Update README.md

README.md

@@ -22,7 +22,37 @@ It performed very well than expected. It do first reasoning and than generate re
 It do reasoning separately no special tokens or in response reasoning.
 Below is inference code.
 ```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
 
+MAX_REASONING_TOKENS = 1024
+MAX_RESPONSE_TOKENS = 512
+
+model_name = "KingNish/Reasoning-0.5b"
+
+model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto")
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "Which is greater 9.9 or 9.11 ??"
+messages = [
+    {"role": "user", "content": prompt}
+]
+
+# Generate reasoning
+reasoning_template = tokenizer.apply_chat_template(messages, tokenize=False, add_reasoning_prompt=True)
+reasoning_inputs = tokenizer(reasoning_template, return_tensors="pt").to(model.device)
+reasoning_ids = model.generate(**reasoning_inputs, max_new_tokens=MAX_REASONING_TOKENS)
+reasoning_output = tokenizer.decode(reasoning_ids[0, reasoning_inputs.input_ids.shape[1]:], skip_special_tokens=True)
+
+# print("REASONING: " + reasoning_output)
+
+# Generate answer
+messages.append({"role": "reasoning", "content": reasoning_output})
+response_template = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+response_inputs = tokenizer(response_template, return_tensors="pt").to(model.device)
+response_ids = model.generate(**response_inputs, max_new_tokens=MAX_RESPONSE_TOKENS)
+response_output = tokenizer.decode(response_ids[0, response_inputs.input_ids.shape[1]:], skip_special_tokens=True)
+
+print("ANSWER: " + response_output)
 ```
 
 - **Trained by:** [Nishith Jain](https://huggingface.co/KingNish)