Hugging Face Changelog
Filter Leaderboards by Model Size
• 139
🤝 Open to Collab
Danny
TheDrunkenSnail
AI & ML interests
None yet
Recent Activity
upvoted a changelog about 1 month ago
Filter Leaderboards by Model Size upvoted a changelog about 1 month ago
Filter Models page by Base Models only reacted to salma-remyx's post with 🔥 about 2 months ago
The space of possible improvements for your AI model is large while evaluation is costly.
So I was excited to discover the ICML 2026 paper from Kobalczyk, Lin, Letham, Zhao, Balandat, and Bakshy titled "LILO: Bayesian Optimization with Natural Language Feedback."
The method learns efficiently from expert preferences, balancing exploration and exploitation in a principled way with Bayesian Optimization for expensive-to-evaluate black-box objectives.
Experimenting with the technique, I trained a Gaussian Process proxy model on the implicit preferences in my code repo's commit history at VQASynth.
The result: I used the model's preference scores to re-rank candidate papers recommended based on my interests in spatial reasoning and multimodal data synthesis.
Semantic relevance is a high-recall method for finding arXiv papers personalized to your interests. Adding contributor preferences, extracted from the merge history of your code offers a high-precision filter.
So what's next? I'm using the model to synthesize a larger volume of preference data to finetune an open-weight coding model with DPO and LoRA. Tuning Coding Agents via Implicit Preference Distillation
arXiv: https://arxiv.org/pdf/2510.17671
Substack: https://remyxai.substack.com/p/lilo-and-myx
VQASynth: https://github.com/remyxai/VQASynthOrganizations
upvoted 2
changelogs about 1 month ago
Hugging Face Changelog
Filter Models page by Base Models only
• 180
reacted to salma-remyx's post with 🔥 about 2 months ago
Post
11605
The space of possible improvements for your AI model is large while evaluation is costly.
So I was excited to discover the ICML 2026 paper from Kobalczyk, Lin, Letham, Zhao, Balandat, and Bakshy titled "LILO: Bayesian Optimization with Natural Language Feedback."
The method learns efficiently from expert preferences, balancing exploration and exploitation in a principled way with Bayesian Optimization for expensive-to-evaluate black-box objectives.
Experimenting with the technique, I trained a Gaussian Process proxy model on the implicit preferences in my code repo's commit history at VQASynth.
The result: I used the model's preference scores to re-rank candidate papers recommended based on my interests in spatial reasoning and multimodal data synthesis.
Semantic relevance is a high-recall method for finding arXiv papers personalized to your interests. Adding contributor preferences, extracted from the merge history of your code offers a high-precision filter.
So what's next? I'm using the model to synthesize a larger volume of preference data to finetune an open-weight coding model with DPO and LoRA. Tuning Coding Agents via Implicit Preference Distillation
arXiv: https://arxiv.org/pdf/2510.17671
Substack: https://remyxai.substack.com/p/lilo-and-myx
VQASynth: https://github.com/remyxai/VQASynth
So I was excited to discover the ICML 2026 paper from Kobalczyk, Lin, Letham, Zhao, Balandat, and Bakshy titled "LILO: Bayesian Optimization with Natural Language Feedback."
The method learns efficiently from expert preferences, balancing exploration and exploitation in a principled way with Bayesian Optimization for expensive-to-evaluate black-box objectives.
Experimenting with the technique, I trained a Gaussian Process proxy model on the implicit preferences in my code repo's commit history at VQASynth.
The result: I used the model's preference scores to re-rank candidate papers recommended based on my interests in spatial reasoning and multimodal data synthesis.
Semantic relevance is a high-recall method for finding arXiv papers personalized to your interests. Adding contributor preferences, extracted from the merge history of your code offers a high-precision filter.
So what's next? I'm using the model to synthesize a larger volume of preference data to finetune an open-weight coding model with DPO and LoRA. Tuning Coding Agents via Implicit Preference Distillation
arXiv: https://arxiv.org/pdf/2510.17671
Substack: https://remyxai.substack.com/p/lilo-and-myx
VQASynth: https://github.com/remyxai/VQASynth
- 1 reply
upvoted an article 3 months ago
Article
TRL v1.0: Post-Training Library Built to Move with the Field
- +2
qgallouedec, stevhliu, pcuenq, sergiopaniego
• • 58 reacted to DedeProGames's post with 🔥 3 months ago
Post
3061
🔥 GRM2 - The small one that surpasses the big ones.
What if a 3-parameter model can beat a 32-parameter model in every benchmark? We prove that it can.
GRM2 is a 3b params model based on the llama architecture, trained for long reasoning and high performance in complex tasks - the first 3b params model to outperform qwen3-32b in ALL benchmarks, and outperform o3-mini in almost all benchmarks.
🤗 Model: OrionLLM/GRM2-3b
The first 3b params model to generate over 1000 lines of code and achieve a score of 39.0 in xBench-DeepSearch-2510.
🚀 Chat with GRM:
https://huggingface.co/spaces/DedeProGames/GRM2-Chat
🏆 Download official GGUFs: OrionLLM/GRM2-3b-GGUF
What if a 3-parameter model can beat a 32-parameter model in every benchmark? We prove that it can.
