japanese-gpt-neox-3.6b-instruction-sft
| Property | Value |
|---|---|
| Parameter Count | 3.76B |
| License | MIT |
| Paper | Research Paper |
| Authors | Tianyu Zhao and Kei Sawada |
| Architecture | 36-layer, 2816-hidden-size transformer |
What is japanese-gpt-neox-3.6b-instruction-sft?
This is a sophisticated Japanese language model based on the GPT-NeoX architecture, specifically fine-tuned for instruction-following and conversational tasks. The model represents a significant advancement in Japanese natural language processing, incorporating 3.6 billion parameters and trained on high-quality datasets including Anthropic HH RLHF data and Stanford Human Preferences Dataset.
Implementation Details
The model utilizes a specialized tokenization system based on SentencePiece with a 32,000-token vocabulary. Notable technical features include byte fallback capability for handling unknown tokens and specific configurations for whitespace handling.
- Custom input/output format for conversation handling between "ユーザー" and "システム"
- Special newline handling using "NL" token
- Optimized tokenization without automatic whitespace prepending
- Support for multiple tensor types including F32, FP16, and U8
Core Capabilities
- Instruction-following conversation in Japanese
- Advanced text generation with temperature control
- Efficient handling of unknown characters through byte fallback
- Preservation of whitespace formatting
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its specialized Japanese language capabilities and careful fine-tuning on instruction-following tasks. The combination of its size (3.6B parameters) and sophisticated tokenization makes it particularly effective for Japanese text generation and conversation.
Q: What are the recommended use cases?
The model is ideal for Japanese conversational AI applications, instruction-following tasks, and general text generation. It's particularly well-suited for applications requiring natural Japanese language interaction and understanding of context.
