InternLM2.5-7B-Chat
| Property | Value |
|---|---|
| Parameter Count | 7.74B parameters |
| License | Apache-2.0 (code), Custom Commercial (weights) |
| Paper | Technical Report |
| Context Length | 1M tokens |
What is internlm2_5-7b-chat?
InternLM2.5-7B-Chat is a state-of-the-art language model that represents a significant advancement in the field of AI language models. Developed with a focus on practical applications, this model stands out for its exceptional reasoning capabilities and impressive context handling abilities.
Implementation Details
The model utilizes a sophisticated architecture optimized for both performance and efficiency. It can be easily deployed using popular frameworks like Transformers, LMDeploy, or vLLM, with support for both float16 and various quantization options.
- Built on advanced transformer architecture with 7.74B parameters
- Supports batch inference and streaming generation
- Compatible with multiple deployment solutions
- Optimized for both CPU and GPU environments
Core Capabilities
- State-of-the-art performance on math reasoning tasks, surpassing models like Llama3 and Gemma2-9B
- Exceptional handling of 1M token context windows with high accuracy
- Advanced tool utilization capabilities, supporting multi-turn interactions
- Strong performance on comprehensive benchmarks (MMLU: 72.8%, BBH: 71.6%, MATH: 60.1%)
Frequently Asked Questions
Q: What makes this model unique?
The model's standout features include its exceptional reasoning capabilities, particularly in mathematics, its ability to handle 1M token context windows effectively, and its advanced tool utilization capabilities for complex tasks.
Q: What are the recommended use cases?
The model excels in scenarios requiring complex reasoning, long-context understanding, and tool-based interactions. It's particularly well-suited for academic research, content analysis, and sophisticated dialogue applications.
