Open-source 70B model surpass GPT-4o and Claude 3.5 on Arena Hard
NVIDIA's Llama-3.1-Nemotron-70B-Instruct model, optimized for helpfulness, ranks first in alignment benchmarks, utilizes advanced RLHF techniques, requires significant hardware, and emphasizes ethical usage guidelines for responsible application.
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NVIDIA has introduced the Llama-3.1-Nemotron-70B-Instruct model, a large language model designed to enhance the helpfulness of responses generated for user queries. As of October 1, 2024, it ranks first in three automatic alignment benchmarks: Arena Hard, AlpacaEval 2 LC, and GPT-4-Turbo MT-Bench, outperforming notable models like GPT-4o and Claude 3.5 Sonnet. The model was trained using Reinforcement Learning from Human Feedback (RLHF) techniques, specifically REINFORCE, and is based on the Llama-3.1-70B-Instruct model. It has been adapted for use within the Hugging Face Transformers framework. The model can handle a maximum of 128k tokens for input and 4k tokens for output. It is compatible with NVIDIA Ampere and newer architectures and requires significant hardware resources for deployment. The model's performance metrics indicate a mean response length of approximately 2199.8 characters, with a notable ability to answer queries accurately without specialized prompting. Ethical considerations are emphasized, urging developers to ensure the model's responsible use in various applications. The model is available for use through the Hugging Face platform, and users are encouraged to follow the terms of service and privacy policies.
- Llama-3.1-Nemotron-70B-Instruct is optimized for helpfulness in user interactions.
- It ranks first in multiple alignment benchmarks as of October 2024.
- The model utilizes advanced RLHF techniques for training.
- It requires substantial hardware resources for effective deployment.
- Ethical usage guidelines are provided to ensure responsible application.
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