InternLM2-1.8B
| Property | Value |
|---|---|
| Model Size | 1.8B parameters |
| License | Apache 2.0 (code), Custom commercial license (weights) |
| Paper | Technical Report |
| Context Length | 200,000 tokens |
What is internlm2-1_8b?
InternLM2-1.8B is the second generation of the InternLM series, featuring a 1.8 billion parameter architecture. It's available in three variants: a base model for flexibility in downstream tasks, a supervised fine-tuned chat model (SFT), and a RLHF-aligned chat model for enhanced instruction following and function calling.
Implementation Details
The model is implemented using PyTorch and integrates seamlessly with the Transformers library. It supports both float16 and float32 precision, with float16 recommended for optimal memory usage. The model demonstrates exceptional performance in handling ultra-long contexts up to 200,000 characters.
- Efficient long-context processing with near-perfect retrieval capabilities
- Comprehensive performance improvements across reasoning, mathematics, and coding tasks
- State-of-the-art performance on benchmarks like LongBench and L-Eval
Core Capabilities
- Advanced text generation with configurable parameters for temperature and top-p sampling
- Strong performance on MMLU (47.1%), GSM8K (39.7%), and HumanEval (32.9%) for the chat variant
- Exceptional long-text processing and comprehension abilities
- Multilingual support with strong performance in both English and Chinese
Frequently Asked Questions
Q: What makes this model unique?
The model's ability to handle 200,000-character contexts while maintaining high performance sets it apart from other open-source models. It also offers three specialized variants for different use cases, from base model flexibility to optimized chat experiences.
Q: What are the recommended use cases?
The base model is ideal for custom fine-tuning and research, while the chat variants are optimized for direct deployment in conversational applications, function calling, and instruction-following tasks. The model excels in long-form content processing and generation.
