| To do so, just call push_to_hub on the tool variable: | |
| python | |
| tool.push_to_hub("hf-model-downloads") | |
| You now have your code on the Hub! Let's take a look at the final step, which is to have the agent use it. | |
| Having the agent use the tool | |
| We now have our tool that lives on the Hub which can be instantiated as such (change the user name for your tool): | |
| thon | |
| from transformers import load_tool | |
| tool = load_tool("lysandre/hf-model-downloads") | |
| In order to use it in the agent, simply pass it in the additional_tools parameter of the agent initialization method: | |
| thon | |
| from transformers import HfAgent | |
| agent = HfAgent("https://api-inference.huggingface.co/models/bigcode/starcoder", additional_tools=[tool]) | |
| agent.run( | |
| "Can you read out loud the name of the model that has the most downloads in the 'text-to-video' task on the Hugging Face Hub?" | |
| ) | |
| which outputs the following:text | |
| ==Code generated by the agent== | |
| model = model_download_counter(task="text-to-video") | |
| print(f"The model with the most downloads is {model}.") | |
| audio_model = text_reader(model) | |
| ==Result== | |
| The model with the most downloads is damo-vilab/text-to-video-ms-1.7b. | |
| and generates the following audio. | |
| | Audio | | |
| |------------------------------------------------------------------------------------------------------------------------------------------------------| | |
| | | | |
| Depending on the LLM, some are quite brittle and require very exact prompts in order to work well. |