The technical report titled InternLM2 Technical Report recounts the development of InternLM2, an open-source Large Language Model that surpasses its predecessors in various benchmarks. This model’s pre-training incorporated diverse data types and captured long-term dependencies, enabling its remarkable performance in the comprehensive 200k “Needle-in-a-Haystack” test.

Key Insights:

• InternLM2 utilized innovative pre-training and optimization strategies.
• It was aligned using Supervised Fine-Tuning (SFT) and COOL RLHF, addressing human preference conflicts and reward hacking.
• InternLM2 models are shared publicly, granting insights into the evolution of open-source LLMs.

This paper is significant as it offers a substantial open-source alternative to proprietary models like ChatGPT, fostering more inclusive and collaborative advancements in AI. The model’s ability to effectively manage long-context information at different stages is particularly vital for the progression of language modeling research.

To learn more, explore the report here.