Llama-2-7b-chat
Maintainer: meta-llama
507
✅
| 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
The Llama-2-7b-chat model is part of the Llama 2 family of large language models (LLMs) developed and publicly released by Meta. Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This 7B fine-tuned model is optimized for dialogue use cases. The Llama-2-Chat models outperform open-source chat models on most benchmarks and are on par with popular closed-source models like ChatGPT and PaLM in human evaluations for helpfulness and safety.
Model inputs and outputs
Inputs
- The model accepts text input only.
Outputs
- The model generates text output only.
Capabilities
The Llama-2-7b-chat model demonstrates strong performance on a variety of academic benchmarks including commonsense reasoning, world knowledge, reading comprehension, and math. It also scores well on safety metrics, producing fewer toxic generations and more truthful and informative outputs compared to earlier Llama models.
What can I use it for?
The Llama-2-7b-chat model is intended for commercial and research use in English. The fine-tuned chat models are optimized for assistant-like dialogue, while the pretrained Llama 2 models can be adapted for a variety of natural language generation tasks. Developers should carefully review the Responsible Use Guide before deploying the model in any applications.
Things to try
Llama-2-Chat models demonstrate strong performance on tasks like open-ended conversation, question answering, and task completion. Developers may want to explore using the model for chatbot or virtual assistant applications, or fine-tuning it further on domain-specific data to tackle specialized language generation challenges.
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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