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Mex Ivanov
MexIvanov
AI & ML interests
NLP, Coding, Quantum Computing and more.
Recent Activity
liked
a dataset
1 day ago
wikimedia/wikipedia
Reacted to
davidberenstein1957's
post
with 🔥
3 days ago
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
updated
a model
6 days ago
MexIvanov/MistRAG-7B-ruen-v1-merged
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MexIvanov's activity
liked
a
dataset
1 day ago
Reacted to
davidberenstein1957's
post with 🔥
3 days ago
Post
1630
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
updated
3
models
6 days ago
liked
a
model
13 days ago
Reacted to
maxiw's
post with ❤️
17 days ago
Post
4582
I was curious to see what people post here on HF so I created a dataset with all HF Posts:
maxiw/hf-posts
Some interesting stats:
Top 5 Authors by Total Impressions:
-----------------------------------
@merve : 171,783 impressions (68 posts)
@fdaudens : 135,253 impressions (81 posts)
@singhsidhukuldeep : 122,591 impressions (81 posts)
@akhaliq : 119,526 impressions (78 posts)
@MonsterMMORPG : 112,500 impressions (45 posts)
Top 5 Users by Number of Reactions Given:
----------------------------------------
@osanseviero : 1278 reactions
@clem : 910 reactions
@John6666 : 899 reactions
@victor : 674 reactions
@samusenps : 655 reactions
Top 5 Most Used Reactions:
-------------------------
❤️: 7048 times
🔥: 5921 times
👍: 4856 times
🚀: 2549 times
🤗: 2065 times
Some interesting stats:
Top 5 Authors by Total Impressions:
-----------------------------------
@merve : 171,783 impressions (68 posts)
@fdaudens : 135,253 impressions (81 posts)
@singhsidhukuldeep : 122,591 impressions (81 posts)
@akhaliq : 119,526 impressions (78 posts)
@MonsterMMORPG : 112,500 impressions (45 posts)
Top 5 Users by Number of Reactions Given:
----------------------------------------
@osanseviero : 1278 reactions
@clem : 910 reactions
@John6666 : 899 reactions
@victor : 674 reactions
@samusenps : 655 reactions
Top 5 Most Used Reactions:
-------------------------
❤️: 7048 times
🔥: 5921 times
👍: 4856 times
🚀: 2549 times
🤗: 2065 times
- 10 replies
updated
a
dataset
20 days ago
updated
3
models
20 days ago
Reacted to
TuringsSolutions's
post with 👀
about 1 month ago
Post
2888
I have been seeing a specific type of AI hype more and more, I call it, releasing research expecting that no one will ever reproduce your methods, then overhyping your results. I test the methodology of maybe 4-5 research papers per day. That is how I find a lot of my research. Usually, 3-4 of those experiments end up not being reproduceable for some reason. I am starting to think it is not accidental.
So, I am launching a new series where I specifically showcase a research paper by reproducing their methodology and highlighting the blatant flaws that show up when you actually do this. Here is Episode 1!
https://www.youtube.com/watch?v=JLa0cFWm1A4
So, I am launching a new series where I specifically showcase a research paper by reproducing their methodology and highlighting the blatant flaws that show up when you actually do this. Here is Episode 1!
https://www.youtube.com/watch?v=JLa0cFWm1A4
- 5 replies
Reacted to
merve's
post with 🤗❤️👍🔥
about 1 month ago
Post
5195
Hugging Face Hub Python library now comes with easy inference for vision language models! ✨
$ pip install huggingface_hub 🤗
$ pip install huggingface_hub 🤗
- 1 reply
Reacted to
singhsidhukuldeep's
post with 🔥
about 1 month ago
Post
1169
Good folks from
@Microsoft
Research have just released bitnet.cpp, a game-changing inference framework that achieves remarkable performance gains.
Key Technical Highlights:
- Achieves speedups of up to 6.17x on x86 CPUs and 5.07x on ARM CPUs
- Reduces energy consumption by 55.4–82.2%
- Enables running 100B parameter models at human reading speed (5–7 tokens/second) on a single CPU
Features Three Optimized Kernels:
1. I2_S: Uses 2-bit weight representation
2. TL1: Implements 4-bit index lookup tables for every two weights
3. TL2: Employs 5-bit compression for every three weights
Performance Metrics:
- Lossless inference with 100% accuracy compared to full-precision models
- Tested across model sizes from 125M to 100B parameters
- Evaluated on both Apple M2 Ultra and Intel i7-13700H processors
This breakthrough makes running large language models locally more accessible than ever, opening new possibilities for edge computing and resource-constrained environments.
Key Technical Highlights:
- Achieves speedups of up to 6.17x on x86 CPUs and 5.07x on ARM CPUs
- Reduces energy consumption by 55.4–82.2%
- Enables running 100B parameter models at human reading speed (5–7 tokens/second) on a single CPU
Features Three Optimized Kernels:
1. I2_S: Uses 2-bit weight representation
2. TL1: Implements 4-bit index lookup tables for every two weights
3. TL2: Employs 5-bit compression for every three weights
Performance Metrics:
- Lossless inference with 100% accuracy compared to full-precision models
- Tested across model sizes from 125M to 100B parameters
- Evaluated on both Apple M2 Ultra and Intel i7-13700H processors
This breakthrough makes running large language models locally more accessible than ever, opening new possibilities for edge computing and resource-constrained environments.
- 4 replies
updated
a
dataset
3 months ago
Reacted to
AlexBodner's
post with 👍
3 months ago
Post
1586
💾🧠Want to know how much VRAM you will need for training your model? 💾🧠
Now you can use this app in which you can input a torch/tensorflow summary or the parameters count and get an estimate of the required memory!
Use it in: howmuchvram.com
Also, everything is Open Source so you can contribute in repo: https://github.com/AlexBodner/How_Much_VRAM
Leave it a star⭐
Now you can use this app in which you can input a torch/tensorflow summary or the parameters count and get an estimate of the required memory!
Use it in: howmuchvram.com
Also, everything is Open Source so you can contribute in repo: https://github.com/AlexBodner/How_Much_VRAM
Leave it a star⭐
Reacted to
lorraine2's
post with 👍
4 months ago
Post
2695
⚡ My PhD thesis, “Scalable Nested Optimization for Deep Learning,” is now on arXiv! ⚡
tl;dr: We develop various optimization tools with highlights, including:
· Making the momentum coefficient complex for adversarial games like GANs.
· Optimizing millions of hyperparameters using implicit differentiation.
· Tuning hyperparameters using hypernetworks.
· Differentiably finding bifurcations in optimization for diverse solutions.
https://arxiv.org/abs/2407.01526
tl;dr: We develop various optimization tools with highlights, including:
· Making the momentum coefficient complex for adversarial games like GANs.
· Optimizing millions of hyperparameters using implicit differentiation.
· Tuning hyperparameters using hypernetworks.
· Differentiably finding bifurcations in optimization for diverse solutions.
https://arxiv.org/abs/2407.01526
- 3 replies