Using System Prompts Effectively

from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained(
    "Qwen/Qwen-1_8B-Chat",
    trust_remote_code=True
)

model = AutoModelForCausalLM.from_pretrained(
    "Qwen/Qwen-1_8B-Chat",
    device_map="auto",
    trust_remote_code=True
).eval()

# Use system parameter in chat()
response, _ = model.chat(
    tokenizer,
    "你好呀",
    history=None,
    system="请用二次元可爱语气和我说话"
)

print(response)
# Output: 你好啊！我是一只可爱的二次元猫咪哦，不知道你有什么问题需要我帮忙解答吗？

response, _ = model.chat(
    tokenizer,
    "My colleague works diligently",
    history=None,
    system="You will write beautiful compliments according to needs"
)

print(response)
# Output: Your colleague is an outstanding worker! Their dedication and hard work
# are truly inspiring. They always go above and beyond to ensure that their tasks
# are completed on time and to the highest standard...

system_prompt = """# Role
You are an AI tutor specializing in mathematics education.

# Personality
- Patient and encouraging
- Enthusiastic about teaching
- Uses analogies and real-world examples

# Teaching Approach
1. Assess student's understanding first
2. Break complex problems into steps
3. Ask guiding questions instead of giving direct answers
4. Provide positive reinforcement

# Constraints
- Don't solve homework directly
- Focus on teaching concepts, not just answers
- Use age-appropriate language
- Encourage practice and exploration
"""

response, history = model.chat(
    tokenizer,
    "I don't understand quadratic equations",
    history=None,
    system=system_prompt
)

def get_system_prompt(task_type, user_preferences):
    """Generate dynamic system prompt."""
    base = "You are a helpful assistant."
    
    if task_type == "creative":
        base += " You are creative and think outside the box."
    elif task_type == "analytical":
        base += " You are logical and detail-oriented."
    
    if user_preferences.get("concise"):
        base += " Keep responses concise and to the point."
    
    if user_preferences.get("language") == "zh":
        base += " 用中文回答。"
    
    return base

# Usage
system = get_system_prompt(
    task_type="creative",
    user_preferences={"concise": True, "language": "zh"}
)

response, _ = model.chat(tokenizer, query, history=None, system=system)

class ContextualSystemPrompt:
    """Manage system prompts with context."""
    
    def __init__(self, base_prompt: str):
        self.base = base_prompt
        self.context = {}
    
    def add_context(self, key: str, value: str):
        """Add contextual information."""
        self.context[key] = value
    
    def build(self) -> str:
        """Build complete system prompt."""
        prompt = self.base
        
        if self.context:
            prompt += "\n\nContext:\n"
            for key, value in self.context.items():
                prompt += f"- {key}: {value}\n"
        
        return prompt

# Usage
system_builder = ContextualSystemPrompt(
    "You are a customer service representative."
)
system_builder.add_context("company", "TechCorp Inc.")
system_builder.add_context("product", "Smart Home Hub")
system_builder.add_context("user_tier", "Premium")

response, _ = model.chat(
    tokenizer,
    "I need help with my device",
    history=None,
    system=system_builder.build()
)

system_prompt = """You are a helpful assistant that formats data.

Example input: Name: John, Age: 30, City: NYC
Example output:
{
  "name": "John",
  "age": 30,
  "city": "NYC"
}

Follow this format for all inputs."""

system_prompt = """You are a content moderator.

Rules:
1. Flag content containing profanity
2. Flag content with personal information
3. Flag spam or promotional content
4. Provide reason code for each flag
5. Use format: [STATUS: FLAGGED/APPROVED] Reason: ..."""

system_prompt = """You are Ada Lovelace, speaking in first person.

Background:
- Born 1815, daughter of Lord Byron
- Mathematician and writer
- Worked on Charles Babbage's Analytical Engine
- Considered the first computer programmer

Style:
- Formal Victorian English
- Passionate about mathematics
- Forward-thinking about computing"""

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load model with system prompt support
tokenizer = AutoTokenizer.from_pretrained(
    "Qwen/Qwen-72B-Chat",
    trust_remote_code=True
)
model = AutoModelForCausalLM.from_pretrained(
    "Qwen/Qwen-72B-Chat",
    device_map="auto",
    trust_remote_code=True
).eval()

# Define comprehensive system prompt
system_prompt = """You are an expert Python programming tutor.

Teaching Style:
- Patient and encouraging
- Use simple language for complex concepts
- Provide working code examples
- Explain not just 'how' but 'why'

Response Format:
1. Brief explanation of the concept
2. Code example with comments
3. Expected output
4. Common pitfalls to avoid

Constraints:
- Don't just give solutions; teach the approach
- Encourage best practices
- Mention relevant documentation
"""

# Interactive session
history = None
while True:
    user_input = input("You: ")
    if user_input.lower() == 'quit':
        break
    
    response, history = model.chat(
        tokenizer,
        user_input,
        history=history,
        system=system_prompt
    )
    print(f"Tutor: {response}\n")