llama-3-70b-instruct-awq
Maintainer: casperhansen
59
⚙️
| 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-3-70b-instruct-awq model is a large language model developed by casperhansen. It is part of a family of Llama models, which are similar models created by different researchers and engineers. The Llama-3-8B-Instruct-Gradient-1048k-GGUF, llama-30b-supercot, Llama-2-7b-longlora-100k-ft, medllama2_7b, and Llama-3-8b-Orthogonalized-exl2 models are some examples of similar Llama models.
Model inputs and outputs
The llama-3-70b-instruct-awq model is a text-to-text model, which means it takes text as input and generates text as output. The specific inputs and outputs can vary depending on the task or application.
Inputs
- Text prompts that the model uses to generate desired outputs
Outputs
- Generated text that is relevant to the provided input prompt
Capabilities
The llama-3-70b-instruct-awq model can be used for a variety of natural language processing tasks, such as text generation, question answering, and language translation. It has been trained on a large amount of text data, which allows it to generate coherent and relevant text.
What can I use it for?
The llama-3-70b-instruct-awq model can be used for a wide range of applications, such as content creation, customer service chatbots, and language learning assistants. By leveraging the model's text generation capabilities, you can create personalized and engaging content for your audience. Additionally, the casperhansen model can be fine-tuned on specific datasets to improve its performance for your particular use case.
Things to try
You can experiment with the llama-3-70b-instruct-awq model by providing different types of prompts and observing the generated text. Try prompts that cover a range of topics, such as creative writing, analysis, and task-oriented instructions. This will help you understand the model's strengths and limitations, and how you can best utilize it for your needs.
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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