Openintelligent123/Llama-3.3-70B-Instruct
Openintelligent123/Llama-3.3-70B-Instruct is a 70 billion parameter instruction-tuned generative language model developed by Meta, based on the Llama 3.3 optimized transformer architecture. It features a 32K context length and is optimized for multilingual dialogue use cases, outperforming many chat models on common benchmarks. This model excels in multilingual text generation and code, supporting 8 languages natively and trained on over 15 trillion tokens.
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Model Overview
Openintelligent123/Llama-3.3-70B-Instruct is a 70 billion parameter instruction-tuned large language model developed by Meta. It is built upon an optimized transformer architecture and has been fine-tuned using supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety. The model boasts a substantial context length of 128K tokens and was trained on over 15 trillion tokens of publicly available online data, with a knowledge cutoff of December 2023.
Key Capabilities
- Multilingual Dialogue: Optimized for multilingual chat, outperforming many open-source and closed chat models on industry benchmarks.
- Broad Language Support: Supports English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai, with potential for fine-tuning in other languages.
- Code Generation: Capable of generating multilingual text and code.
- Tool Use: Supports advanced tool use formats, enabling integration with external functions and services.
- Performance: Achieves strong results across various benchmarks, including 86.0% on MMLU, 88.4% on HumanEval (pass@1), and 91.1% on MGSM.
Good For
- Commercial and Research Use: Intended for a wide range of applications in both commercial and research settings.
- Assistant-like Chatbots: Instruction-tuned for effective assistant-like conversational AI.
- Natural Language Generation: Adaptable for various natural language generation tasks.
- Synthetic Data Generation: Can leverage its outputs to improve other models through synthetic data generation and distillation.