Yeah, it seem pretty nice !
Jorge Munoz Laredo
jorgemunozl
AI & ML interests
I like Vision Language Action Models, AI4Science, Diffusion based architectures (flow matching) and I love physics.
Recent Activity
liked
a dataset
about 21 hours ago
latam-gpt/tulu-3-sft-mixture-no-identity
liked
a model
about 21 hours ago
unitreerobotics/UnifoLM-VLA-Base
liked
a model
about 21 hours ago
moonshotai/Kimi-K2.5
Organizations
replied to
MonsterMMORPG's
post
about 21 hours ago
reacted to
MonsterMMORPG's
post with 👍
about 21 hours ago
Post
1450
LTX 2 & Z Image Base Full Tutorial + Audio to Video Lip Sync + ComfyUI + SwarmUI + Windows + Cloud
Full tutorial link > https://www.youtube.com/watch?v=SkXrYezeEDc
Info
LTX 2 is the newest state of the art (SOTA) Open Source video generation model and tutorial will show you how to use it with very best and most performant way in ComfyUI and also in SwarmUI. Moreover, Z Image Base model published and I will show how to use Z Image Base with most amazing preset and workflow as well. Furthermore, this tutorial will show you how to install, update, setup, download ComfyUI and SwarmUI and models and presets and workflows both on Windows and on RunPod, Massed Compute and SimplePod. Linux users can use Massed Compute scripts and installers directly. This is a masterpiece entire lecture level complete tutorial. This video will kickstart your AI journey 100x. Both local Windows and Cloud.
45 Second Raw Demo Video
This video made with text + image + audio = lip synched and animated video at once
See video below
Full tutorial link > https://www.youtube.com/watch?v=SkXrYezeEDc
Info
LTX 2 is the newest state of the art (SOTA) Open Source video generation model and tutorial will show you how to use it with very best and most performant way in ComfyUI and also in SwarmUI. Moreover, Z Image Base model published and I will show how to use Z Image Base with most amazing preset and workflow as well. Furthermore, this tutorial will show you how to install, update, setup, download ComfyUI and SwarmUI and models and presets and workflows both on Windows and on RunPod, Massed Compute and SimplePod. Linux users can use Massed Compute scripts and installers directly. This is a masterpiece entire lecture level complete tutorial. This video will kickstart your AI journey 100x. Both local Windows and Cloud.
45 Second Raw Demo Video
This video made with text + image + audio = lip synched and animated video at once
See video below
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- 3 replies
reacted to
hassenhamdi's
post with 🔥
10 days ago
Post
1997
Google published the paper. I shipped the code. 🚀
DeepMind just released PACEvolve (Progress-Aware Consistent Evolution), a massive overhaul of the AlphaEvolve framework. It solves the critical issues of "Context Pollution" and "Mode Collapse" that have historically crippled evolutionary coding agents.
But there was no public implementation. So I built one.
Introducing OpenPACEvolve: A fully open-source, production-grade implementation of the PACEvolve framework.
🛠 I engineered this framework solo, but I wasn't working alone. I orchestrated a custom coding agents powered by Claude Opus 4.5 as Engineer and Gemini Pro 3 Preview ensuring fiedelity and quallty.
By leveraging these SOTA models, I was able to translate complex theoretical research into functional, modular Python architecture in record time. This is what the future of AI engineering looks like: Human architectural oversight + AI velocity.
🧠 What OpenPACEvolve Solves: Unlike standard agents that get "stuck" in loops, this framework implements the paper's full recipe for long-horizon stability: ✅ Hierarchical Context Management (HCM): Bi-level pruning to keep the agent's memory clean. ✅ Momentum-Based Backtracking (MBB): Uses "power-law backtracking" to detect stagnation and force pivots. ✅ Self-Adaptive Crossover: Intelligent code-sharing between parallel "islands."
👨💻 This project is more than a repo; it's a demonstration of rapid research-to-production cycles using next-gen AI workflows.
📎 Link of the paper : https://arxiv.org/abs/2601.10657
The code is live. The agents are ready. Check out the repository below. 👇
https://github.com/hassenhamdi/OpenPACEvolve
Star the repo 🌟.
DeepMind just released PACEvolve (Progress-Aware Consistent Evolution), a massive overhaul of the AlphaEvolve framework. It solves the critical issues of "Context Pollution" and "Mode Collapse" that have historically crippled evolutionary coding agents.
But there was no public implementation. So I built one.
Introducing OpenPACEvolve: A fully open-source, production-grade implementation of the PACEvolve framework.
🛠 I engineered this framework solo, but I wasn't working alone. I orchestrated a custom coding agents powered by Claude Opus 4.5 as Engineer and Gemini Pro 3 Preview ensuring fiedelity and quallty.
By leveraging these SOTA models, I was able to translate complex theoretical research into functional, modular Python architecture in record time. This is what the future of AI engineering looks like: Human architectural oversight + AI velocity.
