| Property | Value |
|---|---|
| Parameter Count | 3.21B |
| Context Length | 128k tokens |
| Training Data | Up to 9T tokens |
| License | Llama 3.2 Community License |
| Supported Languages | English, German, French, Italian, Portuguese, Hindi, Spanish, Thai |
What is Llama-3.2-3B-GGUF?
Llama-3.2-3B-GGUF is a quantized version of Meta's Llama-3.2-3B model, optimized for efficient deployment using the GGUF format. This model represents a significant advancement in multilingual language models, specifically designed for dialogue-based applications and instruction-following tasks.
Implementation Details
The model utilizes an optimized transformer architecture with Grouped-Query Attention (GQA) for improved inference scalability. It's been trained using a combination of pretraining on public data and knowledge distillation from larger Llama models, followed by careful alignment through supervised fine-tuning and reinforcement learning.
- Optimized for 8 officially supported languages
- Trained on data with knowledge cutoff of December 2023
- Implements GQA for better inference performance
- Uses shared embeddings architecture
Core Capabilities
- High-performance text generation and dialogue
- Strong performance on MMLU benchmark (63.4% accuracy)
- Effective at math reasoning (77.7% accuracy on GSM8K)
- Long-context understanding with 128k token context window
- Multilingual comprehension and generation
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its efficient size-to-performance ratio, offering strong capabilities in multiple languages while being compact enough for deployment in resource-constrained environments. The GGUF format makes it particularly suitable for efficient inference.
Q: What are the recommended use cases?
The model excels in assistant-like chat applications, knowledge retrieval, summarization, and mobile AI-powered writing assistance. It's particularly well-suited for applications requiring multilingual support while maintaining reasonable resource requirements.