Post
2035
Llama4 is out and Scout is already on the Dell Enterprise Hub to deploy on Dell systems 👉 dell.huggingface.co
Optimum-Intel
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optimum-intel's activity
jeffboudier
posted
an
update
5 days ago
jeffboudier
posted
an
update
8 days ago
Post
1453
Enterprise orgs now enable serverless Inference Providers for all members
- includes $2 free usage per org member (e.g. an Enterprise org with 1,000 members share $2,000 free credit each month)
- admins can set a monthly spend limit for the entire org
- works today with Together, fal, Novita, Cerebras and HF Inference.
Here's the doc to bill Inference Providers usage to your org: https://huggingface.co/docs/inference-providers/pricing#organization-billing
- includes $2 free usage per org member (e.g. an Enterprise org with 1,000 members share $2,000 free credit each month)
- admins can set a monthly spend limit for the entire org
- works today with Together, fal, Novita, Cerebras and HF Inference.
Here's the doc to bill Inference Providers usage to your org: https://huggingface.co/docs/inference-providers/pricing#organization-billing
- 2 replies
jeffboudier
posted
an
update
3 months ago
Post
741
NVIDIA just announced the Cosmos World Foundation Models, available on the Hub:
nvidia/cosmos-6751e884dc10e013a0a0d8e6
Cosmos is a family of pre-trained models purpose-built for generating physics-aware videos and world states to advance physical AI development.
The release includes Tokenizers nvidia/cosmos-tokenizer-672b93023add81b66a8ff8e6
Learn more in this great community article by @mingyuliutw and @PranjaliJoshi https://huggingface.co/blog/mingyuliutw/nvidia-cosmos
Cosmos is a family of pre-trained models purpose-built for generating physics-aware videos and world states to advance physical AI development.
The release includes Tokenizers nvidia/cosmos-tokenizer-672b93023add81b66a8ff8e6
Learn more in this great community article by @mingyuliutw and @PranjaliJoshi https://huggingface.co/blog/mingyuliutw/nvidia-cosmos
- 1 reply
IlyasMoutawwakil
updated
a
Space
4 months ago
jeffboudier
posted
an
update
5 months ago
Post
1109
New - add your bluesky account to your HF profile:
https://huggingface.co/settings/profile
Is the grass greener, the sky bluer? Will try and figure it out at https://bsky.app/profile/jeffboudier.bsky.social
By the way, HF people starter pack https://bsky.app/starter-pack/huggingface.bsky.social/3laz5x7naiz22
https://huggingface.co/settings/profile
Is the grass greener, the sky bluer? Will try and figure it out at https://bsky.app/profile/jeffboudier.bsky.social
By the way, HF people starter pack https://bsky.app/starter-pack/huggingface.bsky.social/3laz5x7naiz22
jeffboudier
posted
an
update
6 months ago
Post
1107
This week in Inference Endpoints - thx
@erikkaum
for the update!
👀 https://huggingface.co/blog/erikkaum/endpoints-changelog
👀 https://huggingface.co/blog/erikkaum/endpoints-changelog
- 1 reply
jeffboudier
posted
an
update
7 months ago
Post
470
Inference Endpoints got a bunch of cool updates yesterday, this is my top 3
jeffboudier
posted
an
update
7 months ago
Post
4084
Pro Tip - if you're a Firefox user, you can set up Hugging Chat as integrated AI Assistant, with contextual links to summarize or simplify any text - handy!
In this short video I show how to set it up
In this short video I show how to set it up
- 3 replies
IlyasMoutawwakil
updated
a
Space
10 months ago
IlyasMoutawwakil
updated
2
models
10 months ago
IlyasMoutawwakil
posted
an
update
10 months ago
Post
4085
Last week, Intel's new Xeon CPUs, Sapphire Rapids (SPR), landed on Inference Endpoints and I think they got the potential to reduce the cost of your RAG pipelines 💸
Why ? Because they come with Intel® AMX support, which is a set of instructions that support and accelerate BF16 and INT8 matrix multiplications on CPU ⚡
I went ahead and built a Space to showcase how to efficiently deploy embedding models on SPR for both Retrieving and Ranking documents, with Haystack compatible components: https://huggingface.co/spaces/optimum-intel/haystack-e2e
Here's how it works:
- Document Store: A FAISS document store containing the seven-wonders dataset, embedded, indexed and stored on the Space's persistent storage to avoid unnecessary re-computation of embeddings.
