model_info.json

{
  "Project Name": "Financial LLaMA Fine-tuning",
  "Base Model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
  "Training Dataset": "Josephgflowers/Finance-Instruct-500k",
  "Fine-tuning Method": "LoRA (Low-Rank Adaptation)",
  "Save Time": "2025-08-08 04:51:47",
  "File List": [
    "README.md",
    "adapter_model.safetensors",
    "adapter_config.json",
    "training_args.bin",
    "chat_template.jinja",
    "tokenizer_config.json",
    "special_tokens_map.json",
    "tokenizer.json",
    "training_config.json",
    "test_results.json"
  ],
  "Local Save Path": "C:\\Users\\Timber's Pad\\OneDrive\\Desktop\\JobHunting\\Project2_FineTune\\Project2_FineTune\\FineTuneSave",
  "File Description": {
    "adapter_config.json": "LoRA configuration file",
    "adapter_model.safetensors": "LoRA weight file",
    "tokenizer.json": "Tokenizer file",
    "tokenizer_config.json": "Tokenizer configuration",
    "special_tokens_map.json": "Special token mapping"
  },
  "Usage Instructions": [
    "1. Extract zip file to target folder",
    "2. Use the following code to load the model:",
    "   from peft import PeftModel",
    "   from transformers import AutoModelForCausalLM, AutoTokenizer",
    "   base_model = AutoModelForCausalLM.from_pretrained('meta-llama/Meta-Llama-3.1-8B-Instruct')",
    "   model = PeftModel.from_pretrained(base_model, 'path/to/model')",
    "   tokenizer = AutoTokenizer.from_pretrained('path/to/model')"
  ]
}