Llama3.2 3B - Details

Llama3.2 3B is a large language model developed by Meta Llama Enterprise with a parameter size of 3b. It operates under the Llama 32 Acceptable Use Policy (Llama-32-AUP) and the Llama 32 Community License Agreement (LLAMA-32-COMMUNITY). Designed for multilingual dialogue use cases, it supports agentic retrieval and summarization tasks.

The Llama3.2 3B model is part of a family of multilingual large language models (LLMs) offering pretrained and instruction-tuned versions in 1B and 3B parameter sizes. It is optimized for multilingual dialogue use cases such as agentic retrieval and summarization, trained on up to 9 trillion tokens of data. The model supports multiple languages and code, making it versatile for diverse applications. It is maintained by Meta Llama Enterprise under the Llama 32 Acceptable Use Policy (Llama-32-AUP) and the Llama 32 Community License Agreement (LLAMA-32-COMMUNITY).

The Llama3.2 3B model features 3b parameters, placing it in the mid-scale category of open-source LLMs, offering a balance between performance and resource efficiency for moderate complexity tasks. Its 128k context length falls into the very long context range, enabling advanced handling of extended texts but requiring significant computational resources. This combination allows the model to manage intricate dialogue scenarios and large-scale data while maintaining practical usability.

• Name: Llama3.2 3B
• Parameter_Size: 3b
• Context_Length: 128k
• Implications: Mid-scale parameters for balanced performance, very long context for extended text handling but resource-intensive.

The Llama3.2 3B model offers possible applications in commercial and research settings across multiple languages, including assistant-like chat systems and agentic tools for tasks such as knowledge retrieval and summarization. Its possible use in mobile AI-powered writing assistants could support content creation, while possible adaptations for natural language generation tasks might enhance model customization. Possible query and prompt rewriting scenarios could improve user interactions, and possible on-device implementations may enable efficient processing with limited resources. The model’s multilingual support, covering languages like English, Italian, French, and others, further expands its possible utility in diverse contexts. However, these possible uses require thorough evaluation to ensure alignment with specific requirements and constraints.

• commercial and research use in multiple languages
• assistant-like chat and agentic applications (knowledge retrieval, summarization)
• mobile AI powered writing assistants
• query and prompt rewriting
• adapting pretrained models for natural language generation tasks
• on-device use-cases with limited compute resources

The Llama3.2 3B model presents possible applications in areas such as multilingual dialogue systems for customer service or content creation, where its support for multiple languages and agentic capabilities could enhance interaction. Possible uses in mobile AI-powered writing assistants might streamline content generation for users with limited computational resources. Possible adaptations for query and prompt rewriting could improve user input efficiency, while possible on-device implementations may enable real-time processing for specific tasks. These possible applications require thorough evaluation to ensure they meet specific needs and constraints.

• multilingual dialogue systems
• mobile AI-powered writing assistants
• query and prompt rewriting
• on-device implementations

The Llama3.2 3B model’s medium q4 version requires a GPU with at least 12GB VRAM and a system with 32GB RAM for optimal performance, making it suitable for devices with moderate computational resources. Possible applications may benefit from this balance of efficiency and accuracy, though specific hardware compatibility should be verified. Additional considerations include adequate cooling and a power supply capable of supporting the GPU.

• fp16, q2, q3, q4, q5, q6, q8

The Llama3.2 3B is a mid-scale large language model with 3b parameters and a 128k context length, optimized for multilingual dialogue and agentic tasks like retrieval and summarization. It supports multiple languages and operates under specific licenses, making it suitable for commercial, research, and on-device applications while requiring careful evaluation for specific use cases.

Huggingface Model Page
Ollama Model Page