Update README.md

README.md

@@ -37,58 +37,7 @@ This model is specifically designed for generating synthetic survey responses fr
 1. A detailed persona description
 2. A specific survey question
 
-### Python Example
-
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-# Load model and tokenizer
-model = AutoModelForCausalLM.from_pretrained("aryashah00/survey-finetuned-tinyllama-for-deployment", device_map="auto", trust_remote_code=True)
-tokenizer = AutoTokenizer.from_pretrained("aryashah00/survey-finetuned-tinyllama-for-deployment", trust_remote_code=True)
-#tokenizer = AutoTokenizer.from_pretrained(model_name)
-
-# Define persona and question
-persona = "A nurse who educates the child about modern medical treatments and encourages a balanced approach to healthcare"
-question = "How often was your pain well controlled during this hospital stay?"
-
-# Prepare prompts
-system_prompt = f"You are embodying the following persona: {{persona}}"
-user_prompt = f"Survey Question: {{question}}\n\nPlease provide your honest and detailed response to this question."
-
-# Create message format
-messages = [
-    {"role": "system", "content": system_prompt},
-    {"role": "user", "content": user_prompt}
-]
-
-# Apply chat template
-input_text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
-
-# Tokenize
-input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to(model.device)
-
-# Generate response
-import torch
-with torch.no_grad():
-    output_ids = model.generate(
-        input_ids=input_ids,
-        max_new_tokens=256,
-        temperature=0.7,
-        top_p=0.9,
-        do_sample=True
-    )
-
-# Decode
-output = tokenizer.decode(output_ids[0], skip_special_tokens=True)
-
-# Extract just the generated response
-response_start = output.find(input_text) + len(input_text)
-generated_response = output[response_start:].strip()
-
-print(f"Generated response: {{generated_response}}")
-```
-
-## Inference on CPU:
+## EXAMPLE Inference on CPU:
 
 ```python
 import torch
@@ -117,7 +66,7 @@ print(f"Model loaded successfully on: {next(model.parameters()).device}")
 
 # Example persona and survey question
 persona = "A caring mother who lost her first child due to a miscarriage."
-question = "Rate on a scale of 1(less likely) to 5(extremely likely):I deeply care about others"
+question = "Rate on a scale of 1(less likely) to 5(extremely likely) for the following question: I deeply care about others"
 
 # Format messages following chat template
 messages = [
@@ -136,7 +85,7 @@ with torch.no_grad():
     output_ids = model.generate(
         input_ids=input_ids,
         max_new_tokens=256,
-        temperature=0.7,
+        temperature=0.9,
         top_p=0.9,
         do_sample=True
     )