osllmai-community/Llama-3.2-11B-Vision-Instruct-bnb-4bit

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Model creator: Llama-3.2-11B-Vision-Instruct-bnb-4bit

Original model: Meta LLAMA 3.2

Official Website • Documentation • Discord

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Acknowledgments
Our sincere gratitude to the Meta and Llama teams for their efforts in developing and releasing these models.

Model Overview
The Meta Llama 3.2 collection features multilingual large language models (LLMs), available in 1B and 3B sizes, with capabilities in both text input and output. The instruction-tuned Llama 3.2 models are optimized for multilingual dialogue, excelling in agentic retrieval and summarization tasks. They demonstrate superior performance on standard industry benchmarks compared to many open-source and closed chat models.

• Developer: Meta
• Architecture: Llama 3.2 is an auto-regressive language model utilizing an optimized transformer structure. Its tuned versions employ supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.
• Supported Languages: Officially supported languages include English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai. Llama 3.2 has been trained on a wider array of languages, and developers may further fine-tune the model for additional languages, subject to the Llama 3.2 Community License and Acceptable Use Policy. Responsible and safe deployment practices are required.
• Token Counts: Token references pertain solely to pretraining data. All versions employ Grouped-Query Attention (GQA) to enhance inference scalability.

Release Information

• Release Date: September 25, 2024
• Status: This is a static model based on an offline dataset. Future updates may further enhance model performance and safety.
• License: Llama 3.2 usage is governed by the Llama 3.2 Community License, a custom commercial license agreement.

Feedback and Further Information
For questions or feedback regarding Llama 3.2, please refer to the model README. Additional technical details and guidance on generation parameters, as well as usage recipes, can be found here.

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