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Fine Tuning Mistral 7b #141

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nourolive opened this issue Mar 21, 2024 · 3 comments
Open

Fine Tuning Mistral 7b #141

nourolive opened this issue Mar 21, 2024 · 3 comments

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@nourolive
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Can we fine-tune with Mistral for a custom dataset in the field of digital marketing/marketing communication?

@ateeq-pk
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Absolutely, Mistral 7b LLM is a great candidate for fine-tuning with a custom dataset in digital marketing/marketing communication.

For Details Check: Fine-tune Mistral-7b with Direct Preference Optimization

@ateeq-pk
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Its strength in natural language processing makes it ideal for tasks like:

  • Generating creative ad copy (headlines, social media posts)
  • Writing different marketing materials (email campaigns, website content)
  • Summarizing marketing research or competitor analysis
  • Creating targeted marketing personas

Here's what you'll need to consider for fine-tuning:

Data Preparation:

  • Collect relevant text data related to digital marketing. This could include existing marketing materials, competitor analysis reports, industry publications, or social media conversations.
  • Preprocess the data by cleaning it, removing irrelevant information, and ensuring consistency in format.

Fine-Tuning Process:

  • Several resources are available to guide you through the fine-tuning process. You can find tutorials that walk you through setting up the environment, preparing data, and using tools like Hugging Face Transformers or PEFT.
  • Consider using techniques like QLoRA for parameter-efficient fine-tuning, which can be helpful when dealing with limited datasets.

Hardware:

Fine-tuning Mistral 7b can be computationally expensive. While you can try it on a free Google Colab notebook with a GPU, a more powerful GPU like V100 or A100 will significantly improve performance.

Here are some additional tips:

  • Start with a smaller subset of your data for initial training and evaluation.
  • Carefully monitor the training process to avoid overfitting, where the model performs well on the training data but poorly on unseen data.
  • Evaluate the fine-tuned model on a separate hold-out test set to assess its generalizability.
  • By following these steps and leveraging available resources, you can fine-tune Mistral 7b to become a powerful tool for your digital marketing needs.

@nourolive
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thank you for these detailed information !

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