Llama-2-13b-chat-hf

Maintainer: meta-llama

Total Score

948

Last updated 4/28/2024

🎯

PropertyValue
Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

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Model Overview

The Llama-2-13b-chat-hf is a version of Meta's Llama 2 large language model, a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This specific 13 billion parameter model has been fine-tuned for dialogue use cases and converted for the Hugging Face Transformers format. The Llama-2-70b-chat-hf and Llama-2-7b-hf models are other variations in the Llama 2 family.

Model Inputs and Outputs

The Llama-2-13b-chat-hf model takes in text as input and generates text as output. It is an auto-regressive language model that uses an optimized transformer architecture. The fine-tuned versions like this one have been further trained using supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align the model to human preferences for helpfulness and safety.

Inputs

  • Text prompts

Outputs

  • Generated text

Capabilities

The Llama-2-13b-chat-hf model is capable of a variety of natural language generation tasks, from open-ended dialogue to specific prompts. It outperforms open-source chat models on most benchmarks that Meta has tested, and its performance on human evaluations for helpfulness and safety is on par with models like ChatGPT and PaLM.

What Can I Use It For?

The Llama-2-13b-chat-hf model is intended for commercial and research use in English. The fine-tuned chat versions are well-suited for building assistant-like applications, while the pretrained models can be adapted for a range of natural language tasks. Some potential use cases include:

  • Building AI assistants and chatbots for customer service, personal productivity, and more
  • Generating creative content like stories, dialogue, and poetry
  • Summarizing text and answering questions
  • Providing language models for downstream applications like translation, question answering, and code generation

Things to Try

One interesting aspect of the Llama 2 models is the use of Grouped-Query Attention (GQA) in the larger 70 billion parameter version. This technique improves the model's inference scalability, allowing for faster generation without sacrificing performance.

Another key feature is the careful fine-tuning and safety testing that Meta has done on the chat-focused versions of Llama 2. Developers should still exercise caution and perform their own safety evaluations, but these models show promising results in terms of helpfulness and reducing harmful outputs.



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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