InternLM2-20B
| Property | Value |
|---|---|
| Model Size | 20B parameters |
| License | Apache-2.0 (code), Custom commercial license (weights) |
| Paper | arXiv:2403.17297 |
| Context Length | 200,000 tokens |
What is internlm2-20b?
InternLM2-20B is the second generation of the InternLM model series, representing a significant advancement in large language model capabilities. It comes in four variants: base, standard (recommended), chat-sft, and chat (recommended), each optimized for different use cases. The model demonstrates exceptional performance across various benchmarks, particularly in handling ultra-long contexts and complex reasoning tasks.
Implementation Details
The model is implemented using PyTorch and can be easily loaded through the Transformers library. It supports both float16 and float32 precision, though float16 is recommended for memory efficiency. The architecture leverages advanced techniques to handle context lengths up to 200,000 characters effectively.
- Comprehensive evaluation results showing strong performance on MMLU (67.7%), BBH (72.1%), and GSM8K (76.1%)
- Supports efficient text generation with customizable parameters for temperature and top-p sampling
- Implements advanced context handling mechanisms for superior long-text processing
Core Capabilities
- Ultra-long context processing up to 200,000 characters
- Enhanced reasoning and mathematical problem-solving abilities
- Advanced coding capabilities with strong performance on HumanEval (48.8%) and MBPP (63.0%)
- Optimized for both general language understanding and specialized tasks
Frequently Asked Questions
Q: What makes this model unique?
The model's ability to handle ultra-long contexts of up to 200,000 characters sets it apart, along with its comprehensive performance improvements across reasoning, mathematics, and coding tasks compared to its predecessor.
Q: What are the recommended use cases?
The model is ideal for long-form content processing, complex reasoning tasks, mathematical problem-solving, and coding applications. The chat variant is specifically optimized for conversational interactions and tool invocation.