- Retriever: It embeds the query at runtime and retrieves from the dataset N documents that are most semantically similar to the query's embedding.
We use the small variant of the BGE family here because we want a model that's fast to run on the entire dataset and has a small embedding space for fast similarity search. Specifically we use an INT8 quantized bge-small-en-v1.5, deployed on an Intel Sapphire Rapids CPU instance.
- Ranker: It re-embeds the retrieved documents at runtime and re-ranks them based on semantic similarity to the query's embedding. We use the large variant of the BGE family here because it's optimized for accuracy allowing us to filter the most relevant k documents that we'll use in the LLM prompt. Specifically we use an INT8 quantized bge-large-en-v1.5, deployed on an Intel Sapphire Rapids CPU instance.
Space: https://huggingface.co/spaces/optimum-intel/haystack-e2e
Retriever IE: optimum-intel/fastrag-retriever
Ranker IE: optimum-intel/fastrag-ranker
Why ? Because they come with Intel® AMX support, which is a set of instructions that support and accelerate BF16 and INT8 matrix multiplications on CPU ⚡
I went ahead and built a Space to showcase how to efficiently deploy embedding models on SPR for both Retrieving and Ranking documents, with Haystack compatible components: https://huggingface.co/spaces/optimum-intel/haystack-e2e
Here's how it works:
- Document Store: A FAISS document store containing the seven-wonders dataset, embedded, indexed and stored on the Space's persistent storage to avoid unnecessary re-computation of embeddings.
- Retriever: It embeds the query at runtime and retrieves from the dataset N documents that are most semantically similar to the query's embedding.
We use the small variant of the BGE family here because we want a model that's fast to run on the entire dataset and has a small embedding space for fast similarity search. Specifically we use an INT8 quantized bge-small-en-v1.5, deployed on an Intel Sapphire Rapids CPU instance.
- Ranker: It re-embeds the retrieved documents at runtime and re-ranks them based on semantic similarity to the query's embedding. We use the large variant of the BGE family here because it's optimized for accuracy allowing us to filter the most relevant k documents that we'll use in the LLM prompt. Specifically we use an INT8 quantized bge-large-en-v1.5, deployed on an Intel Sapphire Rapids CPU instance.
Space: https://huggingface.co/spaces/optimum-intel/haystack-e2e
Retriever IE: optimum-intel/fastrag-retriever
Ranker IE: optimum-intel/fastrag-ranker
IlyasMoutawwakil
updated
a
model
10 months ago
jeffboudier
posted
an
update
11 months ago
Post
1713
TGI v2.0.2 is out!
- New models (idefics2, phi3)
- Cleaner VLM support in the openai layer
- Upgraded to pytorch 2.3.0
https://github.com/huggingface/text-generation-inference/releases/tag/v2.0.2
Kudos @Narsil @olivierdehaene @drbh and so many contributors!
- New models (idefics2, phi3)
- Cleaner VLM support in the openai layer
- Upgraded to pytorch 2.3.0
https://github.com/huggingface/text-generation-inference/releases/tag/v2.0.2
Kudos @Narsil @olivierdehaene @drbh and so many contributors!
jeffboudier
posted
an
update
about 1 year ago
Post
1895
These 15 open models are available for serverless inference on Cloudflare Workers AI, powered by GPUs distributed in 150 datacenters globally - 👏
@rita3ko
@mchenco
@jtkipp
@nkothariCF
@philschmid
Cloudflare/hf-curated-models-available-on-workers-ai-66036e7ad5064318b3e45db6
Cloudflare/hf-curated-models-available-on-workers-ai-66036e7ad5064318b3e45db6
echarlaix
authored
a
paper
almost 2 years ago