Tutorial: Deploying Falcon 40B with Flowise in a No-Code Environment

📋 This video explains how to use Falcon LM, specifically the 40 billion parameter model, with Flowise and other open source models on Hugging Face.

🔧 To run Falcon and other smaller models from Hugging Face with Flowise, you need to copy the model name and API token, connect them using the Hugging Face LM inference block, and provide a prompt.

🔑 To use Falcon with Flowise, you need to obtain a new API key from your Hugging Face account, which can be read-only.

🤔 The video discusses the compatibility of the Falcon 40B with Flowise.

⚙️ Smaller models can work with the hugging face inference block, but larger models may encounter errors or take longer.

💻 To use larger models, creating an account with the hugging face inference endpoint and deploying the model is recommended.

🔹 The video discusses the process of using Azure and selecting the appropriate GPU for Falcon 40B in North America.

🔹 There is an option to make the configuration public without authentication in Flowise, but the model initialization may take around 10-15 minutes and incur usage costs.

🔹 Once the model is initialized, the endpoints can be used for further tasks.

🔑 The Falcon 40B successfully initialized and ran for about 12 minutes at a cost of 75 cents.

🎯 The endpoint URL generated by the Falcon 40B needs to be copied and used in the additional parameters section of Flowise.

🔑 For deployment, a separate API key and account for the organization is required, which is different from the one used for personal accounts.

⚙️ Testing the endpoint of the Falcon 40B model with a random example.

💡 Using a prompt template to generate a response based on the given input.

🔄 Adjusting the token size to increase the length of the generated response.

✨ The Falcon 40B function can generate longer responses by adjusting values like frequency penalty.

🔄 Repeating values can be modified to improve the response generated.

🔎 The Prompt template can be used to test different scenarios and improve the generated response.

🤖 Testing the model with or without the endpoint can help determine its effectiveness in different cases.

💡 To deploy a given model, select the inference endpoint option and pause the instance to avoid costs.

📋 Deployed endpoints can be accessed, restarted, and used in applications.

📚 For in-depth learning on these topics, check the upcoming course at buildbyu.com.