🧠 What OpenPACEvolve Solves: Unlike standard agents that get "stuck" in loops, this framework implements the paper's full recipe for long-horizon stability: ✅ Hierarchical Context Management (HCM): Bi-level pruning to keep the agent's memory clean. ✅ Momentum-Based Backtracking (MBB): Uses "power-law backtracking" to detect stagnation and force pivots. ✅ Self-Adaptive Crossover: Intelligent code-sharing between parallel "islands."
👨💻 This project is more than a repo; it's a demonstration of rapid research-to-production cycles using next-gen AI workflows.
📎 Link of the paper : https://arxiv.org/abs/2601.10657
The code is live. The agents are ready. Check out the repository below. 👇
https://github.com/hassenhamdi/OpenPACEvolve
Star the repo 🌟.
reacted to
jzhang533's
post with 🔥
3 months ago
Post
3255
We’ve officially kicked off the ERNIE AI Developer Challenge!
We want to create something interesting with you all, so we partnered with Unsloth, LLaMA-Factory, Novita AI, D-Robotics, and CAMEL-AI to empower your creativity.
Come build with us: https://baiduernieai.devpost.com/?utm_source=ERNIE-HF&utm_medium=ERNIE-HF&utm_campaign=ERNIE+AI+Developer+Challenge
We want to create something interesting with you all, so we partnered with Unsloth, LLaMA-Factory, Novita AI, D-Robotics, and CAMEL-AI to empower your creativity.
Come build with us: https://baiduernieai.devpost.com/?utm_source=ERNIE-HF&utm_medium=ERNIE-HF&utm_campaign=ERNIE+AI+Developer+Challenge
reacted to
cjerzak's
post with 👀
3 months ago
Post
2876
>>> We're writing a new book, <Planetary Causal Inference>, on how to model counterfactuals at planetary scale by combining satellite imagery + other global data with local studies and RCTs. Forthcoming in 2026+.
>>> Book info: https://planetarycausalinference.org/book-launch
>>> All datasets used in the book will be openly available on our lab’s Hugging Face hub:
theaidevlab
>>> Book info: https://planetarycausalinference.org/book-launch
>>> All datasets used in the book will be openly available on our lab’s Hugging Face hub:
reacted to
codelion's
post with 🚀
3 months ago
Post
3614
On this day in 2019, OpenAI released the final GPT-2 model as part of their staged release. I still remember that November well - so much was happening, but GPT-2's release felt like a watershed moment for the field. It showed us what was possible with carefully trained language models.
To recreate some of that GPT-2 magic, I recently tackled an interesting challenge: can you pretrain a language model with just 1 billion tokens - roughly 1/10th of what GPT-2 used - and still get comparable performance? After 50+ systematic experiments testing different dataset mixtures, the answer is yes.
The result is codelion/gpt-2-70m, which achieves over 90% of GPT-2's benchmark performance despite being trained on 10x less data. The key was finding the optimal dataset composition: 50% high-quality textbook PDFs, 30% filtered web content, and 20% educational resources. It even beats GPT-2 on TruthfulQA (47.31% vs 40.69%).
If you're interested in the full story of how we discovered this optimal mixture and why curriculum learning catastrophically failed, check out the complete article: https://huggingface.co/blog/codelion/optimal-dataset-mixing
Sometimes less really is more - when you mix it right.
To recreate some of that GPT-2 magic, I recently tackled an interesting challenge: can you pretrain a language model with just 1 billion tokens - roughly 1/10th of what GPT-2 used - and still get comparable performance? After 50+ systematic experiments testing different dataset mixtures, the answer is yes.
The result is codelion/gpt-2-70m, which achieves over 90% of GPT-2's benchmark performance despite being trained on 10x less data. The key was finding the optimal dataset composition: 50% high-quality textbook PDFs, 30% filtered web content, and 20% educational resources. It even beats GPT-2 on TruthfulQA (47.31% vs 40.69%).
If you're interested in the full story of how we discovered this optimal mixture and why curriculum learning catastrophically failed, check out the complete article: https://huggingface.co/blog/codelion/optimal-dataset-mixing
Sometimes less really is more - when you mix it right.
- 1 reply
reacted to
daqc's
post with 😎
3 months ago
Post
2879
Just applied for HF Community Grant for “Hugging Research” — a lightweight CodeAgent‑based research assistant built on Hugging Face’s Open Deep Research project for the Hugging Face Hub (models, datasets, Spaces, users, collections, papers). It gathers links via dedicated tools and organizes them for easy review.
