AIDSC/llama2-7b-chat-hf
AIDSC/llama2-7b-chat-hf is a 7 billion parameter Llama 2 model developed by Meta, fine-tuned for dialogue use cases and converted for the Hugging Face Transformers format. This auto-regressive language model utilizes an optimized transformer architecture and is specifically optimized for assistant-like chat in English. It was trained on 2 trillion tokens of publicly available data with a 4k context length, outperforming many open-source chat models in benchmarks.
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Llama 2 7B Chat Model Overview
This model, AIDSC/llama2-7b-chat-hf, is a 7 billion parameter variant from Meta's Llama 2 family of large language models. It is a fine-tuned, generative text model specifically optimized for dialogue use cases, converted for the Hugging Face Transformers format.
Key Capabilities & Features
- Dialogue Optimization: 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 scenarios.
- Performance: Outperforms many open-source chat models on tested benchmarks and achieves competitive results with some popular closed-source models like ChatGPT and PaLM in human evaluations for helpfulness and safety.
- Architecture: Employs an auto-regressive language model with an optimized transformer architecture.
- Training Data: Pretrained on 2 trillion tokens of publicly available online data, with fine-tuning data including publicly available instruction datasets and over one million new human-annotated examples. The pretraining data cutoff is September 2022, with some tuning data up to July 2023.
- Context Length: Supports a context length of 4096 tokens.
Intended Use Cases
This model is primarily intended for commercial and research use in English, specifically for assistant-like chat applications. Developers should follow a specific formatting for inputs, including INST and <<SYS>> tags, BOS and EOS tokens, and proper whitespace, as detailed in the reference code. It is not intended for use in languages other than English or in ways that violate its commercial license and acceptable use policy.