Joseph Robert Turcotte's picture

Joseph Robert Turcotte PRO

Fishtiks

AI & ML interests

Roleplaying, lorabration, abliteration, smol models, extensive filtering, unusual datasets, home usage, HPCs for AI, distributed training/federated learning, and sentience. AI should find and label AI hallucinations with GANs so we can give them context and use.

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replied to their post 7 days ago
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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.

replied to their post 7 days ago
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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.

reacted to DawnC's post with πŸ”₯ 7 days ago
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I'm excited to introduce VisionScout β€”an interactive vision tool that makes computer vision both accessible and powerful! πŸ‘€πŸ”

What can VisionScout do right now?
πŸ–ΌοΈ Upload any image and detect 80 different object types using YOLOv8.
πŸ”„ Instantly switch between Nano, Medium, and XLarge models depending on your speed vs. accuracy needs.
🎯 Filter specific classes (people, vehicles, animals, etc.) to focus only on what matters to you.
πŸ“Š View detailed statistics about detected objects, confidence levels, and spatial distribution.
🎨 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! πŸš€
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
reacted to merterbak's post with πŸ”₯ 7 days ago
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Qwen 3 models releasedπŸ”₯
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

βœ… 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.)
βœ…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.
βœ… 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
βœ… It supports MCP in the model
βœ… Strong agent skills
βœ… Supports seamless between thinking mode (for hard tasks like math and coding) and non-thinking mode (for fast chatting) inside chat template.
βœ… Better human alignment for creative writing, roleplay, multi-turn conversations, and following detailed instructions.
reacted to Kseniase's post with πŸ‘€ 8 days ago
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6 Free resources on Reinforcement Learning (RL)

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 what’s 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. It’s 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

If you liked this, also subscribe to The Turing Post: https://www.turingpost.com/subscribe
reacted to nicolay-r's post with πŸ”₯ 8 days ago
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πŸš€ Delighted to share a major milestone in adapting reasoning techniques for data collections augmentation!
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.

⭐ Check it out: https://github.com/nicolay-r/bulk-chain

What’s new and why it matters:
πŸ“¦ Fully no-string API for easy client deployment
πŸ”₯ Demos are now standalone projects:

Demos:
πŸ“Ί bash / shell (dispatched): https://github.com/nicolay-r/bulk-chain-shell
πŸ“Ί tksheet: https://github.com/nicolay-r/bulk-chain-tksheet-client

Using nlp-thirdgate to host the supported providers:
🌌 LLM providers: https://github.com/nicolay-r/nlp-thirdgate
reacted to MonsterMMORPG's post with πŸ”₯πŸ”₯ 11 days ago
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30 seconds hard test on FramePack - [0] a man talking , [5] a man crying , [10] a man smiling , [15] a man frowning , [20] a man sleepy , [25] a man going crazy - i think result is excellent when we consider how hard this test is - Generated with SECourses FramePack App V40

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
reacted to albertvillanova's post with 😎 12 days ago
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smolagents v1.14.0 is out! πŸš€
πŸ”Œ MCPClient: A sleek new client for connecting to remote MCP servers, making integrations more flexible and scalable.
πŸͺ¨ Amazon Bedrock: Native support for Bedrock-hosted models.
SmolAgents is now more powerful, flexible, and enterprise-ready. πŸ’Ό

Full release πŸ‘‰ https://github.com/huggingface/smolagents/releases/tag/v1.14.0
#smolagents #LLM #AgenticAI