alpaca-lora-7b
Maintainer: chainyo
67
✅
| 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
alpaca-lora-7b is a language model developed by the maintainer chainyo that has been fine-tuned on the Stanford Alpaca dataset. It is based on the LLaMA-7B-hf model, which is available for research purposes only. Similar models include the Llama-2-70b-instruct and alpaca-lora-7b models, which have also been fine-tuned on Alpaca-like datasets.
Model inputs and outputs
The alpaca-lora-7b model takes in text prompts that describe a task, with an optional input context. It then generates a response that appropriately completes the request. The model was trained on prompts of the following format:
Inputs
- Instruction: A text description of a task to be completed
- Input context (optional): Additional context that provides more information about the task
Outputs
- Response: The model's generated text that completes the request
Capabilities
The alpaca-lora-7b model has been fine-tuned to perform a variety of text-to-text tasks, such as answering questions, offering suggestions, and providing informative responses. It has demonstrated strong performance on benchmarks like the Alpaca Evaluation, suggesting it can engage in coherent and relevant dialogue.
What can I use it for?
The alpaca-lora-7b model could be useful for applications that require language understanding and generation, such as chatbots, virtual assistants, and content creation tools. Given its training on the Alpaca dataset, it may be particularly well-suited for tasks that involve answering questions, providing instructions, or offering advice and recommendations.
Things to try
One interesting aspect of the alpaca-lora-7b model is its ability to handle longer input contexts. By leveraging the LLaMA-7B-hf base model, the fine-tuned model can process and generate responses to prompts with more detailed background information. This could be useful for applications that require maintaining context over multiple turns of dialogue.
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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