Llama-3.2-3B
| Property | Value |
|---|---|
| Parameter Count | 3.21B |
| Model Type | Text Generation |
| Architecture | Transformer with GQA |
| License | Llama 3.2 Community License |
| Release Date | September 25, 2024 |
What is Llama-3.2-3B?
Llama-3.2-3B is Meta's latest addition to their multilingual large language model collection, designed specifically for dialogue and text generation tasks. This 3.21B parameter model represents a significant advancement in efficient AI language processing, 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's available in BF16 tensor format and has been fine-tuned using both supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) techniques.
- Optimized for multilingual dialogue use cases
- Supports efficient fine-tuning with 58% less memory usage
- 2.4x faster performance with Unsloth optimization
- Compatible with GGUF, vLLM, and Hugging Face frameworks
Core Capabilities
- Multilingual support for 8 officially supported languages: English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai
- Specialized in dialogue, retrieval, and summarization tasks
- Enhanced performance on industry benchmarks compared to other open-source models
- Efficient fine-tuning capabilities with reduced resource requirements
Frequently Asked Questions
Q: What makes this model unique?
The model stands out for its exceptional balance between size and performance, featuring optimized architecture with GQA and significant memory efficiency improvements. It's particularly notable for its multilingual capabilities while maintaining a relatively small 3.21B parameter size.
Q: What are the recommended use cases?
This model is particularly well-suited for multilingual dialogue applications, text generation, summarization, and retrieval tasks. It's ideal for developers looking to implement efficient language models in resource-constrained environments while maintaining high-quality output across multiple languages.
