Bump huggingface-hub from 0.30.2 to 0.31.4

[dependabot]

Bumps huggingface-hub from 0.30.2 to 0.31.4.

Release notes

Sourced from huggingface-hub's releases.

[v0.31.4]: strict dataclasses, support DTensor saving & some bug fixes

This release includes some new features and bug fixes:

• New strict decorators for runtime dataclass validation with custom and type-based checks. by @​Wauplin in huggingface/huggingface_hub#2895.
• Added DTensor support to _get_unique_id / get_torch_storage_size helpers, enabling transformers to use save_pretrained with DTensor. by @​S1ro1 in huggingface/huggingface_hub#3042.
• Some bug fixes: huggingface/huggingface_hub#3080 & huggingface/huggingface_hub#3076.

Full Changelog: huggingface/huggingface_hub@v0.31.2...v0.31.4

[v0.31.2] Hot-fix: make hf-xet optional again and bump the min version of the package

Patch release to make hf-xet optional. More context in #3079 and #3078.

Full Changelog: huggingface/huggingface_hub@v0.31.1...v0.31.2

[v0.31.0] LoRAs with Inference Providers, auto mode for provider selection, embeddings models and more

🧑‍🎨 Introducing LoRAs with fal.ai and Replicate providers

We're introducing blazingly fast LoRA inference powered by fal.ai and Replicate through Hugging Face Inference Providers! You can use any compatible LoRA available on the Hugging Face Hub and get generations at lightning fast speed ⚡

from huggingface_hub import InferenceClient
client = InferenceClient(provider="fal-ai") # or provider="replicate"
output is a PIL.Image object
image = client.text_to_image(
"a boy and a girl looking out of a window with a cat perched on the window sill. There is a bicycle parked in front of them and a plant with flowers to the right side of the image. The wall behind them is visible in the background.",
model="openfree/flux-chatgpt-ghibli-lora",
)

• [Inference Providers] LoRAs with Replicate by @​hanouticelina in #3054
• [Inference Providers] Support for LoRAs with fal by @​hanouticelina in #3005

⚙️ auto mode for provider selection

You can now automatically select a provider for a model using auto mode — it will pick the first available provider based on your preferred order set in https://hf.co/settings/inference-providers.

from huggingface_hub import InferenceClient
will select the first provider available for the model, sorted by your order.
client = InferenceClient(provider="auto")
completion = client.chat.completions.create(
model="Qwen/Qwen3-235B-A22B",
messages=[
{
</tr></table>

... (truncated)

Commits

• 2f4ee4b Release: v0.31.4
• 86ab658 Release: v0.31.3
• 4c28652 New @strict decorator for dataclass validation (#2895)
• f59601f Feat: support DTensor when saving (#3042)
• 7fde08d [Inference Endpoints] fix inference endpoint creation with custom image (#3076)
• 68475be [HfFileSystem] Fix end-of-file read() (#3080)
• 2e5dc96 Release: v0.31.2
• d1de0aa [Internal] make hf-xet optional (#3079)
• 080b047 bump hf-xet min version (#3078)
• 13a3991 Release: v0.31.1
• Additional commits viewable in compare view

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Superseded by #381.