Llama-2-70b-hf
Maintainer: meta-llama
800
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| 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-2-70b-hf is a 70 billion parameter generative language model developed and released by Meta as part of their Llama 2 family of large language models. This model is a pretrained version converted for the Hugging Face Transformers format. The Llama 2 collection includes models ranging from 7 billion to 70 billion parameters, as well as fine-tuned versions optimized for dialogue use cases. The Llama-2-70b-chat-hf model is the fine-tuned version of this 70B model, optimized for conversational abilities.
Model inputs and outputs
Inputs
Llama-2-70b-hftakes text input only.
Outputs
- The model generates text output only.
Capabilities
The Llama-2-70b-hf model is a powerful auto-regressive language model that can be used for a variety of natural language generation tasks. It outperforms many open-source chat models on industry benchmarks and is on par with some popular closed-source models like ChatGPT and PaLM in terms of helpfulness and safety.
What can I use it for?
The Llama-2-70b-hf model is intended for commercial and research use in English. The pretrained version can be adapted for tasks like text generation, summarization, and translation, while the fine-tuned Llama-2-70b-chat-hf model is optimized for assistant-like chat applications.
Things to try
Developers can fine-tune the Llama-2-70b-hf model for their specific use cases, leveraging the model's strong performance on a variety of NLP tasks. The Llama-2-7b-hf and Llama-2-13b-hf models provide smaller-scale alternatives that may be more practical for certain applications.
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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Llama-2-70b is a 70 billion parameter large language model developed and released by Meta. It is part of the Llama 2 family of models, which also includes smaller 7 billion and 13 billion parameter versions. The Llama 2 models are pretrained on 2 trillion tokens of data and then fine-tuned for dialogue use cases, outperforming open-source chat models on most benchmarks according to the maintainers. The Llama-2-70b-chat-hf and Llama-2-70b-hf versions are also available, with the chat version optimized for dialogue use cases. Model inputs and outputs The Llama-2-70b model takes in text as input and generates text as output. It uses an optimized transformer architecture and was trained using supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align it to human preferences for helpfulness and safety. Inputs Text data Outputs Generated text Capabilities The Llama-2-70b model demonstrates strong performance across a range of benchmarks, including commonsense reasoning, world knowledge, reading comprehension, and mathematics. It also shows improved safety metrics compared to earlier Llama models, with higher truthfulness and lower toxicity levels. What can I use it for? Llama-2-70b is intended for commercial and research use in English-language applications. The fine-tuned chat versions like Llama-2-70b-chat-hf are optimized for assistant-like dialogue, while the pretrained models can be adapted for a variety of natural language generation tasks. Things to try Developers should carefully test and tune the Llama-2-70b model before deploying it, as large language models can produce inaccurate, biased or objectionable outputs. The Responsible Use Guide provides important guidance on the ethical considerations and limitations of using this technology.
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