Joseph Robert Turcotte PRO
AI & ML interests
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Fishtiks's activity
Xtts
Generate custom speech from text or cloned voice
AiTube2
The Latent Video Platform
HiDream E1 Full
Generate an edited image based on text instructions
HyperCLOVAX-SEED-Text-Instruct-1.5B
Generate answers to text questions
VisualCloze
Generate images based on examples and prompts
Mistral Perflexity AI
Private-BitSix-Mistral-Small-3.1-24B-Instruct-2503




It would be great for HuggingFace to make a distributed processing service like Acurast for whatever people want to process together toward AI. The idea is free, so have at it! You can charge for setting up the connections to process and providing the software, and bring in a lot of traffic from people that can't afford to train their own AI or get big names involved. The inference providers may not be entirely happy, but the tasks processed won't have the same time constraints, bringing them reasonable traffic for fast inference of large models.
AI@home: Make a crypto to track usage and determine how much time and processing credit people have. Incentives from corporations if they use their devices with their permission, like advanced access to software and features. Energy usage tracked and taken into consideration, with people given more credit for using less energy.

I'm always doing BOINC, and Folding@home runs at night, because it produces a lot of heat. So, I'm hoping that I'll be compensated for past efforts with an HPC that runs cool with phase change and liquid to continue to process science, which is a real possibility. As it is, I still have some hours and devices to find use for, but, like Aurora in Argonne, my HPC will process any science for free, which I find an admirable use of any such hardware, which is why I like Aiyara clusters so much as well. I need to find intensive tasks to process. Currently, I do about 3,000 hours a week of BOINC, given all of my Androids. I'd love to see an HPC run through those tasks in an hour.

What can VisionScout do right now?
πΌοΈ Upload any image and detect 80 different object types using YOLOv8.
π Instantly switch between Nano, Medium, and XLarge models depending on your speed vs. accuracy needs.
π― Filter specific classes (people, vehicles, animals, etc.) to focus only on what matters to you.
π View detailed statistics about detected objects, confidence levels, and spatial distribution.
π¨ Enjoy a clean, intuitive interface with responsive design and enhanced visualizations.
What's next?
I'm working on exciting updates:
- Support for more models
- Video processing and object tracking across frames
- Faster real-time detection
- Improved mobile responsiveness
The goal is to build a complete but user-friendly vision toolkit for both beginners and advanced users.
Try it yourself! π
DawnC/VisionScout
I'd love to hear your feedback , what features would you find most useful? Any specific use cases you'd love to see supported?
Give it a try and let me know your thoughts in the comments! Stay tuned for future updates.
#ComputerVision #ObjectDetection #YOLO #MachineLearning #TechForLife

It offers 2 MoE and 6 dense models with following parameter sizes: 0.6B, 1.7B, 4B, 8B, 14B, 30B(MoE), 32B, and 235B(MoE).
Models: Qwen/qwen3-67dd247413f0e2e4f653967f
Blog: https://qwenlm.github.io/blog/qwen3/
Demo: Qwen/Qwen3-Demo
GitHub: https://github.com/QwenLM/Qwen3
β Pre-trained 119 languages(36 trillion tokens) and dialects with strong translation and instruction following abilities. (Qwen2.5 was pre-trained on 18 trillion tokens.)
β Qwen3 dense models match the performance of larger Qwen2.5 models. For example, Qwen3-1.7B/4B/8B/14B/32B perform like Qwen2.5-3B/7B/14B/32B/72B.
β Three stage done while pretraining:
β’ Stage 1: General language learning and knowledge building.
β’ Stage 2: Reasoning boost with STEM, coding, and logic skills.
β’ Stage 3: Long context training
β It supports MCP in the model
β Strong agent skills
β Supports seamless between thinking mode (for hard tasks like math and coding) and non-thinking mode (for fast chatting) inside chat template.
β Better human alignment for creative writing, roleplay, multi-turn conversations, and following detailed instructions.

RL now is where the real action is, it's the engine behind autonomous tech, robots, and the next wave of AI that thinks, moves and solves problems on its own. To stay up to date with whatβs happening in RL, we offer some fresh materials on it:
1. "Reinforcement Learning from Human Feedback" by Nathan Lambert -> https://rlhfbook.com/
It's a short introduction to RLHF, explaining instruction tuning, reward modeling, alignment methods, synthetic data, evaluation, and more
2. "A Course in Reinforcement Learning (2nd Edition)" by Dimitri P. Bertsekas -> https://www.mit.edu/~dimitrib/RLbook.html
Explains dynamic programming (DP) and RL, diving into rollout algorithms, neural networks, policy learning, etc. Itβs packed with solved exercises and real-world examples
3. "Mathematical Foundations of Reinforcement Learning" video course by Shiyu Zhao -> https://www.youtube.com/playlist?list=PLEhdbSEZZbDaFWPX4gehhwB9vJZJ1DNm8
Offers a mathematical yet friendly introduction to RL, covering Bellman Equation, value iteration, Monte Carlo learning, approximation, policy gradient, actor-critic methods, etc.
+ Check out the repo for more: https://github.com/MathFoundationRL/Book-Mathematical-Foundation-of-Reinforcement-Learning
4. "Multi-Agent Reinforcement Learning" by Stefano V. Albrecht, Filippos Christianos, and Lukas SchΓ€fer -> https://www.marl-book.com/
Covers models, core ideas of multi-agent RL (MARL) and modern approaches to combining it with deep learning
5. "Reinforcement Learning: A Comprehensive Overview" by Kevin P. Murphy -> https://arxiv.org/pdf/2412.05265
Explains RL and sequential decision making, covering value-based, policy-gradient, model-based, multi-agent RL methods, RL+LLMs, and RL+inference and other topics
6. Our collection of free courses and books on RL -> https://huggingface.co/posts/Kseniase/884818121094439
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Introducing bulk-chain 1.0.0 -- the first major release of a no-string API for adapting your LLM for Chain-of-Thought alike reasoning over records with large amount of parameters across large datasets.
β Check it out: https://github.com/nicolay-r/bulk-chain
Whatβs new and why it matters:
π¦ Fully no-string API for easy client deployment
π₯ Demos are now standalone projects:
Demos:
πΊ bash / shell (dispatched): https://github.com/nicolay-r/bulk-chain-shell
πΊ tksheet: https://github.com/nicolay-r/bulk-chain-tksheet-client
Using nlp-thirdgate to host the supported providers:
π LLM providers: https://github.com/nicolay-r/nlp-thirdgate

App link and 1-click installers for Windows, RunPod and Massed Compute here : https://www.patreon.com/posts/126855226
I got the prompt using idea from this pull request : https://github.com/lllyasviel/FramePack/pull/218/files
Not exactly same implementation but i think pretty accurate when considering that it is a 30 second 30 fps video at 840p resolution

π MCPClient: A sleek new client for connecting to remote MCP servers, making integrations more flexible and scalable.
πͺ¨ Amazon Bedrock: Native support for Bedrock-hosted models.
SmolAgents is now more powerful, flexible, and enterprise-ready. πΌ
Full release π https://github.com/huggingface/smolagents/releases/tag/v1.14.0
#smolagents #LLM #AgenticAI