PRIME Intellect has published INTELLECT-1 ( Instruct + Base ), the first 10 billion parameter language model collaboratively trained in 50 days by 30 participants worldwide.
PRIME Intellect used its own PRIME platform, designed to address the main problems of decentralized learning: network unreliability and dynamic management of computing nodes.
The platform utilized a network of 112 H100 GPUs across 3 continents and achieved a compute utilization rate of 96% under optimal conditions.
The training corpus consisted of 1 trillion public dataset tokens with the following percentage distribution: 55% fineweb-edu, 10% fineweb, 20% Stack V1, 10% dclm-baseline, 5% open-web-math.
INTELLECT-1 achieved 37.5% accuracy on the MMLU test and 72.26% on HellaSwag, and outperformed several other open-source models on WinoGrande with a score of 65.82%.
While these figures lag slightly behind today's popular models, the results of the experiment are a critical step toward democratizing AI development and preventing the consolidation of AI capabilities within a few organizations.
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
torch.set_default_device("cuda")
model = AutoModelForCausalLM.from_pretrained("PrimeIntellect/INTELLECT-1")
tokenizer = AutoTokenizer.from_pretrained("PrimeIntellect/INTELLECT-1")
input_text = "%prompt%"
input_ids = tokenizer.encode(input_text, return_tensors="pt")
output_ids = model.generate(input_ids, max_length=50, num_return_sequences=1)
output_text = tokenizer.decode(output_ids[0], skip_special_tokens=True)
print(output_text)
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❇️ AniGS: Animatable Gaussian Avatar from a Single Image with Inconsistent Gaussian Reconstruction 🔥
🔗 Discover More:
* Github Link
* Project Page: AniGS
* Paper: Read the paper
https://news.1rj.ru/str/DataScienceT✅
🔗 Discover More:
* Github Link
* Project Page: AniGS
* Paper: Read the paper
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NVIDIA BioNeMo2 Framework is a set of tools, libraries, and models for computational drug discovery and design.
It accelerates the most time-consuming and expensive steps in building and adapting biomolecular AI models by providing optimized models and tools that are easily integrated into GPU-based computing resources.
The framework enables the creation, training and tuning of models, and its capabilities span a variety of workloads and therapeutic mechanisms: molecule generation, protein structure prediction, protein-ligand prediction and representation learning.
In addition to pipeline code, noscripts and utilities, BioNeMo2 Framework contains:
#AI #ML #Framework #NVIDIA
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2DMatGMM: An open-source robust machine learning platform for real-time detection and classification of 2D material flakes
🖥 Github: https://github.com/jaluus/2dmatgmm
📕 Paper: https://arxiv.org/abs/2412.09333v1
⭐️ Dataset: https://paperswithcode.com/task/instance-segmentation
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Forwarded from Kaggle Data Hub
OASIS Alzheimer's Detection
Large-scale brain MRI dataset for deep neural network analysis
About Dataset
The dataset used is the OASIS MRI dataset (https://sites.wustl.edu/oasisbrains/), which consists of 80,000 brain MRI images. The images have been divided into four classes based on Alzheimer's progression. The dataset aims to provide a valuable resource for analyzing and detecting early signs of Alzheimer's disease.
To make the dataset accessible, the original .img and .hdr files were converted into Nifti format (.nii) using FSL (FMRIB Software Library). The converted MRI images of 461 patients have been uploaded to a GitHub repository, which can be accessed in multiple parts.
For the neural network training, 2D images were used as input. The brain images were sliced along the z-axis into 256 pieces, and slices ranging from 100 to 160 were selected from each patient. This approach resulted in a comprehensive dataset for analysis.
Patient classification was performed based on the provided metadata and Clinical Dementia Rating (CDR) values, resulting in four classes: demented, very mild demented, mild demented, and non-demented. These classes enable the detection and study of different stages of Alzheimer's disease progression.
During the dataset preparation, the .nii MRI scans were converted to .jpg files. Although this conversion presented some challenges, the files were successfully processed using appropriate tools. The resulting dataset size is 1.3 GB.
https://news.1rj.ru/str/datasets1🌟
Large-scale brain MRI dataset for deep neural network analysis
About Dataset
The dataset used is the OASIS MRI dataset (https://sites.wustl.edu/oasisbrains/), which consists of 80,000 brain MRI images. The images have been divided into four classes based on Alzheimer's progression. The dataset aims to provide a valuable resource for analyzing and detecting early signs of Alzheimer's disease.
