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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Fishtiks's activity

reacted to merterbak's post with 🔥 about 9 hours ago
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2055
Seed-Coder released and it's designed for coding tasks, featuring base, instruct, and reasoning variants at an 8B parameter scale developed by ByteDance Seed team. Unlike traditional open source LLMs that rely on human crafted rules or annotated data for curating code pretraining datasets Seed-Coder introduces a model-centric data pipeline. The pipeline processes raw data from GitHub and web archives into four categories: file-level codes, repository-level codes, GitHub commits, and code-related web data.A quality filter LLM, evaluates code (for readability, modularity, clarity, and reusability) by removing the lowest 10% to create a 6 trillion token dataset supporting 89 programming languages.
Models: ByteDance-Seed/seed-coder-680de32c15ead6555c75b0e4
Github: https://github.com/ByteDance-Seed/Seed-Coder/tree/master
Paper: https://github.com/ByteDance-Seed/Seed-Coder/blob/master/Seed-Coder.pdf
reacted to prithivMLmods's post with 👍 3 days ago
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3295
Dropping some image classification models for content moderation, balancers, and classifiers trained on synthetic datasets—along with others based on datasets available on the Hub. Also loaded a few low-rank datasets for realistic gender portrait classification and document-type classifiers, all fine-tuned on the SigLIP-2 Patch-16 224 backbone. Models and datasets are listed below:

🤗Models & Datasets :

Realistic Gender Classification : prithivMLmods/Realistic-Gender-Classification
prithivMLmods/Realistic-Portrait-Gender-1024px
Document Type Detection : prithivMLmods/Document-Type-Detection
prithivMLmods/Document-Type-Detection
Face Mask Detection : prithivMLmods/Face-Mask-Detection
DamarJati/Face-Mask-Detection
Alzheimer Stage Classifier : prithivMLmods/Alzheimer-Stage-Classifier
SilpaCS/Augmented_alzheimer
Bone Fracture Detection : prithivMLmods/Bone-Fracture-Detection
Hemg/bone-fracture-detection
GiD Land Cover Classification : prithivMLmods/GiD-Land-Cover-Classification
jonathan-roberts1/GID

🤗Collection : prithivMLmods/siglip2-05102025-681c2b0e406f0740a993fc1c

To know more about it, visit the model card of the respective model.
reacted to YerbaPage's post with 🔥 3 days ago
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2093
Curated list of **Next-Gen Code Generation** papers & benchmarks! 🔥

Stay ahead with the latest in:
✅ Repo-level Issue Resolution (SWE-bench, Agents)
✅ Repo-level Code Completion (Repo understanding)
✅ Datasets & Benchmarks

👉 Check it out: https://github.com/YerbaPage/Awesome-Repo-Level-Code-Generation 🔥
reacted to MonsterMMORPG's post with 👀 3 days ago
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2840
TRELLIS is still the lead Open Source AI model to generate high-quality 3D Assets from static images — Some mind blowing examples — Supports multi-angle improved image to 3D as well — Works as low as 6 GB GPUs


Tutorial link : https://www.youtube.com/watch?v=EhU7Jil9WAk

App Link : https://www.patreon.com/posts/Trellis-App-Installer-Zip-File-117470976

Our app is super advanced with so many features and supports as low as 6 GB GPUs

Also fully supports RTX 5000 GPUs as well

TRELLIS is currently the state of the art locally run-able open source image-to-3D very high quality asset generator. I have developed a 1-click installers and super advanced Gradio app for this model with so many amazing features. In this tutorial video I will show you how to step by step use this amazing AI tool and generate the very best very high-quality 3D assets locally. Moreover, you can also use this tool on RunPod and Massed Compute as well if you are GPU poor.

🔗Follow below link to download the zip file that contains Trellis installer and Gradio App - the one used in the tutorial ⤵️
▶️ https://www.patreon.com/posts/Trellis-App-Installer-Zip-File-117470976

🔗 Python, Git, CUDA, C++ Tools, FFmpeg, cuDNN, MSVC installation tutorial - needed for AI apps - 1-time only setup⤵️
▶️ https://youtu.be/DrhUHnYfwC0

🔗 SECourses Official Discord 10500+ Members ⤵️
▶️ https://discord.com/servers/software-engineering-courses-secourses-772774097734074388

🔗 Stable Diffusion, FLUX, Generative AI Tutorials and Resources GitHub ⤵️
▶️ https://github.com/FurkanGozukara/Stable-Diffusion

🔗 SECourses Official Reddit - Stay Subscribed To Learn All The News and More ⤵️
▶️ https://www.reddit.com/r/SECourses/

🔗Official TRELLIS Repo ⤵️
▶️ https://github.com/microsoft/TRELLIS
reacted to onekq's post with 🚀 3 days ago
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2229
The new Mistral medium model is very impressive for its size. Will it be open sourced given the history of Mistral? Does anyone have insights?

onekq-ai/WebApp1K-models-leaderboard
reacted to nomadicsynth's post with 👍🔥 5 days ago
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2151
I Did a Thing!

I made an embedding model to find answers in research papers. It goes deeper than plain "semantic search" by identifying deeply reasoned connections and interdisciplinary insights that might have been overlooked. The goal is to find the solutions that might have been missed and to uncover answers that are already out there.

