Llama-3.2-3B-Instruct
| Property | Value |
|---|---|
| Parameter Count | 3.21B |
| License | Llama 3.2 Community License |
| Developer | Meta |
| Release Date | September 25, 2024 |
| Supported Languages | English, German, French, Italian, Portuguese, Hindi, Spanish, Thai |
What is Llama-3.2-3B-Instruct?
Llama-3.2-3B-Instruct is part of Meta's latest generation of multilingual large language models, specifically designed for instruction-tuned dialogue applications. This 3.2B parameter model represents a significant advancement in efficient AI, offering impressive performance while maintaining a relatively compact size.
Implementation Details
The model utilizes an optimized transformer architecture with Grouped-Query Attention (GQA) for improved inference scalability. It has been trained using both supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF), ensuring alignment with human preferences for helpfulness and safety.
- Enhanced training efficiency: 2.4x faster training speeds
- Reduced memory footprint: 58% less memory usage
- BF16 tensor type optimization
- Compatible with transformers library
Core Capabilities
- Multilingual dialogue processing across 8 officially supported languages
- Specialized in agentic retrieval and summarization tasks
- Optimized for conversational AI applications
- Supports text completion and chat-based interactions
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its exceptional balance of performance and efficiency, offering competitive capabilities while requiring significantly less computational resources than larger models. The Unsloth optimization allows for faster training and reduced memory usage, making it particularly attractive for resource-conscious deployments.
Q: What are the recommended use cases?
The model is ideal for multilingual dialogue applications, text generation, summarization, and conversational AI tasks. It's particularly well-suited for scenarios where computational efficiency is crucial while maintaining high-quality language understanding and generation capabilities.
