This sounds really cool, definitely adding it to my weekend experiment list (kudos for providing the github repo as well).
Also... ShadowPEFT? Badass name.
Cahlen Humphreys PRO
cahlen
AI & ML interests
โ ๏ธ๐ป
Recent Activity
updated a model about 2 hours ago
cahlen/keeloq-neural-distinguishers published a model about 2 hours ago
cahlen/keeloq-neural-distinguishers updated a dataset about 7 hours ago
cahlen/zaremba-densityOrganizations
reacted to SeanLee97's post with ๐๐ฅ 1 day ago
Post
6637
Our lab recently released a paper where we introduce ShadowPEFT, a new Parameter-Efficient Fine-Tuning (PEFT) paradigm tailored for edge computing scenarios.
Unlike traditional approaches such as LoRA and its variants, which inject trainable parameters directly into the weights of Transformer, requiring tight coupling with the backbone.
ShadowPEFT instead enhances the frozen large base model by adding a lightweight, centralized, pretrainable, and detachable Shadow network.
This shadow network operates in parallel with the base model, delivering learned corrections to each decoder layer. Because the shadow module is architecturally decoupled from the backbone, it can be independently trained, stored, and deployed, benefiting edge computing scenarios and edge-cloud collaboration computing.
- HF Paper: ShadowPEFT: Shadow Network for Parameter-Efficient Fine-Tuning (2604.19254)
- GitHub: https://github.com/ShadowLLM/shadow-peft
- HF Collection: https://huggingface.co/collections/shadow-llm/shadow-peft-models
Unlike traditional approaches such as LoRA and its variants, which inject trainable parameters directly into the weights of Transformer, requiring tight coupling with the backbone.
ShadowPEFT instead enhances the frozen large base model by adding a lightweight, centralized, pretrainable, and detachable Shadow network.
This shadow network operates in parallel with the base model, delivering learned corrections to each decoder layer. Because the shadow module is architecturally decoupled from the backbone, it can be independently trained, stored, and deployed, benefiting edge computing scenarios and edge-cloud collaboration computing.
- HF Paper: ShadowPEFT: Shadow Network for Parameter-Efficient Fine-Tuning (2604.19254)
- GitHub: https://github.com/ShadowLLM/shadow-peft
- HF Collection: https://huggingface.co/collections/shadow-llm/shadow-peft-models
- 7 replies
posted an update 2 days ago
Post
157
In the spirit of "just making shit"โbecause, frankly, you can these daysโI decided to get back to basics. I built a lightweight RL MLP powered by WebGPU that runs directly in your browser.
The twist? I replaced the standard MLP with a Continued Fractions network. The real win here is interpretability; by applying a Taylor expansion to the continued fractions, we can actually decompose the output and see exactly which features influenced the outcome. It effectively replaces the usual "itโs just black-box magic" with actual visibility into the logic.
It was a fun experiment. Feel free to clone the repo and make it your own!
cahlen/neuron-runner
https://github.com/cahlen/neuron-runner
CoFrGeNet: Continued Fraction Architectures for Language Generation (2601.21766)
The twist? I replaced the standard MLP with a Continued Fractions network. The real win here is interpretability; by applying a Taylor expansion to the continued fractions, we can actually decompose the output and see exactly which features influenced the outcome. It effectively replaces the usual "itโs just black-box magic" with actual visibility into the logic.
It was a fun experiment. Feel free to clone the repo and make it your own!
cahlen/neuron-runner
https://github.com/cahlen/neuron-runner
CoFrGeNet: Continued Fraction Architectures for Language Generation (2601.21766)
reacted to imnotkitty's post with ๐๐๐ค๐โค๏ธ๐๐ฅ 6 days ago
Post
2583
Just tried tencent/HY-World-2.0 โ a multimodal world model that takes in text or a single image and generates editable 3D scenes.
Unlike Google's Genie and HY-World 1.5, v2.0 generates engine-ready 3D content:
๐ฎ Direct import into Unreal Engine and Unity โ no format wrangling
๐ง Supports multiple 3D asset formats: Mesh, 3DGS, point cloud, etc.
โ๏ธ Fully editable โ not a baked video, but actual geometry you can modify
๐ค Also usable for embodied simulation environments
Basically: from "AI generates a world you can look at" โ "AI generates a world you can ship."
Unlike Google's Genie and HY-World 1.5, v2.0 generates engine-ready 3D content:
๐ฎ Direct import into Unreal Engine and Unity โ no format wrangling
๐ง Supports multiple 3D asset formats: Mesh, 3DGS, point cloud, etc.
