Tele-FLM-1T
| Property | Value |
|---|---|
| Parameter Count | 1.08T (1,083.74B) |
| Architecture | Decoder-only Transformer |
| Context Length | 4,096 tokens |
| License | Apache 2.0 |
| Technical Paper | View Paper |
What is Tele-FLM-1T?
Tele-FLM-1T represents a significant advancement in multilingual language models, featuring a massive 1 trillion parameter architecture trained on approximately 2.3T tokens. As the largest model in the Tele-FLM series, it builds upon its 52B predecessor with enhanced capabilities for handling complex tasks and improved factual judgment.
Implementation Details
The model employs a sophisticated three-stage training approach, scaling from 52B to 102B, and finally to 1T parameters. It utilizes a standard GPT-style decoder-only transformer architecture with several key optimizations:
- 140 layers with 160 attention heads
- Hidden size of 20,480 and FFN hidden size of 98,304
- Rotary Positional Embedding (RoPE) implementation
- RMSNorm for normalization and SwiGLU activation
- 3D parallel training combining data, tensor, and pipeline parallelism
Core Capabilities
- Multilingual processing (English, Chinese, and other languages)
- Enhanced factual judgment capabilities
- Efficient pre-training paradigm
- Compatibility with Llama architecture
- Stable performance across diverse tasks
Frequently Asked Questions
Q: What makes this model unique?
The model's trillion-parameter scale, combined with its efficient training paradigm and enhanced factual judgment capabilities, sets it apart. It represents one of the largest open-source multilingual models available.
Q: What are the recommended use cases?
While still under evaluation, the model is designed for complex language understanding tasks, multilingual applications, and scenarios requiring robust factual judgment. It's particularly suitable for research and industrial applications requiring advanced language processing capabilities.
