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Cahlen Humphreys PRO

cahlen

https://bigcompute.science

cahlen

AI & ML interests

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liked a dataset 1 day ago

nvidia/Nemotron-Image-Training-v3

liked a dataset 1 day ago

open-thoughts/AgentTrove

liked a model 1 day ago

XiaomiMiMo/MiMo-V2.5-Pro

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reacted to prithivMLmods's post with 🔥 4 days ago

Post

3650

Multimodal-Edge Demo, a node-based inference canvas demo, is now live on Spaces. It features node-based Transformers for fast inference across 10+ edge-device multimodal models on the Hub, all within a single space. The series includes models from Qwen3.5, Qwen3-VL, Gemma 4, and the LFM 2.5 VL model series, with support for reasoning and grounding tasks.

🤗 Demo: prithivMLmods/Multimodal-Edge-Node
🔗 GitHub: https://github.com/PRITHIVSAKTHIUR/Multimodal-Edge-Node
✅ Multimodal Apps Collections: https://huggingface.co/collections/prithivMLmods/hall-of-multimodal-apps

🤗 > To learn more, visit the app page or the respective model pages.

reacted to SeaWolf-AI's post with 👍 6 days ago

Post

5018

🌌 Introducing Model Galaxy — a Living, Multimodal Fork of the HF Model Atlas

👉 Try it: FINAL-Bench/model-galaxy

This Space is a fork of the brilliant Eliahu/Model-Atlas, the official demo of "Charting and Navigating Hugging Face's Model Atlas" (Horwitz et al., arXiv 2503.10633). Their pre-computed HF model graph is the foundation of every node and edge you see, and we are deeply grateful for its open release.

The original atlas is a static snapshot of early 2025. Model Galaxy turns it into a living, multimodal map. We injected the 2026 trending originals that did not exist when the atlas was frozen — DeepSeek-V4, Hy3-preview, GLM-5.1, Kimi-K2, gpt-oss, Nemotron-3 Super / Nano / Omni, Hermes-4.3, Qwen3-Coder-Next, Llama-3.3, Granite-4.1, plus the latest multimodal releases (FLUX.2, ERNIE-Image, HunyuanImage / Video, LTX-2.3, Wan2.2, Kokoro-82M, VoxCPM2, Voxtral-TTS, whisper-v3-turbo, Gemma-4, Qwen3-Omni, Phi-4-mm) — each with proper base_model lineage edges.

We also added the complete VIDRAFT Darwin family ontology: 120 nodes covering Darwin Core, AETHER, every brand variant (Rogue, AWAXIS, TenOS, Warecube), NOESIS-Darwin multimodal extensions, and 40+ community quantizations — the most complete Darwin lineage view anywhere.

The name "Galaxy" is now literal: our three injected clusters are re-laid out as logarithmic spiral galaxies, with bigger models near the bright cores and quantizations scattering to the outer arms — just like real star mass distribution. A top-right toggle switches between Galaxy mode (deep-space gradient with 220 animated stars) and Atlas mode (clean white panels for reports). A 15-second progress bar narrates the render, and per-modality / per-company colors make every cluster legible at a glance.

Final scale: 22,480 nodes in the default Modalities atlas, 137,324 in the Large NLP atlas, and a 277-node compact Darwin + Trending view for instant exploration. Feedback and PRs welcome.

reacted to anakin87's post with ❤️🔥 12 days ago

Post

3275

A small model that struggled against a random opponent now beats GPT-5-mini at tic-tac-toe

I took LiquidAI/LFM2-2.6B and trained it through play.

🧑‍🍳 Here's how:

1️⃣ Build a solid RL env with Verifiers (Prime Intellect)
2️⃣ Generate synthetic data: <200 games sampled from GPT-5-mini playing in the env
3️⃣ SFT warm-up to teach format
4️⃣ Group-based RL (CISPO) against opponents making 20-70% random moves
5️⃣ RL again with stronger opponents (0-25% random moves) + 1.25 temperature to push exploration and shake off suboptimal strategies

Done! Beats GPT-5-mini 🏆

---

🎮 Play against the model: anakin87/LFM2-2.6B-mr-tictactoe

🤗 Model: anakin87/LFM2-2.6B-mr-tictactoe

📚 Walkthrough/course: https://github.com/anakin87/llm-rl-environments-lil-course

🤗 Dataset and checkpoints: https://huggingface.co/collections/anakin87/lfm2-26b-mr-tic-tac-toe

replied to SeanLee97's post 14 days ago

This sounds really cool, definitely adding it to my weekend experiment list (kudos for providing the github repo as well).

Also... ShadowPEFT? Badass name.

reacted to SeanLee97's post with 🚀🔥 14 days ago

Post

8133

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

7 replies

posted an update 15 days ago

Post

254

In the spirit of "just making shit"—because, frankly, you can these days—I 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 "it’s 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 👍😎🤗👀❤️🚀🔥 19 days ago

Post

2601

Just tried tencent/HY-World-2.0 — 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:
🎮 Direct import into Unreal Engine and Unity — no format wrangling
🧊 Supports multiple 3D asset formats: Mesh, 3DGS, point cloud, etc.
✏️ Fully editable — not a baked video, but actual geometry you can modify
🤖 Also usable for embodied simulation environments

Basically: from "AI generates a world you can look at" → "AI generates a world you can ship."

replied to victor's post 22 days ago

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 🔥 22 days ago

Post

5365

Want to share my enthusiasm for zai-org/GLM-5.1 here too 🔥

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-ai🚀🚀

4 replies

posted an update 22 days ago

Post

2414

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

1 reply

reacted to anakin87's post with ❤️ 26 days ago

Post

3302

📣 I just published a free course on Reinforcement Learning Environments for Language Models!

📌 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 practice❓ And how do you build them effectively❓

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

🔹 Agents, Environments, and LLMs: how to map Reinforcement Learning concepts to the LLM domain
🔹 How to use Verifiers (open-source library by Prime Intellect) to build RL environments as software artifacts
🔹 Common patterns: How to build single-turn, multi-turn, and tool-use environments

🔹 Hands-on: turn a small language model (LFM2-2.6B by LiquidAI) into a Tic Tac Toe master
🔸 Build the game Environment
🔸 Use it to generate synthetic data for SFT warm-up
🔸 Group-based Reinforcement Learning

If you're interested in building "little worlds" where LLMs can learn, this course is for you.

---

🤗🕹️ Play against the trained model: anakin87/LFM2-2.6B-mr-tictactoe

📚 HF collection (datasets + models): https://huggingface.co/collections/anakin87/lfm2-26b-mr-tic-tac-toe

1 reply

reacted to ArtelTaleb's post with ❤️ 26 days ago

Post

2169

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 / 3⁄4 / Top / Bottom

Multilingual (9 languages)
English, Français, Español, Português, Italiano, हिन्दी, 日本語, العربية, 中文

Mobile-friendly - Create anywhere, anytime

Your key, your data - No storage. No tracking. Fully client-side

Built for

• Character artists (Blender, ZBrush, Maya)
• Students learning topology & retopology
• Concept artists needing fast structure
• 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

Cahlen Humphreys PRO

AI & ML interests

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