GRM2 is a 3b params model based on the llama architecture, trained for long reasoning and high performance in complex tasks - the first 3b params model to outperform qwen3-32b in ALL benchmarks, and outperform o3-mini in almost all benchmarks.
🤗 Model: OrionLLM/GRM2-3b
The first 3b params model to generate over 1000 lines of code and achieve a score of 39.0 in xBench-DeepSearch-2510.
🚀 Chat with GRM:
https://huggingface.co/spaces/DedeProGames/GRM2-Chat
🏆 Download official GGUFs: OrionLLM/GRM2-3b-GGUF
upvoted a changelog 3 months ago
Hugging Face Changelog
Storage Buckets for Spaces
• 144
published 2
models 5 months ago
reacted to darkc0de's post with 🔥 5 months ago
Post
12833
1440GB of VRAM is incredibly satisfying 😁
- 17 replies
reacted to aiconta's post with 👀 7 months ago
Post
5441
hello, who can help me setup a local LLM and RAG for my job i can pay
- 11 replies
liked a
model 7 months ago
liked a
model 11 months ago
reacted to AtAndDev's post with 🔥🚀 12 months ago
Post
716
Qwen 3 Coder is a personal attack to k2, and I love it.
It achieves near SOTA on LCB while not having reasoning.
Finally people are understanding that reasoning isnt necessary for high benches...
Qwen ftw!
DECENTRALIZE DECENTRALIZE DECENTRALIZE
It achieves near SOTA on LCB while not having reasoning.
Finally people are understanding that reasoning isnt necessary for high benches...
Qwen ftw!
DECENTRALIZE DECENTRALIZE DECENTRALIZE
reacted to Wauplin's post with 🔥 12 months ago
Post
3721
Say hello to
We are glad to announce a long-awaited quality-of-life improvement: the Hugging Face CLI has been officially renamed from huggingface-cli to hf!
So... why this change?
Typing huggingface-cli constantly gets old fast. More importantly, the CLI’s command structure became messy as new features were added over time (upload, download, cache management, repo management, etc.). Renaming the CLI is a chance to reorganize commands into a clearer, more consistent format.
We decided not to reinvent the wheel and instead follow a well-known CLI pattern: hf <resource> <action>. Isn't
The full rationale, implementation details, and migration notes are in the blog post: https://huggingface.co/blog/hf-cli
hf: a faster, friendlier Hugging Face CLI ✨We are glad to announce a long-awaited quality-of-life improvement: the Hugging Face CLI has been officially renamed from huggingface-cli to hf!
So... why this change?
Typing huggingface-cli constantly gets old fast. More importantly, the CLI’s command structure became messy as new features were added over time (upload, download, cache management, repo management, etc.). Renaming the CLI is a chance to reorganize commands into a clearer, more consistent format.
We decided not to reinvent the wheel and instead follow a well-known CLI pattern: hf <resource> <action>. Isn't
hf auth login easier to type and remember?The full rationale, implementation details, and migration notes are in the blog post: https://huggingface.co/blog/hf-cli
- 7 replies
liked a
model 12 months ago
reacted to AdinaY's post with 👍 12 months ago
Post
2706
KAT-V1 🔥 a LLM that tackles overthinking by switching between reasoning and direct answers, by Kuaishou.
Kwaipilot/KAT-V1-40B
✨ 40B
✨ Step-SRPO: smarter reasoning control via RL
✨ MTP + Distillation: efficient training, lower cost
Kwaipilot/KAT-V1-40B
✨ 40B
✨ Step-SRPO: smarter reasoning control via RL
✨ MTP + Distillation: efficient training, lower cost
reacted to blaise-tk's post with 🚀 about 1 year ago
Post
4653
A few months ago, I shared that I was building with @deeivihh something like "the Steam for open source apps"...
🚀 Today, I’m excited to announce that Dione is now open source and live in public beta!
Our mission is simple: make it easier to discover, use, and contribute to open source applications.
🔗 GitHub: https://github.com/dioneapp/dioneapp
💬 Join the community: https://discord.gg/JDFJp33vrM
Want to give it a try? I’d love your feedback! 👀
🚀 Today, I’m excited to announce that Dione is now open source and live in public beta!
Our mission is simple: make it easier to discover, use, and contribute to open source applications.
🔗 GitHub: https://github.com/dioneapp/dioneapp
💬 Join the community: https://discord.gg/JDFJp33vrM
Want to give it a try? I’d love your feedback! 👀
reacted to drwlf's post with ❤️🤗 about 1 year ago
Post
5896
Having an insanely good medical LLM is pointless if it won’t answer your questions!
So we’ve made 2 notebook for abliterating any model in order to achieve a good model that will actually help you!
The notebooks are made using @mlabonne ‘s abliteration logic and datasets!
Feel free to use them and happy training 😊
https://github.com/dralexlup/LLM-Abliteration
So we’ve made 2 notebook for abliterating any model in order to achieve a good model that will actually help you!
The notebooks are made using @mlabonne ‘s abliteration logic and datasets!
Feel free to use them and happy training 😊
https://github.com/dralexlup/LLM-Abliteration
- 3 replies