Update README.md

README.md

@@ -9,22 +9,27 @@ To use the model:
 # Load model directly
 from transformers import AutoTokenizer, AutoModelForCausalLM
 
-tokenizer = AutoTokenizer.from_pretrained("Sourabh2/Chemistry_elements", trust_remote_code=True)
-model = AutoModelForCausalLM.from_pretrained("Sourabh2/Chemistry_elements", trust_remote_code=True)
+tokenizer = AutoTokenizer.from_pretrained("Sourabh2/Chemical_compund", trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained("Sourabh2/Chemical_compund", trust_remote_code=True)
 # Set up the device (GPU if available, otherwise CPU)
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 model = model.to(device)
-# Example usage
-messages = [
-    {
-        "role": "user",
-        "content": "hydrogen"
-    }
-]
-prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
-inputs = tokenizer(prompt, return_tensors='pt', padding=True, truncation=True)
-outputs = model.generate(**inputs, max_length=150, num_return_sequences=1)
-text = tokenizer.decode(outputs[0], skip_special_tokens=True)
-print(text.split("assistant")[1])
-# Decode and print output
-['Symbol: H', 'Atomic_Number: 1', 'Atomic_Weight: 1.008', 'Density: 0.0899', 'Melting_Point: 14.01', 'Boiling_Point: 20.28', 'Phase: Gas', 'Absolute_Melting_Point: 14.01']
+
+
+
+input_str = "Nobelium".lower()
+input_ids = tokenizer.encode(input_str, return_tensors='pt').to(device)
+
+output = model.generate(
+    input_ids,
+    max_length=200,
+    num_return_sequences=1,
+    do_sample=True,
+    top_k=8,
+    top_p=0.95,
+    temperature=0.1,
+    repetition_penalty=1.2
+)
+
+decoded_output = tokenizer.decode(output[0], skip_special_tokens=True)
+print(decoded_output)