I’ve set up a demo Space - nomadicsynth/inkling . It’s early days, and I’d love some feedback on the model’s results. Try it out and let me know what you think!

Oh, and if it finds your Nobel-winning answer, I want a cut! 😉
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reacted to sequelbox's post with 👀 5 days ago
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2638
NEW RELEASE: Esper 3 for Qwen 3!

- A full-stack software assistant: a reasoning finetune focused on coding, architecture, and DevOps using the Titanium and Tachibana datasets!
- Improved general and creative reasoning skills, powered by the Raiden dataset.

4B model: ValiantLabs/Qwen3-4B-Esper3
8B model: ValiantLabs/Qwen3-8B-Esper3

We'll also be bringing Esper 3 to larger Qwen 3 models as soon as we can - if you want these, consider helping us out: sequelbox/SupportOpenSource

More models and datasets to come soon!

with my love and enthusiasm,
allegra
reacted to AdinaY's post with 😎 7 days ago
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3873
ACE-Step 🎵 a music generation foundation model released by
StepFun & ACEStudio

Model: ACE-Step/ACE-Step-v1-3.5B
Demo: ACE-Step/ACE-Step

✨ 3.5B, Apache2.0 licensed
✨ 115× faster than LLMs (4-min music in 20s on A100)
✨ Diffusion + DCAE + linear transformer = speed + coherence
✨ Supports voice cloning, remixing, lyric editing & more
  • 1 reply
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reacted to merve's post with 🚀 7 days ago
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6448
A real-time object detector much faster and accurate than YOLO with Apache 2.0 license just landed to Hugging Face transformers 🔥

D-FINE is the sota real-time object detector that runs on T4 (free Colab) 🤩

> Collection with all checkpoints and demo ustc-community/d-fine-68109b427cbe6ee36b4e7352

Notebooks:
> Tracking https://github.com/qubvel/transformers-notebooks/blob/main/notebooks/DFine_tracking.ipynb
> Inference https://github.com/qubvel/transformers-notebooks/blob/main/notebooks/DFine_inference.ipynb
> Fine-tuning https://github.com/qubvel/transformers-notebooks/blob/main/notebooks/DFine_finetune_on_a_custom_dataset.ipynb
h/t @vladislavbro @qubvel-hf @ariG23498 and the authors of the paper 🎩

Regular object detectors attempt to predict bounding boxes in (x, y, w, h) pixel perfect coordinates, which is very rigid and hard to solve 🥲☹️



D-FINE formulates object detection as a distribution for bounding box coordinates, refines them iteratively, and it's more accurate 🤩

Another core idea behind this model is Global Optimal Localization Self-Distillation ⤵️

this model uses final layer's distribution output (sort of like a teacher) to distill to earlier layers to make early layers more performant.

  • 2 replies
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replied to their post 15 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 15 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 🔥 15 days ago
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4247
I'm excited to introduce VisionScout —an interactive vision tool that makes computer vision both accessible and powerful! 👀🔍

What can VisionScout do right now?
🖼️ Upload any image and detect 80 different object types using YOLOv8.
🔄 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.
📊 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! 🚀
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 🔥 15 days ago
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4825
Qwen 3 models released🔥
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

✅ 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.)
✅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.
✅ Three stage done while pretraining:
• Stage 1: General language learning and knowledge building.
• Stage 2: Reasoning boost with STEM, coding, and logic skills.
• Stage 3: Long context training
✅ It supports MCP in the model
✅ Strong agent skills
✅ Supports seamless between thinking mode (for hard tasks like math and coding) and non-thinking mode (for fast chatting) inside chat template.
✅ Better human alignment for creative writing, roleplay, multi-turn conversations, and following detailed instructions.
reacted to Kseniase's post with 👀 16 days ago
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6443
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 Schäfer -> 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 🔥 16 days ago
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2645
🚀 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:
📦 Fully no-string API for easy client deployment
🔥 Demos are now standalone projects:

Demos:
📺 bash / shell (dispatched): https://github.com/nicolay-r/bulk-chain-shell
📺 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 🔥🔥 19 days ago
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2308
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 😎 20 days ago
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2583
smolagents v1.14.0 is out! 🚀
🔌 MCPClient: A sleek new client for connecting to remote MCP servers, making integrations more flexible and scalable.
🪨 Amazon Bedrock: Native support for Bedrock-hosted models.
SmolAgents is now more powerful, flexible, and enterprise-ready. 💼

Full release 👉 https://github.com/huggingface/smolagents/releases/tag/v1.14.0
#smolagents #LLM #AgenticAI
reacted to samihalawa's post with 🔥 20 days ago
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2416
SkyReels-V2 INFINITE VIDEO🔥♾️🎬 UNLIMITED duration video generation model by Skywork.

> “Finally is here. An Open-Source model that achieves what we all have waiting for: Infinite Length Videos.’’😮

Skywork R1V: Pioneering Multimodal Reasoning with Chain-of-Thought (2504.05599)

Model: Skywork/SkyReels-V2-T2V-14B-720P

✨ 1.3B & 14B
✨ Generates infinite length videos using Diffusion Forcing with diffusion models + autoregressive methods