Jeff Boudier
jeffboudier
AI & ML interests
Hugging Face!
Recent Activity
upvoted
a
collection
about 18 hours ago
DeepSeek-R1
liked
a Space
about 23 hours ago
smolagents/smolagents-leaderboard
upvoted
an
article
2 days ago
Welcome Gemma 3: Google's all new multimodal, multilingual, long context open LLM
Organizations
jeffboudier's activity

reacted to
BrigitteTousi's
post with π€
3 days ago

reacted to
mcpotato's
post with π€
8 days ago
Post
2383
Stoked to announce we've partnered with JFrog to continue improving safety on the Hub! πΈ
Their model scanner brings new scanning capabilities to the table, aimed at reducing alert fatigue.
More on that in our blog post: https://huggingface.co./blog/jfrog
Their model scanner brings new scanning capabilities to the table, aimed at reducing alert fatigue.
More on that in our blog post: https://huggingface.co./blog/jfrog

reacted to
clem's
post with π₯
10 days ago
Post
5872
Super happy to welcome Nvidia as our latest enterprise hub customer. They have almost 2,000 team members using Hugging Face, and close to 20,000 followers of their org. Can't wait to see what they'll open-source for all of us in the coming months!
Nvidia's org: https://huggingface.co./nvidia
Enterprise hub: https://huggingface.co./enterprise
Nvidia's org: https://huggingface.co./nvidia
Enterprise hub: https://huggingface.co./enterprise

reacted to
csabakecskemeti's
post with π€
18 days ago
Post
2755
Testing Training on AMD/ROCm the first time!
I've got my hands on an AMD Instinct MI100. It's about the same price used as a V100 but on paper has more TOPS (V100 14TOPS vs MI100 23TOPS) also the HBM has faster clock so the memory bandwidth is 1.2TB/s.
For quantized inference it's a beast (MI50 was also surprisingly fast)
For LORA training with this quick test I could not make the bnb config works so I'm running the FT on the fill size model.
Will share all the install, setup and setting I've learned in a blog post, together with the cooling shroud 3D design.
I've got my hands on an AMD Instinct MI100. It's about the same price used as a V100 but on paper has more TOPS (V100 14TOPS vs MI100 23TOPS) also the HBM has faster clock so the memory bandwidth is 1.2TB/s.
For quantized inference it's a beast (MI50 was also surprisingly fast)
For LORA training with this quick test I could not make the bnb config works so I'm running the FT on the fill size model.
Will share all the install, setup and setting I've learned in a blog post, together with the cooling shroud 3D design.

reacted to
fdaudens's
post with β€οΈ
18 days ago
Post
3287
π Just launched: A toolkit of 20 powerful AI tools that journalists can use right now - transcribe, analyze, create. 100% free & open-source.
Been testing all these tools myself and created a searchable collection of the most practical ones - from audio transcription to image generation to document analysis. No coding needed, no expensive subscriptions.
Some highlights I've tested personally:
- Private, on-device transcription with speaker ID in 100+ languages using Whisper
- Website scraping that just works - paste a URL, get structured data
- Local image editing with tools like Finegrain (impressive results)
- Document chat using Qwen 2.5 72B (handles technical papers well)
Sharing this early because the best tools come from the community. Drop your favorite tools in the comments or join the discussion on what to add next!
π JournalistsonHF/ai-toolkit
Been testing all these tools myself and created a searchable collection of the most practical ones - from audio transcription to image generation to document analysis. No coding needed, no expensive subscriptions.
Some highlights I've tested personally:
- Private, on-device transcription with speaker ID in 100+ languages using Whisper
- Website scraping that just works - paste a URL, get structured data
- Local image editing with tools like Finegrain (impressive results)
- Document chat using Qwen 2.5 72B (handles technical papers well)
Sharing this early because the best tools come from the community. Drop your favorite tools in the comments or join the discussion on what to add next!
π JournalistsonHF/ai-toolkit

reacted to
hexgrad's
post with π₯
18 days ago
Post
8417
hexgrad/Kokoro-82M got an upgrade! β¬οΈ More voices, more languages,
GitHub: https://github.com/hexgrad/kokoro
PyPI: https://pypi.org/project/kokoro/
Space: hexgrad/Kokoro-TTS
pip install kokoro
, and still 82M parameters.GitHub: https://github.com/hexgrad/kokoro
PyPI: https://pypi.org/project/kokoro/
Space: hexgrad/Kokoro-TTS

