zohaib99k/llama-2-13b-chat-hf
The zohaib99k/llama-2-13b-chat-hf model is a 13 billion parameter, fine-tuned generative text model from Meta's Llama 2 family, optimized for dialogue use cases. This version is converted for the Hugging Face Transformers format and utilizes an optimized transformer architecture with a 4096-token context length. It is specifically fine-tuned using supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety in chat applications. The model is intended for commercial and research use in English, excelling in assistant-like chat scenarios.
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Llama 2 13B Chat Model Overview
This model is the 13 billion parameter variant of Meta's Llama 2 family, specifically fine-tuned for dialogue applications and formatted for Hugging Face Transformers. Llama 2 models are built on an optimized transformer architecture and were trained on 2 trillion tokens of publicly available data, with fine-tuning data including over one million human-annotated examples. The training process incorporated supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to enhance helpfulness and safety.
Key Capabilities & Features
- Dialogue Optimization: Specifically fine-tuned for chat and assistant-like interactions.
- Performance: Outperforms many open-source chat models and is competitive with some closed-source models like ChatGPT and PaLM in human evaluations for helpfulness and safety.
- Architecture: Utilizes an optimized transformer architecture with a 4096-token context length.
- Safety: Tuned versions show improved safety metrics, achieving 0.00% toxic generations on ToxiGen for the 7B and 13B chat models.
- Commercial Use: Available for both commercial and research applications under a custom Meta license.
Intended Use Cases
- Assistant-like Chat: Ideal for building conversational AI agents and chatbots.
- Natural Language Generation: Adaptable for various text generation tasks, particularly in English.
Limitations
- Language: Primarily intended for use in English; performance in other languages is not guaranteed.
- Safety: While optimized for safety, developers should conduct further testing and tuning for specific applications due to the inherent risks of LLMs producing inaccurate, biased, or objectionable responses.