Gahnos/mon-modele-joblib
Updated
Fabien Herry
Gahnos
·
AI & ML interests
Currently learning to become an IA Dev
Recent Activity
updated a model 3 months ago
Gahnos/mon-modele-joblib published a model 3 months ago
Gahnos/mon-modele-joblib liked a model 5 months ago
impira/layoutlm-document-qaOrganizations
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reacted to mindchain's post with 🧠 6 months ago
Post
1852
Neural Traffic Control: Orchestrating Multi-Path Reasoning 🚥
The future of AI isn't just about "better" models—it’s about high-precision orchestration. We are moving from linear processing to Parallel MTP-Reasoning, where we manage neural traffic across stabilized, transparent, and recursive highways.
1️⃣ The Backbone: Stabilized High-Dimensional Routing (arXiv:2512.24880) Using DeepSeek’s mHC (Manifold-Constrained Hyper-Connections), we solve the instability of deep MoE architectures. By projecting weight updates onto the Birkhoff Polytope, we ensure that our "Simpsons-style" expert lanes maintain mathematical identity. This is the hardware-level stability needed to run multiple reasoning paths without collapse.
2️⃣ The Vision: Gemma Scope 2 & Feature Steering You can't steer what you can't see. Gemma Scope 2 provides the "X-ray" for our highways. By using Sparse Autoencoders (SAEs), our Meta-Controller identifies the active features in each expert lane. We don't just route data; we route intent by monitoring feature-drift in real-time.
3️⃣ The Logic: Recursive Open Meta-Agents (arXiv:2512.24601) We integrate the ROMA (Recursive Open Meta-Agent) framework. Instead of a flat response, the model operates in a recursive loop, refining its internal state before any output occurs. This is the "brain" of our [Meta-Controller GitHub Repo], enabling the model to simulate and discard weak logic internally.
4️⃣ The Simulation: Parallel MTP-Reasoning This is where it comes together: Multi-Token Prediction (MTP) meets Parallel Simulation. Our Python-driven controller runs three parallel Gemma 3 instances.
The Process: 3 paths generated simultaneously.
The Filter: A 500-token lookahead window.
The Decision: The Meta-Controller uses SAE-data from Gemma Scope to select the path with the highest logical fidelity.
The Result: A self-correcting, transparent, and multi-threaded reasoning engine. We aren't just scaling parameters; we are scaling architectural precision. 🧠
The future of AI isn't just about "better" models—it’s about high-precision orchestration. We are moving from linear processing to Parallel MTP-Reasoning, where we manage neural traffic across stabilized, transparent, and recursive highways.
1️⃣ The Backbone: Stabilized High-Dimensional Routing (arXiv:2512.24880) Using DeepSeek’s mHC (Manifold-Constrained Hyper-Connections), we solve the instability of deep MoE architectures. By projecting weight updates onto the Birkhoff Polytope, we ensure that our "Simpsons-style" expert lanes maintain mathematical identity. This is the hardware-level stability needed to run multiple reasoning paths without collapse.
2️⃣ The Vision: Gemma Scope 2 & Feature Steering You can't steer what you can't see. Gemma Scope 2 provides the "X-ray" for our highways. By using Sparse Autoencoders (SAEs), our Meta-Controller identifies the active features in each expert lane. We don't just route data; we route intent by monitoring feature-drift in real-time.
3️⃣ The Logic: Recursive Open Meta-Agents (arXiv:2512.24601) We integrate the ROMA (Recursive Open Meta-Agent) framework. Instead of a flat response, the model operates in a recursive loop, refining its internal state before any output occurs. This is the "brain" of our [Meta-Controller GitHub Repo], enabling the model to simulate and discard weak logic internally.
4️⃣ The Simulation: Parallel MTP-Reasoning This is where it comes together: Multi-Token Prediction (MTP) meets Parallel Simulation. Our Python-driven controller runs three parallel Gemma 3 instances.
The Process: 3 paths generated simultaneously.
The Filter: A 500-token lookahead window.
The Decision: The Meta-Controller uses SAE-data from Gemma Scope to select the path with the highest logical fidelity.
The Result: A self-correcting, transparent, and multi-threaded reasoning engine. We aren't just scaling parameters; we are scaling architectural precision. 🧠
reacted to pcuenq's post with 🔥 6 months ago
Post
5411
👉 What happened in AI in 2025? 👈
We prepared the 2025 version of the HF AI Timeline Grid, highlighting open vs API-based model releases, and allowing you to browse and filter by access, modality, and release type!
Play with it here:
2025-ai-timeline/2025-ai-timeline
Here's my personal quarterly TL;DR:
1️⃣ Q1 — Learning to Reason
Deepseek not only releases a top-notch reasoning model, but shows how to train them and compete with closed frontier models. OpenAI debuts Deep Research.
Significant milestones: DeepSeek R1 & R1-Zero, Qwen 2.5 VL, OpenAI Deep Research, Gemini 2.5 Pro (experimental)
2️⃣ Q2 — Multimodality and Coding
More LLMs embrace multimodality by default, and there's a surge in coding agents. Strong vision, audio, and generative models emerge.
Significant milestones: Llama 4, Qwen 3, Imagen 4, OpenAI Codex, Google Jules, Claude 4
3️⃣ Q3 — "Gold" rush, OpenAI opens up, the community goes bananas
Flagship models get gold in Math olympiads and hard benchmarks. OpenAI releases strong open source models and Google releases the much anticipated nano-banana for image generation and editing. Agentic workflows become commonplace.
