Meta-Llama-3.1-405B-Instruct
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Meta Llama 3.1 405B Instruct
Overview :
Meta Llama 3.1 is a series of multilingual large pre-trained and instruction-tuned generative models, available in sizes of 8B, 70B, and 405B. These models are specifically optimized for multilingual dialogue use cases and outperform many open-source and closed-source chat models in standard industry benchmarks. The models use an optimized transformer architecture and are fine-tuned through supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for usefulness and safety.
Target Users :
Target audience includes developers and researchers who need to create chatbots, assistants, or any application that requires natural language processing capabilities in multilingual environments. The model's multilingual capabilities and optimized conversational performance make it an ideal choice for globalization application development.
Total Visits: 29.7M
Top Region: US(17.94%)
Website Views : 59.6K
Use Cases
Used to create multilingual chatbots for customer support.
Integrated into a corporate knowledge management system to help users retrieve and understand large volumes of documents.
Serves as a research tool for analyzing and generating multilingual text data.
Features
Supports 8 languages: English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai.
Utilizes Grouped-Query Attention (GQA) technology to enhance inference scalability.
Provides foundational pre-trained models and instruction-tuned models suitable for various natural language generation tasks.
Follows responsible deployment strategies to protect developers and the community from potential misuse.
Continuously improves model safety through community feedback.
Supports commercial and research applications, as well as enhancing other models' capabilities using its outputs.
How to Use
Step 1: Visit the Hugging Face model repository and select the Meta Llama 3.1 model.
Step 2: Read and agree to the terms of use, including the privacy policy and community license agreement.
Step 3: Download the model files and configure and fine-tune them as needed.
Step 4: Integrate the model into your application to implement the desired natural language processing features.
Step 5: Test the model to ensure its outputs meet expected quality and safety standards.
Step 6: Make adjustments based on feedback and continuously optimize the model's performance.
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