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🔥🔥🔥 SmolVLM developers have released open source code for training SmolVLM from scratch on 256 H100!

Inspired by DeepSeek R1, they have open-sourced the complete code for training the model and weights!

You can now train any of the SmolVLMs or create your own VLMs!

Starting training for SmolVLM 256M is very simple:
./vision/experiments/pretraining/vloom/tr_341_smolvlm_025b_1st_stage/01_launch . sh

▪ Code: https://github.com/huggingface/smollm/tree/main/vision
▪ SmolVLM: https://github.com/huggingface/smollm/tree/main

#SmolVLM #llm #opensource #ml #ai

Janus-Pro: Unified Multimodal Understanding and Generation with Data and Model Scaling

Paper: https://arxiv.org/pdf/2501.17811v1.pdf

Code: https://github.com/deepseek-ai/janus

DataSets: #ImageNet - GQA - MM-Vet

#DataScience #ArtificialIntelligence #MachineLearning #PythonProgramming #DeepLearning #AIResearch #BigData #NeuralNetworks #DataAnalytics #NLP #AutoML #DataVisualization #ScikitLearn #Pandas #NumPy #TensorFlow #AIethics #PredictiveModeling #GPUComputing #OpenSourceAI #DeepSeek

https://news.1rj.ru/str/DataScienceT

DeepSeek LLM: Scaling Open-Source Language Models with Longtermism

Paper: https://arxiv.org/pdf/2401.02954v1.pdf

Code: https://github.com/deepseek-ai/deepseek-llm

Dataset: AlignBench

#DataScience #ArtificialIntelligence #MachineLearning #PythonProgramming #DeepLearning #LLM #AIResearch #BigData #NeuralNetworks #DataAnalytics #NLP #AutoML #DataVisualization #ScikitLearn #Pandas #NumPy #TensorFlow #AIethics #PredictiveModeling #GPUComputing #OpenSourceAI #DeepSeek

https://news.1rj.ru/str/DataScienceT

DeepSeek-VL: Towards Real-World Vision-Language Understanding

We present DeepSeek-VL, an open-source Vision-Language (VL) Model designed for real-world vision and language understanding applications. Our approach is structured around three key dimensions: We strive to ensure our data is diverse, scalable, and extensively covers real-world scenarios including web screenshots, PDFs, OCR, charts, and knowledge-based content, aiming for a comprehensive representation of practical contexts. Further, we create a use case taxonomy from real user scenarios and construct an instruction tuning dataset accordingly. The fine-tuning with this dataset substantially improves the model's user experience in practical applications. Considering efficiency and the demands of most real-world scenarios, DeepSeek-VL incorporates a hybrid vision encoder that efficiently processes high-resolution images (1024 x 1024), while maintaining a relatively low computational overhead. This design choice ensures the model's ability to capture critical semantic and detailed information across various visual tasks. We posit that a proficient Vision-Language Model should, foremost, possess strong language abilities. To ensure the preservation of LLM capabilities during pretraining, we investigate an effective VL pretraining strategy by integrating LLM training from the beginning and carefully managing the competitive dynamics observed between vision and language modalities. The DeepSeek-VL family (both 1.3B and 7B models) showcases superior user experiences as a vision-language chatbot in real-world applications, achieving state-of-the-art or competitive performance across a wide range of visual-language benchmarks at the same model size while maintaining robust performance on language-centric benchmarks. We have made both 1.3B and 7B models publicly accessible to foster innovations based on this foundation model.

Paper: https://arxiv.org/pdf/2403.05525v2.pdf

Code: https://github.com/deepseek-ai/deepseek-vl

Datasets: MMLU - GSM8K- HellaSwag

#DataScience #ArtificialIntelligence #MachineLearning #PythonProgramming #DeepLearning #LLM #AIResearch #BigData #NeuralNetworks #DataAnalytics #NLP #AutoML #DataVisualization #ScikitLearn #Pandas #NumPy #TensorFlow #AIethics #PredictiveModeling #GPUComputing #OpenSourceAI #DeepSeek

https://news.1rj.ru/str/DataScienceT

PaSa: An LLM Agent for Comprehensive Academic Paper Search

We introduce PaSa, an advanced Paper Search agent powered by large language models. PaSa can autonomously make a series of decisions, including invoking search tools, reading papers, and selecting relevant references, to ultimately obtain comprehensive and accurate results for complex scholarly queries. We optimize PaSa using reinforcement learning with a synthetic dataset, AutoScholarQuery, which includes 35k fine-grained academic queries and corresponding papers sourced from top-tier AI conference publications. Additionally, we develop RealScholarQuery, a benchmark collecting real-world academic queries to assess PaSa performance in more realistic scenarios. Despite being trained on synthetic data, PaSa significantly outperforms existing baselines on RealScholarQuery, including Google, Google Scholar, Google with GPT-4 for paraphrased queries, chatGPT (search-enabled GPT-4o), GPT-o1, and PaSa-GPT-4o (PaSa implemented by prompting GPT-4o). Notably, PaSa-7B surpasses the best Google-based baseline, Google with GPT-4o, by 37.78% in recall@20 and 39.90% in recall@50. It also exceeds PaSa-GPT-4o by 30.36% in recall and 4.25% in precision. Model, datasets, and code are available at https://github.com/bytedance/pasa.

Paper: https://arxiv.org/pdf/2501.10120v1.pdf

Code: https://github.com/bytedance/pasa

#DataScience #ArtificialIntelligence #MachineLearning #PythonProgramming #DeepLearning #LLM #AIResearch #BigData #NeuralNetworks #DataAnalytics #NLP #AutoML #DataVisualization #ScikitLearn #Pandas #NumPy #TensorFlow #AIethics #PredictiveModeling #GPUComputing #OpenSourceAI #DeepSeek

https://news.1rj.ru/str/DataScienceT