gorilla-7b-hf-delta-v0
Maintainer: gorilla-llm
51
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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 gorilla-7b-hf-delta-v0 is an open-source API caller model developed by the Gorilla LLM team at UC Berkeley. It is a fine-tuned version of the LLaMA model that can reliably use Hugging Face APIs. The model is able to generate semantically- and syntactically-correct API calls given natural language instructions. This sets it apart from similar models like gorilla-openfunctions-v1 and gorilla-openfunctions-v2 which also focus on invoking APIs from language instructions.
Model inputs and outputs
Inputs
- Natural language prompts: The model accepts natural language instructions as input to generate the appropriate API calls.
Outputs
- API calls: The model outputs a string representing the API call with the correct function name and arguments based on the input prompt.
Capabilities
The gorilla-7b-hf-delta-v0 model can reliably generate API calls in response to natural language instructions. For example, given the prompt "I want to generate an image from text," the model would output a semantically correct API call like dalle.generate(prompt="An illustration of a happy dog").
What can I use it for?
The gorilla-7b-hf-delta-v0 model can be used to build applications that allow users to interact with APIs using natural language. This could include chatbots, virtual assistants, or low-code/no-code tools that generate API calls based on user input. The model's ability to accurately translate natural language into executable API calls makes it a valuable tool for developers who want to abstract away the complexity of API integration.
Things to try
One interesting aspect of the gorilla-7b-hf-delta-v0 model is its ability to handle a wide range of API types, from image generation to web scraping and beyond. Developers could experiment with prompting the model to generate calls for different types of APIs and see how it performs. Additionally, the model could be fine-tuned on domain-specific datasets to improve its performance on particular types of APIs or use cases.
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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