Hugging Face's logo

Deploy HF TF ViTs

non-profit
https://github.com/sayakpaul/deploy-hf-tf-vision-models
Activity Feed

AI & ML interests

None defined yet.

Recent Activity

chansung  authored a paper about 2 months ago
KaSA: Knowledge-Aware Singular-Value Adaptation of Large Language Models
sayakpaul  authored a paper about 2 months ago
A Noise is Worth Diffusion Guidance
sayakpaul  authored a paper 5 months ago
LlamaDuo: LLMOps Pipeline for Seamless Migration from Service LLMs to Small-Scale Local LLMs
View all activity

deploy-hf-tf-vit's activity

chansung 
posted an update 3 days ago
view post
Post
1535
New look for AI powered paper reviews from the list by Hugging Face Daily Papers ( managed by the @akhaliq )

Bookmark the webpage along, check comprehensive reviews by Google DeepMind Gemini 1.5, and listen to audio podcast made by the same tech used in NotebookLM.

Link: https://deep-diver.github.io/ai-paper-reviewer/

This is not an official service by Hugging Face. It is just a service developed by an individual developer using his own money :)
chansung 
posted an update 5 days ago
view post
Post
1901
Simple summarization of Evolving Deeper LLM Thinking (Google DeepMind)

The process starts by posing a question.
1) The LLM generates initial responses.
2) These generated responses are evaluated according to specific criteria (program-based checker).
3) The LLM critiques the evaluated results.
4) The LLM refines the responses based on the evaluation, critique, and original responses.

The refined response is then fed back into step 2). If it meets the criteria, the process ends. Otherwise, the algorithm generates more responses based on the refined ones (with some being discarded, some remaining, and some responses potentially being merged).

Through this process, it demonstrated excellent performance in complex scheduling problems (travel planning, meeting scheduling, etc.). It's a viable method for finding highly effective solutions in specific scenarios.

However, there are two major drawbacks:
🤔 An excessive number of API calls are required. (While the cost might not be very high, it leads to significant latency.)
🤔 The evaluator is program-based. (This limits its use as a general method. It could potentially be modified/implemented using LLM as Judge, but that would introduce additional API costs for evaluation.)

https://arxiv.org/abs/2501.09891
chansung 
posted an update 6 days ago
view post
Post
1947
Simple Summarization on DeepSeek-R1 from DeepSeek AI

The RL stage is very important.
↳ However, it is difficult to create a truly helpful AI for people solely through RL.
↳ So, we applied a learning pipeline consisting of four stages: providing a good starting point, reasoning RL, SFT, and safety RL, and achieved performance comparable to o1.
↳ Simply fine-tuning other open models with the data generated by R1-Zero (distillation) resulted in performance comparable to o1-mini.

Of course, this is just a brief overview and may not be of much help. All models are accessible on Hugging Face, and the paper can be read through the GitHub repository.


Model: https://huggingface.co./deepseek-ai
Paper: https://github.com/deepseek-ai/DeepSeek-R1
  • 1 reply
·
sayakpaul 
posted an update about 1 month ago
sayakpaul 
posted an update about 1 month ago
view post
Post
2122
In the past seven days, the Diffusers team has shipped:

1. Two new video models
2. One new image model
3. Two new quantization backends
4. Three new fine-tuning scripts
5. Multiple fixes and library QoL improvements

Coffee on me if someone can guess 1 - 4 correctly.
  • 1 reply
·
chansung 
authored a paper about 2 months ago
sayakpaul 
posted an update about 2 months ago
view post
Post
2101
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
  • 7 replies
·
sayakpaul 
posted an update about 2 months ago
view post
Post
2137
The Control family of Flux from @black-forest-labs should be discussed more!

It enables structural controls like ControlNets while being significantly less expensive to run!

So, we're working on a Control LoRA training script 🤗

It's still WIP, so go easy:
https://github.com/huggingface/diffusers/pull/10130
sayakpaul 
authored a paper about 2 months ago
sayakpaul 
posted an update about 2 months ago
sayakpaul 
posted an update 2 months ago
view post
Post
2655
It's been a while we shipped native quantization support in diffusers 🧨

We currently support bistandbytes as the official backend but using others like torchao is already very simple.

