Update README.md

README.md

@@ -89,18 +89,86 @@ Users should treat this model as an assistant, not a replacement for professiona
 
 ### Install Required Libraries
 
+### for colab
+
+1. Check python version
+```
+!python --version
+```
+2. Clean cache
+```
+!pip cache purge
+```
+3. install dependancy
 ```bash
-pip install transformers torch optimum
+!pip install torch==2.7.0 torchvision==0.22.0 torchaudio==2.7.0
+!pip install transformers accelerate
+!pip install bitsandbytes
+!pip install -U peft
+!pip install huggingface_hub[hf_xet]
+```
+4. check torch version
 ```
+import torch
+import torchvision
+import torchaudio
 
-```bash
+print(f"Torch version: {torch.__version__}")
+print(f"Torchvision version: {torchvision.__version__}")
+print(f"Torchaudio version: {torchaudio.__version__}")
+```
+5. Check if a CUDA device is available
+```
+import torch
+
+print(f"CUDA available: {torch.cuda.is_available()}")
+print(f"CUDA device count: {torch.cuda.device_count()}")
+print(f"Current device: {torch.cuda.current_device()}")
+print(f"Device name: {torch.cuda.get_device_name(0)}" if torch.cuda.is_available() else "No GPU found")
+```
+6. Result Shape
+```
+# Create a tensor and move it to GPU
+tensor = torch.randn(1000, 1000).cuda()
+
+# Perform a matrix multiplication on the GPU
+result = torch.matmul(tensor, tensor)
+
+print(f"Result shape: {result.shape}")
+```
+7. move model to GPU
+```
+import torch.nn as nn
+import torch.optim as optim
+
+# Sample neural network
+model = nn.Sequential(
+    nn.Linear(1000, 500),
+    nn.ReLU(),
+    nn.Linear(500, 10)
+)
+
+# Move the model to the GPU
+model = model.cuda()
+
+# Sample input data (1000 samples, 1000 features)
+inputs = torch.randn(1000, 1000).cuda()
+
+# Forward pass
+output = model(inputs)
+print(output.shape)
+```
+8. Load model
+```
 from transformers import AutoTokenizer, AutoModelForCausalLM
 
 model_name = "himel06/DoctorHimel_V1"
 tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", torch_dtype="auto")
-```
 
+# Load model without adapters or LoRA configuration
+model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", torch_dtype="auto", low_cpu_mem_usage=True)
+```
+9. Prompt template
 ```bash
 prompt_template = """
 Below is a medical question. Please provide a detailed and accurate response based on your knowledge.
@@ -111,7 +179,7 @@ Below is a medical question. Please provide a detailed and accurate response bas
 ### Answer:
 """
 ```
-
+10. Question template
 ```bash
 question = """A 61-year-old woman with a long history of involuntary urine loss during activities like coughing or
               sneezing but no leakage at night undergoes a gynecological exam and Q-tip test. Based on these findings,
@@ -120,7 +188,7 @@ question = """A 61-year-old woman with a long history of involuntary urine loss
 input_text = prompt_template.format(question)
 
 ```
-
+11. Output template
 ```bash
 inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
 outputs = model.generate(