reacted to
andrewrreed's
post with π₯
2 months ago
Post
2783
π Supercharge your LLM apps with Langfuse on Hugging Face Spaces!
Langfuse brings end-to-end observability and tooling to accelerate your dev workflow from experiments through production
Now available as a Docker Space directly on the HF Hub! π€
π Trace everything: monitor LLM calls, retrieval, and agent actions with popular frameworks
1β£ One-click deployment: on Spaces with persistent storage and integrated OAuth
π Simple Prompt Management: Version, edit, and update without redeployment
β Intuitive Evals: Collect user feedback, run model/prompt evaluations, and improve quality
π Dataset Creation: Build datasets directly from production data to enhance future performance
Kudos to the Langfuse team for this collab and the awesome, open-first product theyβre building! π @marcklingen @Clemo @MJannik
π Space: langfuse/langfuse-template-space
π Docs: https://huggingface.co./docs/hub/spaces-sdks-docker-langfuse
Langfuse brings end-to-end observability and tooling to accelerate your dev workflow from experiments through production
Now available as a Docker Space directly on the HF Hub! π€
π Trace everything: monitor LLM calls, retrieval, and agent actions with popular frameworks
1β£ One-click deployment: on Spaces with persistent storage and integrated OAuth
π Simple Prompt Management: Version, edit, and update without redeployment
β Intuitive Evals: Collect user feedback, run model/prompt evaluations, and improve quality
π Dataset Creation: Build datasets directly from production data to enhance future performance
Kudos to the Langfuse team for this collab and the awesome, open-first product theyβre building! π @marcklingen @Clemo @MJannik
π Space: langfuse/langfuse-template-space
π Docs: https://huggingface.co./docs/hub/spaces-sdks-docker-langfuse

posted
an
update
2 months ago
Post
689
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

reacted to
MoritzLaurer's
post with π₯
2 months ago
Post
2236
π Releasing a new zeroshot-classifier based on ModernBERT! Some key takeaways:
- β‘ Speed & efficiency: It's multiple times faster and uses significantly less memory than DeBERTav3. You can use larger batch sizes and enabling bf16 (instead of fp16) gave me a ~2x speed boost as well
- π Performance tradeoff: It performs slightly worse than DeBERTav3 on average across my zeroshot classification task collection
- π§ Use cases: I recommend using it for scenarios requiring speed and a larger context window (8k).
- π‘ Whatβs next? Iβm preparing a newer version trained on better + longer synthetic data to fully leverage the 8k context window and improve upon the training mix of my older zeroshot-v2.0 models. I also hope that there will be a multilingual variant in the future.
Great work by https://huggingface.co./answerdotai !
If youβre looking for a high-speed zeroshot classifier, give it a try!
π Resources below: π
Base model: MoritzLaurer/ModernBERT-base-zeroshot-v2.0
Large model: MoritzLaurer/ModernBERT-large-zeroshot-v2.0
Updated zeroshot collection: MoritzLaurer/zeroshot-classifiers-6548b4ff407bb19ff5c3ad6f
ModernBERT collection with paper: answerdotai/modernbert-67627ad707a4acbf33c41deb
- β‘ Speed & efficiency: It's multiple times faster and uses significantly less memory than DeBERTav3. You can use larger batch sizes and enabling bf16 (instead of fp16) gave me a ~2x speed boost as well
- π Performance tradeoff: It performs slightly worse than DeBERTav3 on average across my zeroshot classification task collection
- π§ Use cases: I recommend using it for scenarios requiring speed and a larger context window (8k).
- π‘ Whatβs next? Iβm preparing a newer version trained on better + longer synthetic data to fully leverage the 8k context window and improve upon the training mix of my older zeroshot-v2.0 models. I also hope that there will be a multilingual variant in the future.
Great work by https://huggingface.co./answerdotai !
If youβre looking for a high-speed zeroshot classifier, give it a try!
π Resources below: π
Base model: MoritzLaurer/ModernBERT-base-zeroshot-v2.0
Large model: MoritzLaurer/ModernBERT-large-zeroshot-v2.0
Updated zeroshot collection: MoritzLaurer/zeroshot-classifiers-6548b4ff407bb19ff5c3ad6f
ModernBERT collection with paper: answerdotai/modernbert-67627ad707a4acbf33c41deb

reacted to
burtenshaw's
post with π€β€οΈ
3 months ago
Post
3044
People are flexing their end of year stats, so I made this app to show hub stats in a tidy design!
Thanks @Ameeeee and @jfcalvo for the feature from Argilla!
burtenshaw/recap
Thanks @Ameeeee and @jfcalvo for the feature from Argilla!
burtenshaw/recap

reacted to
julien-c's
post with π€β€οΈ
3 months ago
Post
10351
After some heated discussion π₯, we clarify our intent re. storage limits on the Hub
TL;DR:
- public storage is free, and (unless blatant abuse) unlimited. We do ask that you consider upgrading to PRO and/or Enterprise Hub if possible
- private storage is paid above a significant free tier (1TB if you have a paid account, 100GB otherwise)
docs: https://huggingface.co./docs/hub/storage-limits
We optimize our infrastructure continuously to scale our storage for the coming years of growth in Machine learning, to the benefit of the community π₯
cc: @reach-vb @pierric @victor and the HF team
TL;DR:
- public storage is free, and (unless blatant abuse) unlimited. We do ask that you consider upgrading to PRO and/or Enterprise Hub if possible
- private storage is paid above a significant free tier (1TB if you have a paid account, 100GB otherwise)
docs: https://huggingface.co./docs/hub/storage-limits
We optimize our infrastructure continuously to scale our storage for the coming years of growth in Machine learning, to the benefit of the community π₯
cc: @reach-vb @pierric @victor and the HF team