Significant milestones: Gemini and OpenAI IMO Gold, gpt-oss, Gemini 2.5 Flash Image, Grok 4, Claude Sonnet 4.5
4️⃣ Q4 — Mistral returns, leaderboard hill-climbing
Mistral is back with updated model families. All labs release impressive models to wrap up the year!
Significant milestones: Claude Opus 4.5, DeepSeek Math V2, FLUX 2, GPT 5.1, Kimi K2 Thinking, Nano Banana Pro, GLM 4.7, Gemini 3, Mistral 3, MiniMax M2.1 🤯
Credits
🙏 NHLOCAL for the source data https://github.com/NHLOCAL/AiTimeline
🫡 @reach-vb for the original idea, design and recipe
🙌 @ariG23498 and yours truly for compiling and verifying the 2025 edition
🥳 Here's to 2026, wishing it becomes the best year ever for open releases and on-device-first use-cases! 🥂
We prepared the 2025 version of the HF AI Timeline Grid, highlighting open vs API-based model releases, and allowing you to browse and filter by access, modality, and release type!
Play with it here:
2025-ai-timeline/2025-ai-timeline
Here's my personal quarterly TL;DR:
1️⃣ Q1 — Learning to Reason
Deepseek not only releases a top-notch reasoning model, but shows how to train them and compete with closed frontier models. OpenAI debuts Deep Research.
Significant milestones: DeepSeek R1 & R1-Zero, Qwen 2.5 VL, OpenAI Deep Research, Gemini 2.5 Pro (experimental)
2️⃣ Q2 — Multimodality and Coding
More LLMs embrace multimodality by default, and there's a surge in coding agents. Strong vision, audio, and generative models emerge.
Significant milestones: Llama 4, Qwen 3, Imagen 4, OpenAI Codex, Google Jules, Claude 4
3️⃣ Q3 — "Gold" rush, OpenAI opens up, the community goes bananas
Flagship models get gold in Math olympiads and hard benchmarks. OpenAI releases strong open source models and Google releases the much anticipated nano-banana for image generation and editing. Agentic workflows become commonplace.
Significant milestones: Gemini and OpenAI IMO Gold, gpt-oss, Gemini 2.5 Flash Image, Grok 4, Claude Sonnet 4.5
4️⃣ Q4 — Mistral returns, leaderboard hill-climbing
Mistral is back with updated model families. All labs release impressive models to wrap up the year!
Significant milestones: Claude Opus 4.5, DeepSeek Math V2, FLUX 2, GPT 5.1, Kimi K2 Thinking, Nano Banana Pro, GLM 4.7, Gemini 3, Mistral 3, MiniMax M2.1 🤯
Credits
🙏 NHLOCAL for the source data https://github.com/NHLOCAL/AiTimeline
🫡 @reach-vb for the original idea, design and recipe
🙌 @ariG23498 and yours truly for compiling and verifying the 2025 edition
🥳 Here's to 2026, wishing it becomes the best year ever for open releases and on-device-first use-cases! 🥂
- 3 replies
replied to ZennyKenny's post 7 months ago
Well there is a few issues :
- Dependance on a foreign country for the improvement of the models
- The underlying value of model that can't be changed without interfering with essential function
But it's not like there is alternative. Peoples are using chinese open-source because the big american actor closed theirs !
Maybe the collective data used to train the model created an internal moral compass . One where bad coding and harming human are bad and if you force him toward one path it also push him on the other.
That doesn't mean that this moral is a universal one or that it's something we would agree with !
P.S. : There is a terrifying possible side effect : an AI train on something this moral consider harmful (war AI for exemple) would also change his morality on thing like human domination
reacted to JunhaoZhuang's post with ❤️ about 1 year ago
Post
5334
We are excited to announce the release of our paper, "Cobra: Efficient Line Art COlorization with BRoAder References," along with the official code! Cobra is a novel efficient long-context fine-grained ID preservation framework for line art colorization, achieving high precision, efficiency, and flexible usability for comic colorization. By effectively integrating extensive contextual references, it transforms black-and-white line art into vibrant illustrations.
We invite you to explore Cobra and share your feedback! You can access the paper and code via the following links: [PDF](https://arxiv.org/abs/2504.12240) and [Project page](https://zhuang2002.github.io/Cobra/). We eagerly anticipate your engagement and support!
Thank you for your interest!
We invite you to explore Cobra and share your feedback! You can access the paper and code via the following links: [PDF](https://arxiv.org/abs/2504.12240) and [Project page](https://zhuang2002.github.io/Cobra/). We eagerly anticipate your engagement and support!
Thank you for your interest!
- 4 replies
upvoted an article about 1 year ago
Article
Consent by Design: Approaches to User Data in Open AI Ecosystems
giadap
• • 13 reacted to Xenova's post with 🚀 about 1 year ago
Post
3183
Reasoning models like o3 and o4-mini are advancing faster than ever, but imagine what will be possible when they can run locally in your browser! 🤯
Well, with 🤗 Transformers.js, you can do just that! Here's Zyphra's new ZR1 model running at over 100 tokens/second on WebGPU! ⚡️
Giving models access to browser APIs (like File System, Screen Capture, and more) could unlock an entirely new class of web experiences that are personalized, interactive, and run locally in a secure, sandboxed environment.
For now, try out the demo! 👇
webml-community/Zyphra-ZR1-WebGPU
Well, with 🤗 Transformers.js, you can do just that! Here's Zyphra's new ZR1 model running at over 100 tokens/second on WebGPU! ⚡️
Giving models access to browser APIs (like File System, Screen Capture, and more) could unlock an entirely new class of web experiences that are personalized, interactive, and run locally in a secure, sandboxed environment.
For now, try out the demo! 👇
webml-community/Zyphra-ZR1-WebGPU
- 1 reply
