OpenPipe/Llama-3.1-8B-Instruct
OpenPipe/Llama-3.1-8B-Instruct is an 8 billion parameter instruction-tuned large language model developed by Meta, part of the Llama 3.1 collection. Optimized for multilingual dialogue use cases, it features a 128k token context length and excels in assistant-like chat applications. This model supports tool use and is designed for commercial and research applications across multiple languages including English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai.
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OpenPipe/Llama-3.1-8B-Instruct Overview
OpenPipe/Llama-3.1-8B-Instruct is an 8 billion parameter instruction-tuned model from Meta's Llama 3.1 series, released on July 23, 2024. It is built on an optimized transformer architecture, 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 was pretrained on over 15 trillion tokens of publicly available online data, with a knowledge cutoff of December 2023, and features a substantial 128k token context length.
Key Capabilities & Features
- Multilingual Support: Optimized for multilingual dialogue in English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai.
- Extended Context Window: Boasts a 128k token context length, enabling processing of longer inputs and generating more extensive responses.
- Instruction Following: Instruction-tuned for assistant-like chat and general natural language generation tasks.
- Tool Use: Supports advanced tool use capabilities, allowing integration with external functions and services.
- Performance Improvements: Demonstrates notable improvements over Llama 3 8B Instruct across various benchmarks, including MMLU (73.0% CoT), HumanEval (72.6% pass@1), and MATH (51.9% final_em).
Intended Use Cases
- Assistant-like Chatbots: Ideal for building conversational AI agents that can engage in multilingual dialogue.
- Code Generation: Shows strong performance in coding benchmarks like HumanEval and MBPP.
- Tool Integration: Suitable for applications requiring function calling and interaction with external APIs.
- Research & Commercial Applications: Designed for broad use in both academic research and commercial deployments, with a focus on responsible AI development.