To train Llama 2, specifically the 7 billion parameter (7B) variant, you can follow these steps based on the information provided in the search results:

  1. Load the Base Model: Start by loading the pre-trained Llama 2 7B model from the Hugging Face Hub using transformers Python package. Set up the necessary configurations such as quantization settings if needed. Here's a sample command:

    from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
    base_model_id = "meta-llama/Llama-2-7b-hf"
    bnb_config = BitsAndBytesConfig(...)  # Adjust according to your needs
    base_model = AutoModelForCausalLM.from_pretrained(
    tokenizer = AutoTokenizer.from_pretrained(base_model_id)
  2. Data Preparation: Format your custom dataset in a way that matches the input requirements of the Llama 2 model. Ensure each entry consists of pairs of inputs and corresponding outputs, similar to the JSON format mentioned in the Reddit thread:

    {"input": "...", "output": "..." }
  3. Quantize and Fine-Tune Using QLoRA: Utilize Quantization and Low Rank Adaptation (QLoRA) methods to fine-tune the model on your custom dataset. Follow the instructions in the notebook linked in the blog post from Alternatively, watch the accompanying video walk-through for guidance.

  4. Monitor Performance: Evaluate the progress of your fine-tuning by testing the model against some examples from your dataset. Check whether the generated responses match the desired style or content.

  5. Save the Trained Model: Once satisfied with the performance, save the trained model under a unique name.

Please note that training a large language model requires substantial computational resources, typically involving multiple high-end GPUs. The exact hardware requirements depend on factors such as batch size, learning rate, and other hyperparameters. Consult the referenced materials for further details and adjustments tailored to your situation.