llama-7b-hf
Maintainer: yahma
75
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| Property | Value |
|---|---|
| Run this model | Run 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-7b-hf is a 7B parameter version of the LLaMA language model, developed by the FAIR team at Meta AI. It is an autoregressive transformer-based model trained on over 1 trillion tokens of data. The model has been converted to work with the Hugging Face Transformers library, making it more accessible to researchers and developers. This version resolves some issues with the EOS token that were present in earlier releases.
There are several similar open-source LLaMA models available, including the open_llama_7b and open_llama_13b models from the OpenLLaMA project, which are permissively licensed reproductions of the LLaMA model trained on public datasets.
Model inputs and outputs
Inputs
- Text: The model takes raw text as input and generates additional text in an autoregressive manner.
Outputs
- Text: The model generates coherent, human-like text continuations based on the provided input.
Capabilities
The llama-7b-hf model is capable of a wide range of natural language processing tasks, including question answering, summarization, and open-ended text generation. It has shown strong performance on academic benchmarks like commonsense reasoning, world knowledge, and reading comprehension.
What can I use it for?
The primary intended use of the llama-7b-hf model is for research on large language models, including exploring potential applications, understanding model capabilities and limitations, and developing techniques to improve safety and performance. The model could be fine-tuned or used as a base for downstream applications like conversational AI, content generation, and knowledge-intensive tasks.
Things to try
Researchers and developers can experiment with the llama-7b-hf model to explore its capabilities and limitations. Some ideas include testing the model's performance on specialized tasks, evaluating its safety and alignment with human values, and using it as a starting point for fine-tuning on domain-specific datasets.
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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