โ๏ธ Fully editable โ not a baked video, but actual geometry you can modify
๐ค Also usable for embodied simulation environments
Basically: from "AI generates a world you can look at" โ "AI generates a world you can ship."
its a really great model. i just got done evaluating it for the past week. the long term agentic loops really really help in autonomous app building, especially the recursive fixing. sometimes i neglect the fact that a bigger model is just.. .well.. better usually.
very good model. now if we can't just figure out how to squeeze it all down to an rtx 5090 possibly? hehe
reacted to victor's post with ๐ฅ 9 days ago
Post
4898
Want to share my enthusiasm for zai-org/GLM-5.1 here too ๐ฅ
I think we have it: our open source Claude Code = GLM-5.1 + Pi (https://pi.dev/) - Built a Three.js racing game to eval and it's extremely impressive. Thoughts:
- One-shot car physics with real drift mechanics (this is hard)
- My fav part: Awesome at self iterating (with no vision!) created 20+ Bun.WebView debugging tools to drive the car programmatically and read game state. Proved a winding bug with vector math without ever seeing the screen
- 531-line racing AI in a single write: 4 personalities, curvature map, racing lines, tactical drifting. Built telemetry tools to compare player vs AI speed curves and data-tuned parameters
- All assets from scratch: 3D models, procedural textures, sky shader, engine sounds, spatial AI audio!
- Can do hard math: proved road normals pointed DOWN via vector cross products, computed track curvature normalized by arc length to tune AI cornering speed
You are going to hear about this model a lot in the next months - open source let's go - and thanks z-ai๐๐
I think we have it: our open source Claude Code = GLM-5.1 + Pi (https://pi.dev/) - Built a Three.js racing game to eval and it's extremely impressive. Thoughts:
- One-shot car physics with real drift mechanics (this is hard)
- My fav part: Awesome at self iterating (with no vision!) created 20+ Bun.WebView debugging tools to drive the car programmatically and read game state. Proved a winding bug with vector math without ever seeing the screen
- 531-line racing AI in a single write: 4 personalities, curvature map, racing lines, tactical drifting. Built telemetry tools to compare player vs AI speed curves and data-tuned parameters
- All assets from scratch: 3D models, procedural textures, sky shader, engine sounds, spatial AI audio!
- Can do hard math: proved road normals pointed DOWN via vector cross products, computed track curvature normalized by arc length to tune AI cornering speed
You are going to hear about this model a lot in the next months - open source let's go - and thanks z-ai๐๐
- 4 replies
posted an update 9 days ago
Post
2341
Huggingface just enabled cuda kernel repos!! This is crazy cool!
Expect a ton more portable number theory cuda kernels in the near future. I'm going to have a hell of a lot of fun with this new feature.
Appreciate it huggingface!
https://huggingface.co/kernels
Expect a ton more portable number theory cuda kernels in the near future. I'm going to have a hell of a lot of fun with this new feature.
Appreciate it huggingface!
https://huggingface.co/kernels
- 1 reply
reacted to anakin87's post with โค๏ธ 13 days ago
Post
3293
๐ฃ I just published a free course on Reinforcement Learning Environments for Language Models!
๐ COURSE: https://github.com/anakin87/llm-rl-environments-lil-course
Over the past year, we've seen a shift in LLM Post-Training.
Previously, Supervised Fine-Tuning was the most important part: making models imitate curated Question-Answer pairs.
Now we also have Reinforcement Learning with Verifiable Rewards. With techniques like GRPO, models can learn through trial and error in dynamic environments. They can climb to new heights without relying on expensively prepared data.
But what actually are these environments in practiceโ And how do you build them effectivelyโ
Fascinated by these concepts, I spent time exploring this space through experiments, post-training Small Language Models.
I've packaged everything I learned into this short course.
What you'll learn
๐น Agents, Environments, and LLMs: how to map Reinforcement Learning concepts to the LLM domain
๐น How to use Verifiers (open-source library by Prime Intellect) to build RL environments as software artifacts
๐น Common patterns: How to build single-turn, multi-turn, and tool-use environments
๐น Hands-on: turn a small language model (LFM2-2.6B by LiquidAI) into a Tic Tac Toe master
๐ธ Build the game Environment
๐ธ Use it to generate synthetic data for SFT warm-up
๐ธ Group-based Reinforcement Learning
If you're interested in building "little worlds" where LLMs can learn, this course is for you.
---
๐ค๐น๏ธ Play against the trained model: anakin87/LFM2-2.6B-mr-tictactoe
๐ HF collection (datasets + models): https://huggingface.co/collections/anakin87/lfm2-26b-mr-tic-tac-toe
๐ COURSE: https://github.com/anakin87/llm-rl-environments-lil-course
Over the past year, we've seen a shift in LLM Post-Training.
Previously, Supervised Fine-Tuning was the most important part: making models imitate curated Question-Answer pairs.
Now we also have Reinforcement Learning with Verifiable Rewards. With techniques like GRPO, models can learn through trial and error in dynamic environments. They can climb to new heights without relying on expensively prepared data.
But what actually are these environments in practiceโ And how do you build them effectivelyโ
Fascinated by these concepts, I spent time exploring this space through experiments, post-training Small Language Models.
I've packaged everything I learned into this short course.
