GLM-4-9B-Chat-1M
| Property | Value |
|---|---|
| Parameter Count | 9.48B |
| Model Type | Large Language Model |
| Architecture | Transformer-based |
| License | GLM-4 |
| Paper | arXiv:2406.12793 |
| Context Length | 1M tokens |
What is glm-4-9b-chat-1m?
GLM-4-9B-Chat-1M is an advanced language model from THUDM that represents a significant breakthrough in long-context language modeling. As part of the GLM-4 series, this model stands out for its exceptional ability to handle contexts up to 1M tokens (approximately 200 million Chinese characters), making it particularly suitable for complex, long-form content processing.
Implementation Details
The model utilizes BF16 tensor type and implements advanced transformer architecture. It supports both standard transformers and VLLM backends for inference, with specialized optimizations for handling extremely long contexts.
- Supports 26 different languages including English, Chinese, Japanese, Korean, and German
- Implements efficient context handling mechanisms for 1M token sequences
- Features built-in support for chat templating and generation parameters
Core Capabilities
- Multi-turn dialogue with extended context retention
- Web browsing and content analysis
- Code execution and interpretation
- Function calling capabilities
- Long-text inference up to 1M tokens
- Multilingual support across 26 languages
Frequently Asked Questions
Q: What makes this model unique?
The model's standout feature is its ability to handle extremely long contexts (1M tokens) while maintaining high performance across various tasks including semantic understanding, mathematics, reasoning, and coding. Its performance in the "needle in a haystack" experiments demonstrates exceptional long-text comprehension abilities.
Q: What are the recommended use cases?
The model is particularly well-suited for applications requiring long-form content analysis, complex multi-turn conversations, code generation, and multilingual processing. It's ideal for tasks involving document analysis, technical documentation, and cross-lingual communication.
