Introducing the Synthetic Data Generator - Build Datasets with Natural Language
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ben burtenshaw
burtenshaw
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burtenshaw/recap:The downloads stat only count last month
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5 days ago
burtenshaw/recap
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burtenshaw/recap:Only shows dataset and spaces info for me?
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posted
an
update
6 days ago
Post
2540
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
- 1 reply
reacted to
lhoestq's
post with ❤️
12 days ago
Post
1606
Made a HF Dataset editor a la gg sheets here:
lhoestq/dataset-spreadsheets
With Dataset Spreadsheets:
✏️ Edit datasets in the UI
🔗 Share link with collaborators
🐍 Use locally in DuckDB or Python
Available for the 100,000+ parquet datasets on HF :)
With Dataset Spreadsheets:
✏️ Edit datasets in the UI
🔗 Share link with collaborators
🐍 Use locally in DuckDB or Python
Available for the 100,000+ parquet datasets on HF :)
posted
an
update
15 days ago
Post
2386
Quick update from week 1 of smol course. The community is taking the driving seat and using the material for their own projects. If you want to do the same, join in!
- we have ongoing translation projects in Korean, Vietnamese, Portuguese, and Spanish
- 3 chapters are ready for students. On topics like, instruction tuning, preference alignment, and parameter efficient fine tuning
- 3 chapters are in progress on evaluation, vision language models, and synthetic data.
- around 780 people have forked the repo to use it for learning, teaching, sharing.
⏭️ Next step is to support people that want to use the course for teaching, content creation, internal knowledge sharing, or anything. If you're into this. Drop an issue or PR
REPO: https://buff.ly/3ZCMKX2
discord channel: https://buff.ly/4f9F8jA
- we have ongoing translation projects in Korean, Vietnamese, Portuguese, and Spanish
- 3 chapters are ready for students. On topics like, instruction tuning, preference alignment, and parameter efficient fine tuning
- 3 chapters are in progress on evaluation, vision language models, and synthetic data.
- around 780 people have forked the repo to use it for learning, teaching, sharing.
⏭️ Next step is to support people that want to use the course for teaching, content creation, internal knowledge sharing, or anything. If you're into this. Drop an issue or PR
REPO: https://buff.ly/3ZCMKX2
discord channel: https://buff.ly/4f9F8jA
What model will the platform use?
reacted to
sayakpaul's
post with 🤗
15 days ago
Post
2040
Introducing a high-quality open-preference dataset to further this line of research for image generation.
Despite being such an inseparable component for modern image generation, open preference datasets are a rarity!
So, we decided to work on one with the community!
Check it out here:
https://huggingface.co/blog/image-preferences
Despite being such an inseparable component for modern image generation, open preference datasets are a rarity!
So, we decided to work on one with the community!
Check it out here:
https://huggingface.co/blog/image-preferences
-
- 7 replies
reacted to
their
post with ❤️
22 days ago
Post
2565
For anyone looking to boost their LLM fine-tuning and alignment skills this decemeber. We're running this free and open course called smol course. It’s not big like Li Yin and
@mlabonne
, it’s just smol.
👷 It focuses on practical use cases, so if you’re working on something, bring it along.
👯♀️ It’s peer reviewed and open so you can discuss and get feedback.
🤘 If you’re already a smol pro, feel free to drop a star or issue.
> > Part 1 starts now, and it’s on instruction tuning!
https://github.com/huggingface/smol-course
👷 It focuses on practical use cases, so if you’re working on something, bring it along.
👯♀️ It’s peer reviewed and open so you can discuss and get feedback.
🤘 If you’re already a smol pro, feel free to drop a star or issue.
> > Part 1 starts now, and it’s on instruction tuning!
https://github.com/huggingface/smol-course
posted
an
update
22 days ago
Post
2565
For anyone looking to boost their LLM fine-tuning and alignment skills this decemeber. We're running this free and open course called smol course. It’s not big like Li Yin and
@mlabonne
, it’s just smol.
👷 It focuses on practical use cases, so if you’re working on something, bring it along.
👯♀️ It’s peer reviewed and open so you can discuss and get feedback.
🤘 If you’re already a smol pro, feel free to drop a star or issue.
> > Part 1 starts now, and it’s on instruction tuning!
https://github.com/huggingface/smol-course
👷 It focuses on practical use cases, so if you’re working on something, bring it along.
👯♀️ It’s peer reviewed and open so you can discuss and get feedback.
🤘 If you’re already a smol pro, feel free to drop a star or issue.
> > Part 1 starts now, and it’s on instruction tuning!
https://github.com/huggingface/smol-course
posted
an
update
25 days ago
Post
1586
[SATURDAY ROUNDUP] ☕️🧑🎓
In case you missed everything this week. It’s all about vision language models and image preference datasets. Here are the models and datasets you can use in your projects.
QWQ-32B-Preview is the first open weights model to reason like o1 with comparable performance. It’s large but is acing some of the hardest tasks.
https://bsky.app/profile/philschmid.bsky.social/post/3lbylz6nzqk25
SmolVLM is a vision implementation of the recently released SmolLM2. It uses the Idefics3 approach to add a vision encoder. The main difference being the smaller language model (8b > 1.7b) and more compression of images. This results in a model that is very accurate for its memory footprint.
https://huggingface.co/blog/smolvlm
ColSmolVLM is a vision embedding model based on SmolVLM using the Colbert approach from ColPali. This is shown to be great at document retrieval and everyone should test it out in their RAG setups.
https://huggingface.co/posts/merve/663466156074132
In an effort to build a FLUX level open source image generation model, the community is building a dataset of image preferences. The dataset is already open and the project is still running. Join in!
https://huggingface.co/posts/davidberenstein1957/405018978675827
TRL tutorial Drop - This week I dropped a load of tutorials on finetuning and aligning models with TRL. If you’re upskilling in this space, you should check these out.
https://bsky.app/profile/benburtenshaw.bsky.social/post/3lbrc56ap3222
In case you missed everything this week. It’s all about vision language models and image preference datasets. Here are the models and datasets you can use in your projects.
