No code finetune on TIR AI platform

-by E2E Networks Limited

Fine-Tuning Mistral-7B-Instruct with TIR AI: A No-Code Solution.The TIR AI platform revolutionizes the process of fine-tuning large language models by eliminating the need for coding expertise. This platform supports a variety of models, allowing you to customize them to fit your specific requirements effortlessly. In this blog, we will walk through the process of fine-tuning the Mistral-7B-Instruct model, but the concepts discussed here are applicable to any of the models supported by the TIR AI platform, including Gemma, Llama3, Llama2, and Mistral. Introduction to TIR AI Platform TIR AI is designed to simplify and streamline the process of training and fine-tuning large language models. With a user-friendly interface and powerful backend capabilities, TIR AI makes it possible for users to leverage advanced AI models without needing to write any code. This democratizes access to cutting-edge AI, enabling businesses and researchers to implement AI solutions more efficiently. Supported Models While this guide focuses on Mistral-7B-Instruct, the TIR AI platform supports a range of models, including:

  • Gemma: A versatile model known for its robustness in handling various NLP tasks.

  • Llama3: The latest iteration in the Llama series, offering significant improvements in performance and accuracy.

  • Llama2: A popular choice for many users due to its balance of performance and computational efficiency.

  • Mistral: A model that excels in generating high-quality, coherent text for complex prompts.

Way to Fine-Tune langage models on TIR

  • Setting Up Your Account

To get started, you need to create an account on the TIR AI platform. Once your account is set up, you can log in and access the dashboard. Guide to setting up an account on E2E networks click here

  • Choosing the Model

From the dashboard, navigate to the model selection area. Here, you can choose any language model from the list of available models. If you wish to fine-tune other models like Gemma, Llama3, Llama2, or Mistral, you can select them in a similar manner. Guide to finetune click here

  • Uploading Your Dataset

Fine-tuning requires a dataset that the model will learn from. The TIR AI platform allows you to upload your dataset in various formats (e.g., JSONL). Make sure your dataset is clean and well-organized to ensure the best results.

  • Configuring the Training Parameters

After uploading your dataset, you will need to configure the training parameters. This includes settings such as the number of training epochs, learning rate, batch size, and any other specific parameters relevant to the model you are fine-tuning.

  • Starting the Fine-Tuning Process

Once everything is configured, you can initiate the fine-tuning process with a simple click. The TIR AI platform handles all the heavy lifting, utilizing its powerful infrastructure to fine-tune the model efficiently.

  • Monitoring Progress

The platform provides real-time updates on the training progress, allowing you to monitor metrics such as loss and accuracy. You can also view logs and other relevant information to ensure the process is running smoothly.

  • Evaluating the Fine-Tuned Model

After the fine-tuning process is complete, you can evaluate the performance of the fine-tuned model using a validation dataset. This step is crucial to ensure that the model meets your expectations and performs well on your specific tasks. Perplexity, a metric that has emerged as a valuable tool for evaluating the effectiveness of Language Models (LLMs) and Generative AI Models. Perplexity is calculated using average cross-entropy, which in turn is calculated using the number of words in the data set and the predicted probability of a word (target word) as per the preceding context. The preceding context is typically represented by a fixed-length sequence of words that precede the target word. This length can vary depending on the specific model architecture and requirements.

  • Deploying the Model

Once you are satisfied with the performance of the fine-tuned model, TIR AI makes it easy to deploy the model. You can integrate the model into your applications via API, enabling seamless interaction with your AI-powered solutions.


Fine-tuning large language models has never been easier thanks to the TIR AI platform. By following these straight forward steps, you can customize or finetune powerful models like Mistral-7B-Instruct, Gemma, Llama3, Llama2, and Mistral to suit your unique needs—all without writing a single line of code. Embrace the power of AI and transform your workflows with TIR AI’s no-code solution.