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Official organization for the Hugging Face Accelerate library
Wauplin 
posted an update 25 days ago
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2916
Say hello to hf: a faster, friendlier Hugging Face CLI ✨

We are glad to announce a long-awaited quality-of-life improvement: the Hugging Face CLI has been officially renamed from huggingface-cli to hf!

So... why this change?

Typing huggingface-cli constantly gets old fast. More importantly, the CLI’s command structure became messy as new features were added over time (upload, download, cache management, repo management, etc.). Renaming the CLI is a chance to reorganize commands into a clearer, more consistent format.

We decided not to reinvent the wheel and instead follow a well-known CLI pattern: hf <resource> <action>. Isn't hf auth login easier to type and remember?

The full rationale, implementation details, and migration notes are in the blog post: https://huggingface.co/blog/hf-cli
marcsun13 
in hf-accelerate/accelerate_examples 3 months ago

add multi_xpu configuration

3
#5 opened 3 months ago by
faaany
marcsun13 
in hf-accelerate/model-memory-usage 3 months ago

Error accessing Llama4 models: 'does not have any library metadata on the Hub'

3
#39 opened 4 months ago by
asharma2024
marcsun13 
updated a Space 4 months ago
marcsun13 
in hf-accelerate/model-memory-usage 4 months ago

GPTQ group size effects vRAM usage

2
#18 opened almost 2 years ago by
sgjohnson

Determining Minimum GPU Memory and Input Text Length Calculation in Model Training

👍 1
2
#19 opened almost 2 years ago by
kobe8-24

microsoft/Florence-2-base raises error without any detail
#36 opened 11 months ago by
fcakyon

For Llama-2 model, I tried to get the memory usage, it says I do not have the access

1
#35 opened 12 months ago by
AiEvanTao

Improve name of the model detection to allow several URL usages

2
#28 opened over 1 year ago by
Pablohn26

meta-llama/Meta-Llama-3.1-8B-Instruct ....why not found this

➕ 6
#34 opened 12 months ago by
world-of-ai

Llama3 is not found using link and API token

👀 6
#31 opened about 1 year ago by
barnaszocs

Question about Gradient calculation
#30 opened over 1 year ago by
Infinity4B

How to Calculate GPU usage for re-training Microsoft/speechT5_tts ?

1
#27 opened over 1 year ago by
huggingfacerohit

Unable to Access Gated Model Repository databricks/dbrx-instruct

1
#26 opened over 1 year ago by
lambda1989

Data Wrong, THUDM/chatglm3-6b

1
#20 opened over 1 year ago by
YEKANGMING

Analyzing tool for inference
#13 opened almost 2 years ago by
cllatMTK

Dropdown menu of models

3
#2 opened almost 2 years ago by
Ouz-G

Plz fix it!!!
#33 opened about 1 year ago by
DonDonDon

This space is no longer working

➕ 11
3
#38 opened 6 months ago by
LuisVasquezBSC
Wauplin 
posted an update 5 months ago
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2292
‼️ huggingface_hub's v0.30.0 is out with our biggest update of the past two years!

Full release notes: https://github.com/huggingface/huggingface_hub/releases/tag/v0.30.0.

🚀 Ready. Xet. Go!

Xet is a groundbreaking new protocol for storing large objects in Git repositories, designed to replace Git LFS. Unlike LFS, which deduplicates files, Xet operates at the chunk level—making it a game-changer for AI builders collaborating on massive models and datasets. Our Python integration is powered by [xet-core](https://github.com/huggingface/xet-core), a Rust-based package that handles all the low-level details.

You can start using Xet today by installing the optional dependency:

pip install -U huggingface_hub[hf_xet]


With that, you can seamlessly download files from Xet-enabled repositories! And don’t worry—everything remains fully backward-compatible if you’re not ready to upgrade yet.

Blog post: https://huggingface.co/blog/xet-on-the-hub
Docs: https://huggingface.co/docs/hub/en/storage-backends#xet


⚡ Inference Providers

- We’re thrilled to introduce Cerebras and Cohere as official inference providers! This expansion strengthens the Hub as the go-to entry point for running inference on open-weight models.

- Novita is now our 3rd provider to support text-to-video task after Fal.ai and Replicate.

- Centralized billing: manage your budget and set team-wide spending limits for Inference Providers! Available to all Enterprise Hub organizations.

from huggingface_hub import InferenceClient
client = InferenceClient(provider="fal-ai", bill_to="my-cool-company")
image = client.text_to_image(
    "A majestic lion in a fantasy forest",
    model="black-forest-labs/FLUX.1-schnell",
)
image.save("lion.png")


- No more timeouts when generating videos, thanks to async calls. Available right now for Fal.ai, expecting more providers to leverage the same structure very soon!
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