To make the dataset accessible, the original .img and .hdr files were converted into Nifti format (.nii) using FSL (FMRIB Software Library). The converted MRI images of 461 patients have been uploaded to a GitHub repository, which can be accessed in multiple parts.
For the neural network training, 2D images were used as input. The brain images were sliced along the z-axis into 256 pieces, and slices ranging from 100 to 160 were selected from each patient. This approach resulted in a comprehensive dataset for analysis.
Patient classification was performed based on the provided metadata and Clinical Dementia Rating (CDR) values, resulting in four classes: demented, very mild demented, mild demented, and non-demented. These classes enable the detection and study of different stages of Alzheimer's disease progression.
During the dataset preparation, the .nii MRI scans were converted to .jpg files. Although this conversion presented some challenges, the files were successfully processed using appropriate tools. The resulting dataset size is 1.3 GB.
https://news.1rj.ru/str/datasets1
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⚡️ Byte Latent Transformer: Patches Scale Better Than Tokens
Byte Latent Transformer architecture (BLTs), a new byte-level LLM architecture that for the first time, matches tokenization-based LLM performance at scale, with significant improvements in inference efficiency and robustness.
🖥 Github: https://github.com/facebookresearch/blt
📕 Paper: https://arxiv.org/abs/2412.09871v1
🌟 Dataset: https://paperswithcode.com/dataset/mmlu
https://news.1rj.ru/str/DataScienceT✅
Byte Latent Transformer architecture (BLTs), a new byte-level LLM architecture that for the first time, matches tokenization-based LLM performance at scale, with significant improvements in inference efficiency and robustness.
🌟 Dataset: https://paperswithcode.com/dataset/mmlu
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🀄 GuoFeng Webnovel: A Discourse-Level and Multilingual Corpus of Web Fiction
🖥 Github: https://github.com/longyuewangdcu/guofeng-webnovel
📕 Paper: https://arxiv.org/abs/2412.11732v1
🌟 Dataset: www2.statmt.org/wmt24/literary-trans
https://news.1rj.ru/str/DataScienceT🏳
🌟 Dataset: www2.statmt.org/wmt24/literary-trans
https://news.1rj.ru/str/DataScienceT
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Large Language Models Course: Learn by Doing LLM Projects
🖥 Github: https://github.com/peremartra/Large-Language-Model-Notebooks-Course
📕 Paper: https://doi.org/10.31219/osf.io/qgxea
https://news.1rj.ru/str/DataScienceT✅
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KAG: Boosting LLMs in Professional Domains via Knowledge Augmented Generation
Paper: https://arxiv.org/pdf/2409.13731v3.pdf
Code: https://github.com/openspg/kag
Dataset: 2WikiMultiHopQA
https://news.1rj.ru/str/DataScienceT💙
Paper: https://arxiv.org/pdf/2409.13731v3.pdf
Code: https://github.com/openspg/kag
Dataset: 2WikiMultiHopQA
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CogAgent: A Visual Language Model for GUI Agents
Paper: https://arxiv.org/pdf/2312.08914v3.pdf
CVPR 2024: http://openaccess.thecvf.com//content/CVPR2024/papers/Hong_CogAgent_A_Visual_Language_Model_for_GUI_Agents_CVPR_2024_paper.pdf
Code1: https://github.com/thudm/cogvlm
Code2: https://github.com/digirl-agent/digirl
Code3: https://github.com/THUDM/CogAgent
Dataset: TextVQA
https://news.1rj.ru/str/DataScienceT🩵
Paper: https://arxiv.org/pdf/2312.08914v3.pdf
CVPR 2024: http://openaccess.thecvf.com//content/CVPR2024/papers/Hong_CogAgent_A_Visual_Language_Model_for_GUI_Agents_CVPR_2024_paper.pdf
Code1: https://github.com/thudm/cogvlm
Code2: https://github.com/digirl-agent/digirl
Code3: https://github.com/THUDM/CogAgent
Dataset: TextVQA
https://news.1rj.ru/str/DataScienceT
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Automating the Search for Artificial Life with Foundation Models
paper: https://arxiv.org/pdf/2412.17799v1.pdf
Code: https://github.com/sakanaai/asal
https://news.1rj.ru/str/DataScienceT💙
paper: https://arxiv.org/pdf/2412.17799v1.pdf
Code: https://github.com/sakanaai/asal
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