns of the same command share a consistent name, while different commands get distinct names — making it much easier to find and group related jobs in the UI or CLI. Names follow the server-side character rules: :, / and . in image tags are replaced with - so python:3.12 foo --truc becomes python-3-12-7c6db949. Explicit --name still takes precedence.

>>> hf jobs run --detach python:3.12 foo --truc
  id: 6a60b85c13e6ef894d54b949
Hint: Job auto-named 'python-3-12-7c6db949'. Pass `--name` or run `hf jobs labels <id> --name` to rename.

📚 Documentation: Jobs guide, CLI guide

🔧 Other QoL Improvements

📖 Documentation

🐛 Bug and typo fixes

🏗️ Internal

v1.24.0: [v1.24.0] Name your Jobs! (and download fixes)

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📊 Name your Jobs!

Jobs on the Hub now support an optional --name flag on the CLI and a name parameter on the Python API (run_job, run_uv_job, create_scheduled_job, create_scheduled_uv_job). Names are stored as the name label and make Jobs easier to find and identify in the UI. You can also name an existing Job using hf jobs labels <job_id> --name my-job. Names are optional and do not need to be unique.

# Create a named Job
hf jobs run --name training-v2 python:3.12 python train.py

# Name an existing Job
hf jobs labels <job_id> --name training-v2

# Named scheduled Job
hf jobs scheduled run @hourly --name hourly-task python:3.12 python -c 'print("This runs every hour!")'

📚 Documentation: CLI guide, Jobs guide

📖 Documentation

The README has been completely refreshed to put the hf CLI first. The standalone installer (curl/PowerShell) and a terminal quick start — covering auth login, models ls, download, upload, and jobs run — now appear before the Python library section. A new For AI agents section introduces hf skills add for Codex, Cursor, OpenCode, Claude Code, and other AI tools. The Python content remains intact under the renamed Use the Python library heading, with refreshed example models and a corrected tagline ("The official CLI and Python client for the Hugging Face Hub").

🐛 Bug and typo fixes

🏗️ Internal

v1.23.0: [v1.23.0] Space templates, CLI extension updates & smoother Xet downloads

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🚀 Create Spaces from templates

You can now seed a new Space from one of the official Hub templates (JupyterLab, a Gradio chatbot, a Streamlit app, etc.) instead of starting from an empty repo. List what's available with the new list_space_templates() API or the hf spaces templates CLI command, then pass a template's repo_id (or its short name) to create_repo(..., space_template=...) or hf repos create --type space --template. The Space SDK is inferred from the template, and templates recommended as private (like JupyterLab) are created privately by default unless you explicitly choose a visibility.

# List available templates
$ hf spaces templates
NAME        REPO_ID                             SDK     PREFERRED_PRIVATE
----------- ----------------------------------- ------- -----------------
Streamlit   streamlit/streamlit-template-space  docker
JupyterLab  SpacesExamples/jupyterlab           docker  ✔

# Create a Space from a template
$ hf repos create my-jupyterlab --type space --template jupyterlab
✓ Repo created
  repo_id: Wauplin/my-jupyterlab
  url: https://huggingface.co/spaces/Wauplin/my-jupyterlab
>>> from huggingface_hub import create_repo
>>> create_repo("my-jupyterlab", repo_type="space", space_template="jupyterlab")

🔌 Update installed CLI extensions

A new hf extensions update command brings your installed CLI extensions to their latest published version on GitHub. Pass a name to update a single extension, or run it with no argument to check every installed extension and update the ones that are behind. Updates are applied in place — Python extensions reuse their existing venv and binary extensions are overwritten — so a failed update no longer leaves the extension uninstalled, and extensions that are already up to date are simply skipped.

# Update a single extension (accepts <name>, hf-<name> or OWNER/hf-<name>)
hf extensions update hf-claude

# Check every installed extension and update the outdated ones
hf extensions update

📶 Smoother Xet download progress with dual bars

Xet downloads now show two progress bars so you can tell a transfer is alive even on a slow connection. The transfer bar advances as bytes arrive over the network, while the reconstruction bar tracks real progress as buffered chunks are written to disk — previously the single bar could sit at 0% for a long time while data was actually arriving. The dual bars are wired into single-file downloads (hf_hub_download), snapshot_download (where parallel file downloads feed the repo-level transfer and reconstruction bars), the hf download CLI, and bucket downloads.

big.bin: downloading bytes:   |  52.4MB     1.2MB/s
big.bin: reconstructing file: |  52.4MB / 105MB     800kB/s

🤖 Always up-to-date, offline hf-cli skill

hf skills add and hf skills update now generate the built-in hf-cli skill locally from your installed CLI version instead of downloading it from the marketplace bucket. The installed SKILL.md is therefore always in sync with the CLI you're running, and installing or updating the hf-cli skill works fully offline — the marketplace is only contacted when you install another managed skill. As defense-in-depth against path traversal, skill names coming from the marketplace payload are now validated before any filesystem work.

# Works fully offline, and always matches your installed CLI version
$ HF_HUB_OFFLINE=1 hf skills add --dest ./skills
Installed 'hf-cli' to ./skills/hf-cli

🖥️ CLI

🔧 Other QoL Improvements

📖 Documentation

🐛 Bug and typo fixes

  • [Utils] Treat backslashes as path separators in filter_repo_objects by @​Wauplin in #​4506 — fixes a v1.22 regression where snapshot_download silently skipped files on Windows
  • [CLI] Coerce enum member defaults to their value when building click params by @​dhruv7477 in #​4494 — fixes a v1.22 regression where commands using an enum option default failed at runtime
  • Update install.ps1 by @​ufocia in #​4501 — fixes false install verification failures on Windows

🏗️ Internal

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