What you'll learn
๐น Agents, Environments, and LLMs: how to map Reinforcement Learning concepts to the LLM domain
๐น How to use Verifiers (open-source library by Prime Intellect) to build RL environments as software artifacts
๐น Common patterns: How to build single-turn, multi-turn, and tool-use environments
๐น Hands-on: turn a small language model (LFM2-2.6B by LiquidAI) into a Tic Tac Toe master
๐ธ Build the game Environment
๐ธ Use it to generate synthetic data for SFT warm-up
๐ธ Group-based Reinforcement Learning
If you're interested in building "little worlds" where LLMs can learn, this course is for you.
---
๐ค๐น๏ธ Play against the trained model: anakin87/LFM2-2.6B-mr-tictactoe
๐ HF collection (datasets + models): https://huggingface.co/collections/anakin87/lfm2-26b-mr-tic-tac-toe
- 1 reply
reacted to ArtelTaleb's post with โค๏ธโ๐๐ 13 days ago
Post
2165
8VIEW AI Studio - Turn ideas into clean topology reference sheets for 3D/animation
What if you could generate production-ready topology references in seconds?
8VIEW AI Studio is built for character artists who care about clean edge flow, structure, and consistency - not just pretty images.
Upload an image or few and instantly get 8-view orthographic sheets with topology guides you can actually use in your workflow.
Why it matters
Most AI tools stop at visuals.
But real 3D work starts with topology.
-clean edge loops
-correct pole placement
-usable structure for sculpting & retopo
8VIEW bridges the gap between AI generation and real production workflows.
Features
8-view sheets
Front / back / side / 3โ4 / Top / Bottom
Multilingual (9 languages)
English, Franรงais, Espaรฑol, Portuguรชs, Italiano, เคนเคฟเคจเฅเคฆเฅ, ๆฅๆฌ่ช, ุงูุนุฑุจูุฉ, ไธญๆ
Mobile-friendly - Create anywhere, anytime
Your key, your data - No storage. No tracking. Fully client-side
Built for
โข Character artists (Blender, ZBrush, Maya)
โข Students learning topology & retopology
โข Concept artists needing fast structure
โข Game artists building production assets
Workflow
Get a free Gemini API key (2 minutes)
Paste it into the app
Upload or describe your subject
Generate clean topology reference sheets
The goal
Less time guessing topology.
More time building clean models.
ArtelTaleb/8view-ai-studio
With Love ! A3
What if you could generate production-ready topology references in seconds?
8VIEW AI Studio is built for character artists who care about clean edge flow, structure, and consistency - not just pretty images.
Upload an image or few and instantly get 8-view orthographic sheets with topology guides you can actually use in your workflow.
Why it matters
Most AI tools stop at visuals.
But real 3D work starts with topology.
-clean edge loops
-correct pole placement
-usable structure for sculpting & retopo
8VIEW bridges the gap between AI generation and real production workflows.
Features
8-view sheets
Front / back / side / 3โ4 / Top / Bottom
Multilingual (9 languages)
English, Franรงais, Espaรฑol, Portuguรชs, Italiano, เคนเคฟเคจเฅเคฆเฅ, ๆฅๆฌ่ช, ุงูุนุฑุจูุฉ, ไธญๆ
Mobile-friendly - Create anywhere, anytime
Your key, your data - No storage. No tracking. Fully client-side
Built for
โข Character artists (Blender, ZBrush, Maya)
โข Students learning topology & retopology
โข Concept artists needing fast structure
โข Game artists building production assets
Workflow
Get a free Gemini API key (2 minutes)
Paste it into the app
Upload or describe your subject
Generate clean topology reference sheets
The goal
Less time guessing topology.
More time building clean models.
ArtelTaleb/8view-ai-studio
With Love ! A3
reacted to Parveshiiii's post with ๐ฅ 14 days ago
Post
1609
Excited to announce my latest open-source release on Hugging Face: Parveshiiii/breast-cancer-detector.
This model has been trained and validated on external datasets to support medical research workflows. It is designed to provide reproducible benchmarks and serve as a foundation for further exploration in healthcare AI.
Key highlights:
- Built for medical research and diagnostic study contexts
- Validated against external datasets for reliability
- Openly available to empower the community in building stronger, more effective solutions
This release is part of my ongoing effort to make impactful AI research accessible through **Modotte**. A detailed blog post explaining the methodology, dataset handling, and validation process will be published soon.
You can explore the model here: Parveshiiii/breast-cancer-detector
#AI #MedicalResearch #DeepLearning #Healthcare #OpenSource #HuggingFace
This model has been trained and validated on external datasets to support medical research workflows. It is designed to provide reproducible benchmarks and serve as a foundation for further exploration in healthcare AI.
Key highlights:
- Built for medical research and diagnostic study contexts
- Validated against external datasets for reliability
- Openly available to empower the community in building stronger, more effective solutions
This release is part of my ongoing effort to make impactful AI research accessible through **Modotte**. A detailed blog post explaining the methodology, dataset handling, and validation process will be published soon.
You can explore the model here: Parveshiiii/breast-cancer-detector
#AI #MedicalResearch #DeepLearning #Healthcare #OpenSource #HuggingFace