QWQ-32B-Preview is the first open weights model to reason like o1 with comparable performance. It’s large but is acing some of the hardest tasks.
https://bsky.app/profile/philschmid.bsky.social/post/3lbylz6nzqk25
SmolVLM is a vision implementation of the recently released SmolLM2. It uses the Idefics3 approach to add a vision encoder. The main difference being the smaller language model (8b > 1.7b) and more compression of images. This results in a model that is very accurate for its memory footprint.
https://huggingface.co/blog/smolvlm
ColSmolVLM is a vision embedding model based on SmolVLM using the Colbert approach from ColPali. This is shown to be great at document retrieval and everyone should test it out in their RAG setups.
https://huggingface.co/posts/merve/663466156074132
In an effort to build a FLUX level open source image generation model, the community is building a dataset of image preferences. The dataset is already open and the project is still running. Join in!
https://huggingface.co/posts/davidberenstein1957/405018978675827
TRL tutorial Drop - This week I dropped a load of tutorials on finetuning and aligning models with TRL. If you’re upskilling in this space, you should check these out.
https://bsky.app/profile/benburtenshaw.bsky.social/post/3lbrc56ap3222
reacted to
davidberenstein1957's
post with ❤️🔥
27 days ago
Post
1568
🔥 Dataset Drop - Open Image Preferences
BlackForest Labs Flux Dev VS. Stability AI Stable Diffusion Large 3.5
Together with the data-is-better-together community, we've worked on an Apache 2.0 licensed open image preference dataset based on the fal ai imgsys prompts dataset. Thanks to the awesome community, we have managed to get 5K preference pairs in less than 2 days. The annotation alignment among annotators is great too.
Aashish Kumar won a month of Hugging Face Pro by making the most contributions! Congrats from the entire team 🥇
The best thing?! We are not done yet! Let's keep the annotations coming for 5K more in the second part of the sprint! (with more prices to go around).
Dataset: https://huggingface.co/datasets/data-is-better-together/image-preferences-results
BlackForest Labs Flux Dev VS. Stability AI Stable Diffusion Large 3.5
Together with the data-is-better-together community, we've worked on an Apache 2.0 licensed open image preference dataset based on the fal ai imgsys prompts dataset. Thanks to the awesome community, we have managed to get 5K preference pairs in less than 2 days. The annotation alignment among annotators is great too.
Aashish Kumar won a month of Hugging Face Pro by making the most contributions! Congrats from the entire team 🥇
The best thing?! We are not done yet! Let's keep the annotations coming for 5K more in the second part of the sprint! (with more prices to go around).
Dataset: https://huggingface.co/datasets/data-is-better-together/image-preferences-results
reacted to
davidberenstein1957's
post with ➕😎🤗🧠🚀🔥
29 days ago
Post
1701
Let’s make a generation of amazing image-generation models
The best image generation models are trained on human preference datasets, where annotators have selected the best image from a choice of two. Unfortunately, many of these datasets are closed source so the community cannot train open models on them. Let’s change that!
The community can contribute image preferences for an open-source dataset that could be used for building AI models that convert text to image, like the flux or stable diffusion families. The dataset will be open source so everyone can use it to train models that we can all use.
Blog: https://huggingface.co/blog/burtenshaw/image-preferences
The best image generation models are trained on human preference datasets, where annotators have selected the best image from a choice of two. Unfortunately, many of these datasets are closed source so the community cannot train open models on them. Let’s change that!
The community can contribute image preferences for an open-source dataset that could be used for building AI models that convert text to image, like the flux or stable diffusion families. The dataset will be open source so everyone can use it to train models that we can all use.
Blog: https://huggingface.co/blog/burtenshaw/image-preferences
reacted to
clem's
post with 🤗
4 months ago
Post
3680
This isn’t a goal of ours because we have plenty of money in the bank but quite excited to see that
@huggingfaceis
profitable these days, with 220 team members and most of our platform being free (like model hosting) and open-source for the community!
Especially noteworthy at a time when most AI startups wouldn’t survive a year or two without VC money. Yay!
Especially noteworthy at a time when most AI startups wouldn’t survive a year or two without VC money. Yay!
- 4 replies
reacted to
davidberenstein1957's
post with 🧠🔥
4 months ago
Post
3006
🚀 We will be generating a preference dataset for DPO/ORPO and cleaning it with AI feedback during our upcoming meetup!
In this session, we'll walk you through the essentials of building a distilabel pipeline by exploring two key use cases: cleaning an existing dataset and generating a preference dataset for DPO/ORPO. You’ll also learn how to make the most of AI feedback, integrating Argilla to gather human feedback and improve the overall data quality.
This session is perfect for you
- if you’re getting started with distilabel or synthetic data
- if you want to learn how to use LLM inference endpoints for **free**
- if you want to discover new functionalities
- if you want to provide us with new feedback
Sign up here: https://lu.ma/dt0c7jru
In this session, we'll walk you through the essentials of building a distilabel pipeline by exploring two key use cases: cleaning an existing dataset and generating a preference dataset for DPO/ORPO. You’ll also learn how to make the most of AI feedback, integrating Argilla to gather human feedback and improve the overall data quality.
This session is perfect for you
- if you’re getting started with distilabel or synthetic data
- if you want to learn how to use LLM inference endpoints for **free**
- if you want to discover new functionalities
- if you want to provide us with new feedback
Sign up here: https://lu.ma/dt0c7jru