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Optimizing Pretraining Data Mixes with LLM-Estimated Utility
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Fishtiks
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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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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
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
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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.
🤗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.
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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 🔥
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 🔥
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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
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
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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?
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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! 😉
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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