InternLM2.5-7B-Chat-1M
| Property | Value |
|---|---|
| Parameter Count | 7.74B |
| Context Length | 1M tokens |
| License | Apache 2.0 (code), Custom (weights) |
| Paper | arXiv:2403.17297 |
What is internlm2_5-7b-chat-1m?
InternLM2.5-7B-Chat-1M is a state-of-the-art language model designed to handle extremely long contexts up to 1 million tokens. It represents a significant advancement in the field of large language models, particularly excelling in mathematical reasoning and long-text comprehension tasks.
Implementation Details
The model leverages advanced architectures and training techniques to achieve its impressive capabilities. It can be deployed using multiple frameworks including LMDeploy for optimal 1M context processing, Transformers for standard operations, and vLLM for efficient serving.
- Supports multiple deployment options including LMDeploy, Transformers, and vLLM
- Requires 4xA100-80G GPUs for full 1M context utilization
- Implements advanced position encoding with rope_scaling_factor=2.5
Core Capabilities
- Outstanding mathematical reasoning capabilities, surpassing models like Llama3 and Gemma2-9B
- Near-perfect information retrieval in 1M token contexts
- Enhanced tool utilization with support for processing 100+ web pages
- State-of-the-art performance on LongBench benchmark
Frequently Asked Questions
Q: What makes this model unique?
The model's ability to handle 1M token context windows while maintaining high performance in reasoning tasks sets it apart. It demonstrates exceptional capabilities in the "needle in a haystack" approach for information retrieval in long documents.
Q: What are the recommended use cases?
The model is ideal for tasks requiring long document processing, mathematical reasoning, and complex tool utilization scenarios. It's particularly well-suited for applications needing to analyze and comprehend extensive documents or perform complex reasoning tasks.
