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model_info.json

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+{
+  "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')"
+  ]
+}