InternLM - An Agentic Model?

The video introduces InternLM 2.5, a high-performance model optimized for function calling and handling JSON data, developed by the Shanghai AI lab in collaboration with SenseTime. InternLM 2.5 excels in math reasoning, has surpassed other models in performance metrics, and is supported by an agentic framework called Lagent, making it a promising tool for various tasks and applications.

In the video, the speaker introduces InternLM 2.5, a new model optimized for function calling and handling JSON data. Developed by the Shanghai AI lab in collaboration with SenseTime, InternLM 2.5 is highlighted for its capabilities in supporting local agents and quantized versions. It is noted that the model has surpassed other models like Llama-3 and Gemma 2 in performance, making it the number one model on Hugging Face for models with parameters below 10-12 billion.

InternLM 2.5 is commended for its focus on math reasoning and its ability to handle a million context window, excelling in tasks like finding needles in haystacks and performing well on long bench tests. The model comes with an accompanying framework called Lagent, designed specifically for leveraging the model’s capabilities in function calling and tool use. This agentic framework is lauded for its potential to enhance the model’s responses and compatibility with different tasks.

The video delves into the technical aspects of InternLM 2.5, emphasizing its open-source nature and the availability of both base and fine-tuned versions for experimentation. The model’s tech report is praised for its detailed insights into the fine-tuning process and data selection, providing valuable information for those interested in the model’s performance and training data sources.

Practical demonstrations of InternLM 2.5’s performance are showcased using Hugging Face and Ollama implementations. The model is observed to handle various tasks such as generating human-like text, responding to emails, and performing math calculations with commendable accuracy. The speaker also demonstrates how function calling can be utilized with the model, showcasing the model’s ability to return JSON responses and engage in agentic interactions.

Overall, InternLM 2.5 is portrayed as a promising model for those interested in building agents, handling function calling tasks, and leveraging math reasoning capabilities. Its availability on platforms like Hugging Face and Ollama makes it accessible for experimentation and development of diverse applications. The video concludes with an invitation for viewers to explore the model further, experiment with its capabilities, and share feedback for future improvements.