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test_model.py

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+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+import torch
+import json
+
+# Detect device
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+print(f"Device set to use: {device}")
+
+# Load base model and tokenizer
+base_model_name = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
+adapter_repo = "Harish2002/cli-lora-tinyllama"
+
+tokenizer = AutoTokenizer.from_pretrained(base_model_name)
+base_model = AutoModelForCausalLM.from_pretrained(base_model_name)
+model = PeftModel.from_pretrained(base_model, adapter_repo)
+model = model.to(device)
+model.eval()
+
+# Test prompts
+test_prompts = {
+    "Git": "How do I create a new branch and switch to it in Git?",
+    "Bash": "How to list all files including hidden ones?",
+    "Grep": "How do I search for a pattern in multiple files using grep?",
+    "Tar/Gzip": "How to extract a .tar.gz file?",
+    "Python venv": "How do I activate a virtual environment on Windows?"
+}
+
+# Run test and store results
+results = {}
+for topic, prompt in test_prompts.items():
+    inputs = tokenizer(prompt, return_tensors="pt").to(device)
+    with torch.no_grad():
+        outputs = model.generate(**inputs, max_new_tokens=128)
+    answer = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    results[topic] = {
+        "question": prompt,
+        "answer": answer
+    }
+    print(f"\n🧪 {topic}:\nQ: {prompt}\nA: {answer}")
+
+# Save to file
+with open("test_outputs.json", "w", encoding="utf-8") as f:
+    json.dump(results, f, indent=4)
+
+print("\n✅ All outputs saved to test_outputs.json")