Best Practices

This guide outlines best practices for using local_llm_kit effectively and efficiently.

Model Selection

Choosing the Right Model

Consider your requirements: * Task complexity * Response quality needs * Performance constraints * Resource availability
Match model size to available resources: * 7B models for most use cases * 13B+ models for higher quality * Quantized models for resource constraints
Consider specialization: * Chat models for dialogue * Code models for programming * Multi-lingual models for language tasks

Performance Optimization

Memory Management

Token Management: .. code-block:: python

client = LLMClient(model=”llama2”) client.enable_memory(max_tokens=1000)

# Regularly clear memory client.clear_memory()

# Monitor token usage response = client.chat.completions.create(…) print(f”Used tokens: {response.usage.total_tokens}”)
Batch Processing: .. code-block:: python

# Process multiple prompts efficiently responses = [] for prompt in prompts:

response = client.chat.completions.create(
model=”llama2”, messages=[{“role”: “user”, “content”: prompt}], max_tokens=50 # Limit response length

) responses.append(response)

GPU Utilization

Optimal Settings: .. code-block:: python

client = LLMClient(
model=”llama2”, device=”cuda”, use_flash_attention=True, max_batch_size=32, dtype=”float16”

)
Memory Monitoring: * Use GPU monitoring tools * Adjust batch size based on memory * Consider gradient checkpointing

Error Handling

Robust Implementation

Basic Error Handling: .. code-block:: python

try:

response = client.chat.completions.create(
model=”llama2”, messages=[{“role”: “user”, “content”: “Hello”}]

)

except Exception as e:
logger.error(f”Chat completion failed: {e}”) # Implement fallback behavior
Specific Error Types: .. code-block:: python

from local_llm_kit.exceptions import (
ModelNotFoundError, TokenLimitError, InvalidRequestError

)

try:
# Your code here

except ModelNotFoundError:
# Handle missing model

except TokenLimitError:
# Handle token limit exceeded

except InvalidRequestError:
# Handle invalid parameters

Retry Logic

from tenacity import retry, stop_after_attempt, wait_exponential

@retry(stop=stop_after_attempt(3), wait=wait_exponential())
def get_completion(prompt):
    return client.chat.completions.create(
        model="llama2",
        messages=[{"role": "user", "content": prompt}]
    )

Prompt Engineering

Effective Prompts

Clear Instructions: .. code-block:: python

messages = [

{
“role”: “system”, “content”: “You are a helpful assistant. Provide clear, concise answers.”

}, {

“role”: “user”, “content”: “What is machine learning? Explain in simple terms.”

}

]
Context Management: .. code-block:: python

# Add relevant context messages = [

{“role”: “system”, “content”: system_prompt}, {“role”: “user”, “content”: “Remember this: I’m John.”}, {“role”: “assistant”, “content”: “Hello John!”}, {“role”: “user”, “content”: “What’s my name?”}

]

Temperature Control

For Deterministic Responses: .. code-block:: python

response = client.chat.completions.create(
model=”llama2”, messages=messages, temperature=0.0, # More deterministic top_p=1.0

)
For Creative Responses: .. code-block:: python

response = client.chat.completions.create(
model=”llama2”, messages=messages, temperature=0.8, # More creative top_p=0.9

)

Security Considerations

Input Validation

Sanitize Inputs: .. code-block:: python

def sanitize_input(text):
# Implement input sanitization return cleaned_text

user_input = sanitize_input(raw_input) response = client.chat.completions.create(

model=”llama2”, messages=[{“role”: “user”, “content”: user_input}]

)
Content Filtering: .. code-block:: python

def is_safe_content(text):
# Implement content safety checks return is_safe

if not is_safe_content(user_input):
raise SecurityError(“Unsafe content detected”)

Model Security

Model Access Control: .. code-block:: python

# Use environment variables for sensitive paths model_path = os.getenv(“LOCAL_LLM_KIT_MODEL_PATH”) client = LLMClient(

model=”llama2”, model_path=model_path

)
Rate Limiting: .. code-block:: python

from ratelimit import limits, sleep_and_retry

@sleep_and_retry @limits(calls=10, period=60) # 10 calls per minute def rate_limited_completion(prompt):

return client.chat.completions.create(
model=”llama2”, messages=[{“role”: “user”, “content”: prompt}]

)

Monitoring and Logging

Logging Setup

Basic Logging: .. code-block:: python

import logging

logging.basicConfig(level=logging.INFO) logger = logging.getLogger(“local_llm_kit”)

logger.info(“Initializing client…”) client = LLMClient(model=”llama2”)
Detailed Logging: .. code-block:: python

handler = logging.FileHandler(“llm.log”) handler.setFormatter(logging.Formatter(

‘%(asctime)s - %(name)s - %(levelname)s - %(message)s’

)) logger.addHandler(handler)

Performance Monitoring

Response Times: .. code-block:: python

import time

start_time = time.time() response = client.chat.completions.create(…) duration = time.time() - start_time

logger.info(f”Response time: {duration:.2f}s”)
Resource Usage: .. code-block:: python

import psutil

def log_resource_usage():
process = psutil.Process() logger.info(f”Memory usage: {process.memory_info().rss / 1024 / 1024:.2f} MB”) logger.info(f”CPU usage: {process.cpu_percent()}%”)

Testing and Validation

Unit Testing

Basic Tests: .. code-block:: python

import unittest

class TestLLMClient(unittest.TestCase):

def setUp(self):
self.client = LLMClient(model=”llama2”)

def test_completion(self):

response = self.client.chat.completions.create(
model=”llama2”, messages=[{“role”: “user”, “content”: “Hello”}]

) self.assertIsNotNone(response)
Mock Testing: .. code-block:: python

from unittest.mock import patch

@patch(“local_llm_kit.LLMClient”) def test_with_mock(mock_client):

mock_client.return_value.chat.completions.create.return_value = mock_response # Test implementation

Integration Testing

def test_end_to_end():
    client = LLMClient(model="llama2")

    # Test chat completion
    response1 = client.chat.completions.create(...)

    # Test function calling
    response2 = client.chat.completions.create(
        functions=[function_spec],
        function_call="auto",
        ...
    )

    # Test streaming
    response3 = client.chat.completions.create(
        stream=True,
        ...
    )

Deployment Best Practices

Environment Setup

Dependencies: .. code-block:: bash

pip install local-llm-kit[all] # Install all optional dependencies
Environment Variables: .. code-block:: bash

export LOCAL_LLM_KIT_MODEL_PATH=”/path/to/models” export LOCAL_LLM_KIT_CACHE_DIR=”/path/to/cache”

Production Configuration

Load Balancing: .. code-block:: python

clients = [
LLMClient(model=”llama2”, device=f”cuda:{i}”) for i in range(torch.cuda.device_count())

]
Health Checks: .. code-block:: python

def health_check():

try:

response = client.chat.completions.create(
model=”llama2”, messages=[{“role”: “user”, “content”: “test”}]

) return True

except Exception:
return False

Remember to regularly review and update these practices based on your specific use case and requirements.