INTELLECT-2: A Large-Scale Language Model Trained with Distributed Resources | Llm machine learning tutorial geeksforgeeks | Best course on large language models online for beginners | Large language models pdf | Turtles AI

INTELLECT-2 represents a significant advancement in large language models, distinguishing itself by its use of a distributed architecture and training focused on mathematics and programming.

Key Points:

• 32 billion parameter language model with distributed training.
• Use of unauthorized global GPU resources via the prime-rl framework.
• Optimization for mathematics and programming tasks with verifiable rewards.
• Compatibility with popular inference libraries such as vllm and sglang.

INTELLECT-2 stands out in the landscape of advanced language models for its 32 billion parameter architecture, developed through a distributed reinforcement learning process. This approach leveraged unauthorized global GPU resources, made available by the community, via the prime-rl framework, designed for distributed asynchronous reinforcement learning. The model used GRPO on verifiable rewards, with modifications aimed at improving training stability. The training focused on mathematics and programming tasks, using the PrimeIntellect/Intellect-2-RL-Dataset, based on the QwQ-32B model. INTELLECT-2 is compatible with popular inference libraries such as vllm and sglang, thanks to its qwen2 architecture. For best results, it is recommended to add the instruction "Think for 10000 tokens before giving a response" to the prompt, even though the model has not fully learned the length control objective. During training, INTELLECT-2 improved its mathematical and programming skills compared to QwQ, while its performance on IFEval slightly decreased, likely due to the lack of diverse training data and the exclusive focus on mathematics and programming.

This initiative highlights the potential of distributed collaborations in training advanced language models, leveraging global computational resources in innovative ways.