This post is just a reminder of what's possible:

1. Loading a model with a quantization config
2. Saving a model with quantization config
3. Loading a pre-quantized model
4. enable_model_cpu_offload()
5. Training and loading LoRAs into quantized checkpoints

Docs:
https://huggingface.co./docs/diffusers/main/en/quantization/bitsandbytes
  • 1 reply
·
chansung 
posted an update 2 months ago
view post
Post
1946
🎙️ Listen to the audio "Podcast" of every single Hugging Face Daily Papers.

Now, "AI Paper Reviewer" project can automatically generates audio podcasts on any papers published on arXiv, and this is integrated into the GitHub Action pipeline. I sounds pretty similar to hashtag#NotebookLM in my opinion.

🎙️ Try out yourself at https://deep-diver.github.io/ai-paper-reviewer/

This audio podcast is powered by Google technologies: 1) Google DeepMind Gemini 1.5 Flash model to generate scripts of a podcast, then 2) Google Cloud Vertex AI's Text to Speech model to synthesize the voice turning the scripts into the natural sounding voices (with latest addition of "Journey" voice style)

"AI Paper Reviewer" is also an open source project. Anyone can use it to build and own a personal blog on any papers of your interests. Hence, checkout the project repository below if you are interested in!
: https://github.com/deep-diver/paper-reviewer

This project is going to support other models including open weights soon for both text-based content generation and voice synthesis for the podcast. The only reason I chose Gemini model is that it offers a "free-tier" which is enough to shape up this projects with non-realtime batch generations. I'm excited to see how others will use this tool to explore the world of AI research, hence feel free to share your feedback and suggestions!
·
chansung 
posted an update 3 months ago
view post
Post
4751
Effortlessly stay up-to-date with AI research trends using a new AI tool, "AI Paper Reviewer" !!

It analyzes a list of Hugging Face Daily Papers(w/ @akhaliq ) and turn them into insightful blog posts. This project leverages Gemini models (1.5 Pro, 1.5 Flash, and 1.5 Flash-8B) for content generation and Upstage Document Parse for parsing the layout and contents.
blog link: https://deep-diver.github.io/ai-paper-reviewer/

Also, here is the link of GitHub repository for parsing and generating pipeline. By using this, you can easily build your own GitHub static pages based on any arXiv papers with your own interest!
: https://github.com/deep-diver/paper-reviewer
sayakpaul 
posted an update 4 months ago
view post
Post
2763
Did some little experimentation to resize pre-trained LoRAs on Flux. I explored two themes:

* Decrease the rank of a LoRA
* Increase the rank of a LoRA

The first one is helpful in reducing memory requirements if the LoRA is of a high rank, while the second one is merely an experiment. Another implication of this study is in the unification of LoRA ranks when you would like to torch.compile() them.

Check it out here:
sayakpaul/flux-lora-resizing
  • 1 reply
·
sayakpaul 
authored a paper 5 months ago
chansung 
authored a paper 5 months ago
sayakpaul 
posted an update 5 months ago
sayakpaul 
posted an update 6 months ago
view post
Post
4494
Flux.1-Dev like images but in fewer steps.

Merging code (very simple), inference code, merged params: sayakpaul/FLUX.1-merged

Enjoy the Monday 🤗
·
sayakpaul 
posted an update 6 months ago
view post
Post
3801
With larger and larger diffusion transformers coming up, it's becoming increasingly important to have some good quantization tools for them.

We present our findings from a series of experiments on quantizing different diffusion pipelines based on diffusion transformers.

We demonstrate excellent memory savings with a bit of sacrifice on inference latency which is expected to improve in the coming days.

Diffusers 🤝 Quanto ❤️

This was a juicy collaboration between @dacorvo and myself.

Check out the post to learn all about it
https://huggingface.co./blog/quanto-diffusers
·
sayakpaul 
posted an update 7 months ago