Llama-2-7b-chat-hf
Maintainer: meta-llama
3.5K
🏋️
| Property | Value |
|---|---|
| Model Link | View on HuggingFace |
| API Spec | View on HuggingFace |
| Github Link | No Github link provided |
| Paper Link | No paper link provided |
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Model overview
Llama-2-7b-chat-hf is a 7 billion parameter generative text model developed and released by Meta. It is part of the Llama 2 family of large language models, which range in size from 7 billion to 70 billion parameters. The Llama 2 models are trained on a new mix of publicly available online data and fine-tuned for dialogue use cases using supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF). Compared to the pretrained Llama-2-7b model, the Llama-2-7b-chat-hf model is specifically optimized for chat and assistant-like applications.
Model inputs and outputs
Inputs
- The
Llama-2-7b-chat-hfmodel takes text as input.
Outputs
- The model generates text as output.
Capabilities
The Llama 2 family of models, including Llama-2-7b-chat-hf, have shown strong performance on a variety of academic benchmarks, outperforming many open-source chat models. The 70B parameter Llama 2 model in particular achieved top scores on commonsense reasoning, world knowledge, reading comprehension, and mathematical reasoning tasks. The fine-tuned chat models like Llama-2-7b-chat-hf are also evaluated to be on par with popular closed-source models like ChatGPT and PaLM in terms of helpfulness and safety, as measured by human evaluations.
What can I use it for?
The Llama-2-7b-chat-hf model is intended for commercial and research use in English, with a focus on assistant-like chat applications. Developers can use the model to build conversational AI agents that can engage in helpful and safe dialogue. The model can also be adapted for a variety of natural language generation tasks beyond just chat, such as question answering, summarization, and creative writing.
Things to try
One key aspect of the Llama-2-7b-chat-hf model is the specific formatting required to get the expected chat-like features and performance. This includes using INST and <<SYS>> tags, BOS and EOS tokens, and proper whitespacing and linebreaks in the input. Developers should review the reference code provided in the Llama GitHub repository to ensure they are properly integrating the model for chat use cases.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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