As this is for the community, comments and suggestions are appreciated: daqc/hugging-research#1
As this is for the community, comments and suggestions are appreciated: daqc/hugging-research#1
reacted to
MonsterMMORPG's
post with ❤️
5 months ago
Post
7424
I have concluded first 8 traininings of Qwen Image LoRA - we are not at the level of FLUX yet and next 8 trainings starting hopefully - 2656x2656px image generated with 8 steps Fast Qwen LoRA + myself trained LoRA :
Grid test results shared here along with App installer : https://www.patreon.com/posts/137551634
Grid test results shared here along with App installer : https://www.patreon.com/posts/137551634
reacted to
mrs83's
post with 👀
6 months ago
Post
2863
Introducing the Computer Says No Dataset:
ethicalabs/computer-says-no
An LLM can do almost anything, but should it?
This dataset provides clear examples of when LLMs should decline requests, such as:
- Counting characters (e.g., "number of 'r's in 'raspberry'" – seriously, you’ve got this)
- Solving basic equations (like *5.9 = x + 5.11* – please, show that calculator some love)
Inspired by Little Britain's iconic "Computer Says No" sketch, we address a critical issue in AI systems today: the waste of using a rocket launcher to swat flies (aka powerful models for trivial tasks).
Goals:
- Reduce waste by saving compute for tasks that actually need it
- Guide users to better tools
- Spark discussion about ethical AI
This isn’t a training set. It’s a provocation: if we don’t define AI's limits, who will?
An LLM can do almost anything, but should it?
This dataset provides clear examples of when LLMs should decline requests, such as:
- Counting characters (e.g., "number of 'r's in 'raspberry'" – seriously, you’ve got this)
- Solving basic equations (like *5.9 = x + 5.11* – please, show that calculator some love)
Inspired by Little Britain's iconic "Computer Says No" sketch, we address a critical issue in AI systems today: the waste of using a rocket launcher to swat flies (aka powerful models for trivial tasks).
Goals:
- Reduce waste by saving compute for tasks that actually need it
- Guide users to better tools
- Spark discussion about ethical AI
This isn’t a training set. It’s a provocation: if we don’t define AI's limits, who will?
- 9 replies
reacted to
pcuenq's
post with 🔥
6 months ago
Post
10283
OpenELM in Core ML
Apple recently released a set of efficient LLMs in sizes varying between 270M and 3B parameters. Their quality, according to benchmarks, is similar to OLMo models of comparable size, but they required half the pre-training tokens because they use layer-wise scaling, where the number of attention heads increases in deeper layers.
I converted these models to Core ML, for use on Apple Silicon, using this script: https://gist.github.com/pcuenca/23cd08443460bc90854e2a6f0f575084. The converted models were uploaded to this community in the Hub for anyone that wants to integrate inside their apps: corenet-community/openelm-core-ml-6630c6b19268a5d878cfd194
The conversion was done with the following parameters:
- Precision: float32.
- Sequence length: fixed to 128.
With swift-transformers (https://github.com/huggingface/swift-transformers), I'm getting about 56 tok/s with the 270M on my M1 Max, and 6.5 with the largest 3B model. These speeds could be improved by converting to
I'm also looking at optimizing inference using an experimental kv cache in swift-transformers. It's a bit tricky because the layers have varying number of attention heads, but I'm curious to see how much this feature can accelerate performance in this model family :)
Regarding the instruct fine-tuned models, I don't know the chat template that was used. The models use the Llama 2 tokenizer, but the Llama 2 chat template, or the default Alignment Handbook one that was used to train, are not recognized. Any ideas on this welcome!
Apple recently released a set of efficient LLMs in sizes varying between 270M and 3B parameters. Their quality, according to benchmarks, is similar to OLMo models of comparable size, but they required half the pre-training tokens because they use layer-wise scaling, where the number of attention heads increases in deeper layers.
I converted these models to Core ML, for use on Apple Silicon, using this script: https://gist.github.com/pcuenca/23cd08443460bc90854e2a6f0f575084. The converted models were uploaded to this community in the Hub for anyone that wants to integrate inside their apps: corenet-community/openelm-core-ml-6630c6b19268a5d878cfd194
The conversion was done with the following parameters:
- Precision: float32.
- Sequence length: fixed to 128.
With swift-transformers (https://github.com/huggingface/swift-transformers), I'm getting about 56 tok/s with the 270M on my M1 Max, and 6.5 with the largest 3B model. These speeds could be improved by converting to
float16. However, there's some precision loss somewhere and generation doesn't work in float16 mode yet. I'm looking into this and will keep you posted! Or take a look at this issue if you'd like to help: https://github.com/huggingface/swift-transformers/issues/95I'm also looking at optimizing inference using an experimental kv cache in swift-transformers. It's a bit tricky because the layers have varying number of attention heads, but I'm curious to see how much this feature can accelerate performance in this model family :)
Regarding the instruct fine-tuned models, I don't know the chat template that was used. The models use the Llama 2 tokenizer, but the Llama 2 chat template, or the default Alignment Handbook one that was used to train, are not recognized. Any ideas on this welcome!
- 5 replies