Step-by-Step Guide to Fine-Tuning a Mistral 7B Model

Table of Contents

1. Introduction
2. Creating the Data Set
3. Formatting the Data to Json L
4. Uploading the Data Set to the Together AI Platform
5. Running the Fine-Tuning Job
6. Downloading the Fine-Tuned Model
7. Converting the Model to a Usable Format
8. Testing the Model in LM Studio
9. Conclusion
10. FAQ

Introduction

In this article, we will be going through the step-by-step process of fine-tuning a Mistal 7B model. We will start by generating a data set using our local model, then use the Together AI platform to perform the fine-tuning job. After that, we will download the fine-tuned model, convert it into a usable format, upload it to LM Studio, and finally test its performance. Let's get started!

Creating the Data Set

To create our data set, we took inspiration from a Reddit post on local llama. The post provided examples of system prompts and user inputs, which we used to emulate the desired data set. We created a Python script that randomly selected questions and generated corresponding bot responses. The script created 400 examples in the desired format. After running the script, we reviewed the responses and removed any outliers or unnecessary lines. The final data set was then ready for formatting.

Formatting the Data to Json L

To format the data set, we used a simple script called "text to Json L." This script read the text files containing the examples and converted them into the desired Json L format. Each line of the text files represented one example, with the system Prompt, user input, and bot response. The script looped through all the examples and appended them to a Json L file. In the end, we obtained a Json L file with 336 lines, each representing one example.

Uploading the Data Set to the Together AI Platform

With the Json L file ready, we proceeded to upload it to the Together AI platform. We first checked the data set using a script that validated its format and content. Once the check passed, we moved on to the data set upload. We provided our Together AI API key and executed the upload script. The data set was uploaded successfully, and we obtained a file ID for the uploaded data set.

Running the Fine-Tuning Job

Now that the data set was uploaded, we could start the fine-tuning job. We provided the necessary configurations, such as the model name (Mistal 7B), the batch size, and the number of training epochs. We also included our Weights and Biases API key to Visualize the loss curve during training. After running the script, the fine-tuning job started, and we monitored its progress on the Together AI platform. The job took approximately 9 hours to complete.

Downloading the Fine-Tuned Model

Once the fine-tuning job finished, we downloaded the fine-tuned model. The model files were saved in a folder called "crazy bot." We then prepared the model for conversion to a usable format.

Converting the Model to a Usable Format

To make the model usable, we converted it to the GG UF format. We used a script called "lama CPP" to perform the conversion. The script required specifying the path to the model and the output file format. After running the script, we obtained the converted model file.

Testing the Model in LM Studio

With the converted model in HAND, we could test its performance in LM Studio. We uploaded the model to LM Studio and selected it for testing. We configured the server for GPU offloading and removed the system prompt. We then tested the model by interacting with it using speech input. The model provided responses, although some were off the rails due to the limited examples and data quality.

Conclusion

In conclusion, we successfully fine-tuned a Mistal 7B model using a locally created data set and the Together AI platform. The process involved generating the data set, formatting it to Json L, uploading it to Together AI, running the fine-tuning job, downloading the fine-tuned model, converting it to a usable format, and testing it in LM Studio. Although the obtained results were not optimal due to limited examples and data quality, the step-by-step guide provides insights into the fine-tuning process.

FAQ

Q: Can I follow the exact steps Mentioned in the article to achieve great results in fine-tuning a model? A: While you can follow the general recipe provided in this article, achieving great results in model fine-tuning requires a larger and more carefully curated data set. It is recommended to invest more time and effort in creating a high-quality data set for better results.

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Q: Can I access the scripts and instructions mentioned in the article? A: Yes, the scripts and instructions mentioned in the article are available in the GitHub repository. You can become a member of the Channel to gain access to the repository and download the resources.

Q: How long does the fine-tuning job typically take to complete? A: The duration of the fine-tuning job can vary depending on various factors such as the model size, complexity of the task, and available computational resources. In the example mentioned in the article, the fine-tuning job took approximately 9 hours to complete.

Q: Is the fine-tuning process applicable to other models and platforms? A: Yes, the fine-tuning process described in the article can be applied to other models and platforms with necessary modifications. However, it is important to refer to the specific documentation and guidelines provided by the model and platform developers for accurate implementation.

Q: Can I use a smaller data set for fine-tuning, or is a larger data set required for optimal results? A: While it is possible to fine-tune a model with a smaller data set, larger data sets generally yield better results. A larger data set provides more diverse and representative examples, allowing the model to learn more effectively. It is recommended to aim for a larger and high-quality data set for optimal fine-tuning outcomes.

Resources:

• Together AI
• LM Studio
• Weights and Biases