FLM-2-52B-Instruct-2407
| Property | Value |
|---|---|
| Parameter Count | 52.85B |
| Model Type | GPT-style decoder-only transformer |
| Architecture | 64 layers, 64 attention heads, 8,192 hidden size |
| Paper | 52B to 1T: Lessons Learned via Tele-FLM Series |
What is FLM-2-52B-Instruct-2407?
FLM-2-52B-Instruct-2407 is part of the Tele-FLM series, representing a significant advancement in large language models. This instruction-tuned model demonstrates exceptional performance, particularly in Chinese language processing, and was trained using an innovative fine-tuning approach with carefully selected 30,735 samples.
Implementation Details
The model employs a sophisticated architecture with several key optimizations:
- Rotary Positional Embedding (RoPE) for enhanced position understanding
- RMSNorm for efficient normalization
- SwiGLU activation function
- Disabled linear bias and untied embedding/language model head
- 80,000 vocabulary size with specialized input/output multiplier
Core Capabilities
- Superior performance in Chinese language understanding and generation
- Strong results in AlignBench evaluation across multiple domains
- Exceptional performance in writing, role-playing, and professional knowledge tasks
- Competitive results against larger models like GPT-4 in specific categories
Frequently Asked Questions
Q: What makes this model unique?
The model stands out for its efficient fine-tuning approach and competitive performance against larger models, particularly in Chinese language tasks. It achieves impressive scores in AlignBench evaluations, sometimes surpassing GPT-4 in specific categories like Chinese advanced understanding.
Q: What are the recommended use cases?
The model excels in Chinese language processing, making it ideal for tasks involving writing, professional knowledge, and role-playing scenarios. It's particularly well-suited for applications requiring strong Chinese language understanding and generation capabilities.
