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Lucain Pouget PRO
Wauplin
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Cohere on Hugging Face Inference Providers 🔥
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Article
Cohere on Hugging Face Inference Providers 🔥
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124
update-novita-logo
1
#475 opened 19 days ago
by
viktor-hu
updated
a
dataset
13 days ago
Update thumbnail in repo metadata (not done in this PR)
2
#1 opened 13 days ago
by
Wauplin
updated
a
dataset
19 days ago
updated
a
dataset
21 days ago
replied to
their
post
28 days ago
Please open a proper issue in the corresponding Github repo (is it a huggingface_hub issue? a gradio one? etc.) with more details and especially a reproducible example of the issue you are having. If it is a HTTP request issue, try to isolate it first. (the more details you can provider, the better it is for the maintainers)
replied to
their
post
28 days ago
My spaces use models that, for some reason, state that they are unavailable to be used by any of the inference providers, despite them working fine two months ago
The Huggingface's serverless Inference API wasn't a production-ready service. It was only meant to easily experiment and prototype ML apps. We started rolling out inference providers to tackle this topic and make things more future-proof. Regarding the specific problem you have, it's hard to help without knowing the specific models you were using back then. The most likely is that these models have been removed from HF Inference API infra as we are now focusing on making fewer but high-impact models available.
posted
an
update
28 days ago
Post
2100
‼️ 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:
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.
- 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!
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!
-
- 6 replies
liked
a
Space
about 1 month ago
Add library tag for better metrics/ downstream usage
1
1
#9 opened about 1 month ago
by
reach-vb