Update README.md

README.md

@@ -10,6 +10,91 @@ license: apache-2.0
 language:
 - en
 ---
+## Run in kaggle
+```
+# =========================================================
+# Install dependencies (Kaggle usually already has some)
+# =========================================================
+!pip install -q transformers peft accelerate bitsandbytes
+
+# =========================================================
+# Imports
+# =========================================================
+import torch
+from transformers import AutoProcessor, AutoModelForCausalLM
+from peft import PeftModel
+
+# =========================================================
+# Config
+# =========================================================
+BASE_MODEL = "google/gemma-4-E2B-it"
+LORA_MODEL = "rahul7star/gemma_4_lora"
+
+# =========================================================
+# Load processor
+# =========================================================
+processor = AutoProcessor.from_pretrained(BASE_MODEL)
+
+# =========================================================
+# Load base model
+# =========================================================
+model = AutoModelForCausalLM.from_pretrained(
+    BASE_MODEL,
+    torch_dtype=torch.float16,   # safer for Kaggle GPU
+    device_map="auto"
+)
+
+# =========================================================
+# Load LoRA adapter on top of base model
+# =========================================================
+model = PeftModel.from_pretrained(model, LORA_MODEL)
+
+# optional: merge LoRA for faster inference
+model = model.merge_and_unload()
+
+print("Model + LoRA loaded successfully 🚀")
+
+# =========================================================
+# Inference function
+# =========================================================
+def generate_response(user_input):
+    messages = [
+        {"role": "system", "content": "You are a helpful assistant."},
+        {"role": "user", "content": user_input},
+    ]
+
+    text = processor.apply_chat_template(
+        messages,
+        tokenize=False,
+        add_generation_prompt=True,
+        enable_thinking=False
+    )
+
+    inputs = processor(text=text, return_tensors="pt").to(model.device)
+    input_len = inputs["input_ids"].shape[-1]
+
+    with torch.no_grad():
+        outputs = model.generate(
+            **inputs,
+            max_new_tokens=512,
+            temperature=0.7,
+            top_p=0.9
+        )
+
+    response = processor.decode(
+        outputs[0][input_len:],
+        skip_special_tokens=True
+    )
+
+    return response
+
+
+# =========================================================
+# Test
+# =========================================================
+print(generate_response("Write a short joke about saving RAM."))
+```
+
 
 # Uploaded  model