Launched on October 9, 2024, no-code tuning simplifies the process of tuning a text generation model for those looking to take an open source text generation model (as Gemma – 2B in the example) to create custom, high-performing, and secure solutions that align with their specific needs, industries, and brand voice. Why would you want […]
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Guidelines to get you started with the art of finetuning! Dataset Requirements Before configuring your training parameters, ensure you upload a clean dataset in CSV format with proper column names. The dataset should be well-structured and free from errors. A high-quality dataset is crucial for effective fine-tuning of the LLM. Training parameters of LLM models
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Fine-tuning a large language model (LLM) or Stable Diffusion is the process of adjusting its parameters to perform better on a specific task or within a particular domain. While pre-trained models like GPT are great at general language understanding, they may not be as effective when applied to specialized fields. Fine-tuning helps make these models
If you’re a graphics designer or a small business owner or just helping someone with a logo design, we have simplified it to these 5 steps (note – the steps are same if you’re generating another AI based text to image file using Stable Diffusion AI model. You can jump straight to the instructions or
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In this technical bog, we provide a comprehensive tutorial on fine-tuning the Llama-3 model, a large language model (LLM), using the Qubrid AI Platform. The platform features an AI Hub with various models, including Llama-3, where users can write a text prompt and receive stunning response from large language models, enhanced natural language understanding, and
How to Fine-Tune Meta Llama 3 on Qubrid AI Model Studio Read More »
How to generate synthetic data on Qubrid AI Step 1: Navigate to Qubrid AI – Train | Tune | Deploy and login with your username and password or use google sign-in option. If you do not have an account please create an account,instructions here . Step 2: Upon successful login, you will be navigated to
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Getting Started with Fine-Tuning: Let’s roll up our sleeves and dive into the Qubrid AI platform. Whether you’re a seasoned AI practitioner or a newcomer to the field, Qubrid AI’s intuitive interface makes fine-tuning a breeze. Begin by navigating to the Qubrid AI Hub section. Here, you’ll find a range of pre-trained models ready for
7-Steps to Fine-Tune AI Models On Qubrid AI Model Studio Read More »
What Does Fine-tuning Do? Fine-tuning is a technique used to adapt a pre-trained model to a new task. By providing the model with your specific data, you can significantly improve its performance on your unique use case. What to Expect After Clicking “Fine-tune Notebook” Clicking the “Fine-tune Notebook” button will launch a single instance: On Which Instance
Introduction to Fine-tuning on Qubrid AI Model Studio Read More »
This section guides you through utilizing the QUBRID AI platform’s AI Model Studio to run inference on pre-trained models. Inference refers to the process of using a trained model to make predictions on new data. Getting Started: 6. Paste or upload your query data into the designated input field within the inference interface. 7. Click
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This section guides you through launching compute instances and utilizing Jupyter Notebook for your specific use case on the QUBRID AI platform. Launching a GPU Instance : Notes & Warnings: Launching a CPU Instance Notes & Warnings: Checking for Launched Notebooks (For Both CPU and GPU): If you’re unsure whether a notebook has been launched
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