sharpbai/Llama-2-7b-chat is a 7 billion parameter Llama 2 model developed by Meta, fine-tuned for dialogue use cases. This auto-regressive language model uses an optimized transformer architecture and is specifically optimized for assistant-like chat applications in English. It was trained on 2 trillion tokens of publicly available data with a 4096-token context length, outperforming many open-source chat models in human evaluations for helpfulness and safety.
Loading preview...
Overview
sharpbai/Llama-2-7b-chat is a 7 billion parameter variant from Meta's Llama 2 family of large language models. This specific model is a fine-tuned version, optimized for dialogue and chat-based applications, and converted for the Hugging Face Transformers format. It leverages an optimized transformer architecture and was trained using supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to enhance alignment with human preferences for helpfulness and safety.
Key Capabilities
- Dialogue Optimization: Specifically fine-tuned for assistant-like chat use cases.
- Performance: Outperforms many open-source chat models on various benchmarks and achieves strong human evaluation scores for helpfulness and safety.
- Context Length: Supports a context length of 4096 tokens.
- Training Data: Pretrained on 2 trillion tokens of publicly available data, with fine-tuning data including over one million human-annotated examples.
Intended Use Cases
- Commercial and Research: Designed for use in both commercial products and research initiatives.
- English Language: Primarily intended for applications in English.
- Assistant-like Chat: Best suited for conversational AI and chatbot development, requiring specific formatting for optimal performance.