reacted to
clem's
post with π₯
3 months ago
Post
4676
Six predictions for AI in 2025 (and a review of how my 2024 predictions turned out):
- There will be the first major public protest related to AI
- A big company will see its market cap divided by two or more because of AI
- At least 100,000 personal AI robots will be pre-ordered
- China will start to lead the AI race (as a consequence of leading the open-source AI race).
- There will be big breakthroughs in AI for biology and chemistry.
- We will begin to see the economic and employment growth potential of AI, with 15M AI builders on Hugging Face.
How my predictions for 2024 turned out:
- A hyped AI company will go bankrupt or get acquired for a ridiculously low price
β (Inflexion, AdeptAI,...)
- Open-source LLMs will reach the level of the best closed-source LLMs
β with QwQ and dozens of others
- Big breakthroughs in AI for video, time-series, biology and chemistry
β for video π΄for time-series, biology and chemistry
- We will talk much more about the cost (monetary and environmental) of AI
β Monetary π΄Environmental (π’)
- A popular media will be mostly AI-generated
β with NotebookLM by Google
- 10 millions AI builders on Hugging Face leading to no increase of unemployment
πcurrently 7M of AI builders on Hugging Face
- There will be the first major public protest related to AI
- A big company will see its market cap divided by two or more because of AI
- At least 100,000 personal AI robots will be pre-ordered
- China will start to lead the AI race (as a consequence of leading the open-source AI race).
- There will be big breakthroughs in AI for biology and chemistry.
- We will begin to see the economic and employment growth potential of AI, with 15M AI builders on Hugging Face.
How my predictions for 2024 turned out:
- A hyped AI company will go bankrupt or get acquired for a ridiculously low price
β (Inflexion, AdeptAI,...)
- Open-source LLMs will reach the level of the best closed-source LLMs
β with QwQ and dozens of others
- Big breakthroughs in AI for video, time-series, biology and chemistry
β for video π΄for time-series, biology and chemistry
- We will talk much more about the cost (monetary and environmental) of AI
β Monetary π΄Environmental (π’)
- A popular media will be mostly AI-generated
β with NotebookLM by Google
- 10 millions AI builders on Hugging Face leading to no increase of unemployment
πcurrently 7M of AI builders on Hugging Face

reacted to
andito's
post with β€οΈ
4 months ago
Post
3374
Let's go! We are releasing SmolVLM, a smol 2B VLM built for on-device inference that outperforms all models at similar GPU RAM usage and tokens throughputs.
- SmolVLM generates tokens 7.5 to 16 times faster than Qwen2-VL! π€―
- Other models at this size crash a laptop, but SmolVLM comfortably generates 17 tokens/sec on a macbook! π
- SmolVLM can be fine-tuned on a Google collab! Or process millions of documents with a consumer GPU!
- SmolVLM even outperforms larger models in video benchmarks, despite not even being trained on videos!
Check out more!
Demo: HuggingFaceTB/SmolVLM
Blog: https://huggingface.co./blog/smolvlm
Model: HuggingFaceTB/SmolVLM-Instruct
Fine-tuning script: https://github.com/huggingface/smollm/blob/main/finetuning/Smol_VLM_FT.ipynb
- SmolVLM generates tokens 7.5 to 16 times faster than Qwen2-VL! π€―
- Other models at this size crash a laptop, but SmolVLM comfortably generates 17 tokens/sec on a macbook! π
- SmolVLM can be fine-tuned on a Google collab! Or process millions of documents with a consumer GPU!
- SmolVLM even outperforms larger models in video benchmarks, despite not even being trained on videos!
Check out more!
Demo: HuggingFaceTB/SmolVLM
Blog: https://huggingface.co./blog/smolvlm
Model: HuggingFaceTB/SmolVLM-Instruct
Fine-tuning script: https://github.com/huggingface/smollm/blob/main/finetuning/Smol_VLM_FT.ipynb

posted
an
update
4 months ago
Post
1096
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
π Wed Oct 30th - 9am PT / 12pm ET / 18h CET
Can't wait!

reacted to
clem's
post with β€οΈπ€
5 months ago
Post
4472
This is no Woodstock AI but will be fun nonetheless haha. Iβll be hosting a live workshop with team members next week about the Enterprise Hugging Face hub.
1,000 spots available first-come first serve with some surprises during the stream!
You can register and add to your calendar here: https://streamyard.com/watch/JS2jHsUP3NDM
1,000 spots available first-come first serve with some surprises during the stream!
You can register and add to your calendar here: https://streamyard.com/watch/JS2jHsUP3NDM