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Llama 3.1 Tulu 3 70B DPO
Overview :
Llama-3.1-Tulu-3-70B-DPO is part of the Tülu3 model family, offering comprehensive guidelines for modern post-training techniques. This model family aims to achieve state-of-the-art performance across various tasks beyond chat, such as MATH, GSM8K, and IFEval. It is trained on publicly available, synthetic, and human-created datasets, mainly in English, and complies with the Llama 3.1 community licensing agreement.
Target Users :
The target audience includes researchers, developers, and educators who can leverage this model for natural language processing tasks, particularly in scenarios requiring instruction following and conversational capabilities. Due to the model's open-source nature, it is also suitable for those wishing to utilize advanced models for teaching and learning in educational environments.
Use Cases
Researchers using the model to evaluate its mathematical problem-solving capabilities on the MATH benchmark.
Developers utilizing the model's chat template feature to create an interactive customer service chatbot.
Educators integrating the model into teaching platforms for personalized learning support and Q&A.
Features
Supports various tasks, including mathematics, question answering, and evaluation tasks.
Provides fully open-source data, code, and recipes for research and educational use.
Fine-tuned model based on allenai/Llama-3.1-Tulu-3-70B-SFT.
Includes code examples for loading the model, enabling developers to get started quickly.
Supports VLLM services for easy model deployment.
Features built-in chat templates for facilitating conversational interactions.
Utilizes default system prompts to define the model's identity and purpose.
How to Use
1. Visit the Hugging Face model page to learn about the model's basic information and performance metrics.
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2. Load the model into your local environment using the code examples provided on the page in Python.
3. Use the model's API for text generation or other NLP tasks.
4. If model deployment is needed, follow the guidelines provided for VLLM services.
5. Adjust system prompts as necessary to fit specific application scenarios.
6. Participate in community discussions to gain technical support and best practices.
7. Adhere to the model's licensing agreement to ensure legal and compliant use of the model.
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