RedPajama-INCITE-Chat-3B-v1
| Property | Value |
|---|---|
| Model Size | 2.8B parameters |
| License | Apache 2.0 |
| Language | English |
| Training Hardware | 8 A100 GPUs |
| Memory Requirements | 8GB (GPU), 6GB (Int8) |
What is RedPajama-INCITE-Chat-3B-v1?
RedPajama-INCITE-Chat-3B-v1 is an advanced language model developed through collaboration between Together Computer and leading institutions in the AI community. This model represents a significant achievement in creating accessible, efficient language models, being fine-tuned specifically on OASST1 and Dolly2 datasets to enhance its conversational capabilities.
Implementation Details
The model offers multiple deployment options, including GPU inference (both standard and Int8), and CPU inference. It requires Transformers version 4.25.1 or higher and can be implemented with different precision levels depending on hardware constraints. The model utilizes a specific prompt format with <human> and <bot> tags for interaction.
- Supports both float16 and int8 precision for efficient inference
- Implements temperature and top-p sampling for response generation
- Offers flexible deployment options across different hardware configurations
- Trained on 131M tokens with Adam optimizer
Core Capabilities
- Natural language understanding and generation
- Chat-oriented responses with context awareness
- Memory-efficient operation with multiple precision options
- Scalable deployment from CPU to GPU environments
Frequently Asked Questions
Q: What makes this model unique?
The model stands out for its efficient architecture and versatile deployment options, making it accessible for both research and production environments. Its specialized fine-tuning on chat datasets makes it particularly effective for conversational applications while maintaining reasonable hardware requirements.
Q: What are the recommended use cases?
The model is best suited for conversational AI applications, text generation, and general language understanding tasks. It's particularly effective for scenarios requiring balanced performance and resource efficiency, though users should be mindful of its limitations regarding sensitive or critical applications.
