SGLang Template

The SGLang template provides structured generation capabilities for complex prompting, reasoning, and multi-step AI workflows. SGLang (Structured Generation Language) excels at complex reasoning tasks and controlled text generation.

What is SGLang?

SGLang is a domain-specific language for complex prompting and generation that provides:

🧠 Structured reasoning with multi-step prompts
🔄 Control flow for dynamic generation
📊 State management across generation steps
🎯 Guided generation with constraints
🔗 Chain-of-thought prompting patterns

Quick Start

from chutes.chute import NodeSelector
from chutes.chute.template.sglang import build_sglang_chute

chute = build_sglang_chute(
    username="myuser",
    model_name="microsoft/DialoGPT-medium",
    revision="main",
    node_selector=NodeSelector(
        gpu_count=1,
        min_vram_gb_per_gpu=16
    )
)

This creates a complete SGLang deployment with:

✅ Structured generation engine
✅ Multi-step reasoning capabilities
✅ Custom prompting patterns
✅ State-aware generation
✅ Auto-scaling based on demand

Function Reference

def build_sglang_chute(
    username: str,
    model_name: str,
    revision: str = "main",
    node_selector: NodeSelector = None,
    image: str | Image = None,
    tagline: str = "",
    readme: str = "",
    concurrency: int = 1,

    # SGLang-specific parameters
    max_new_tokens: int = 512,
    temperature: float = 0.7,
    top_p: float = 0.9,
    guidance_scale: float = 1.0,
    enable_sampling: bool = True,
    structured_output: bool = True,
    **kwargs
) -> Chute:

Required Parameters

: Your Chutes username
: HuggingFace model identifier

SGLang Configuration

: Maximum tokens to generate (default: 512)
: Sampling temperature (default: 0.7)
: Nucleus sampling parameter (default: 0.9)
: Guidance strength for controlled generation (default: 1.0)
: Enable probabilistic sampling (default: True)
: Enable structured output formatting (default: True)

Complete Example

from chutes.chute import NodeSelector
from chutes.chute.template.sglang import build_sglang_chute

# Build SGLang chute for complex reasoning
chute = build_sglang_chute(
    username="myuser",
    model_name="microsoft/DialoGPT-medium",
    revision="main",
    node_selector=NodeSelector(
        gpu_count=1,
        min_vram_gb_per_gpu=16
    ),
    tagline="Advanced reasoning with SGLang",
    readme="""
# Advanced Reasoning Engine

This chute provides structured generation capabilities using SGLang
for complex reasoning and multi-step AI workflows.

## Features
- Multi-step reasoning
- Structured output generation
- Chain-of-thought prompting
- Guided generation

## API Endpoints
- `/generate` - Basic text generation
- `/reason` - Multi-step reasoning
- `/structured` - Structured output generation
    """,

    # SGLang configuration
    max_new_tokens=1024,
    temperature=0.8,
    top_p=0.95,
    guidance_scale=1.2,
    structured_output=True
)

API Endpoints

Basic Generation

curl -X POST https://myuser-sglang-chute.chutes.ai/generate \
  -H "Content-Type: application/json" \
  -d '{
    "prompt": "Explain quantum computing",
    "max_tokens": 200,
    "temperature": 0.7
  }'

Structured Reasoning

curl -X POST https://myuser-sglang-chute.chutes.ai/reason \
  -H "Content-Type: application/json" \
  -d '{
    "problem": "What are the environmental impacts of renewable energy?",
    "steps": [
      "analyze_benefits",
      "identify_drawbacks",
      "compare_alternatives",
      "provide_conclusion"
    ]
  }'

Chain-of-Thought

curl -X POST https://myuser-sglang-chute.chutes.ai/chain-of-thought \
  -H "Content-Type: application/json" \
  -d '{
    "question": "If a train travels 60 mph for 2.5 hours, how far does it go?",
    "show_reasoning": true
  }'

SGLang Programs

Multi-Step Reasoning

@sglang.function
def analyze_problem(s, problem):
    s += f"Problem: {problem}\n\n"
    s += "Let me think about this step by step:\n\n"

    s += "Step 1: Understanding the problem\n"
    s += sglang.gen("understanding", max_tokens=100)
    s += "\n\n"

    s += "Step 2: Identifying key factors\n"
    s += sglang.gen("factors", max_tokens=100)
    s += "\n\n"

    s += "Step 3: Analysis\n"
    s += sglang.gen("analysis", max_tokens=150)
    s += "\n\n"

    s += "Conclusion:\n"
    s += sglang.gen("conclusion", max_tokens=100)

    return s

Structured Output

@sglang.function
def extract_information(s, text):
    s += f"Text: {text}\n\n"
    s += "Extract the following information:\n\n"

    s += "Name: "
    s += sglang.gen("name", max_tokens=20, stop=["\n"])
    s += "\n"

    s += "Age: "
    s += sglang.gen("age", max_tokens=10, regex=r"\d+")
    s += "\n"

    s += "Occupation: "
    s += sglang.gen("occupation", max_tokens=30, stop=["\n"])
    s += "\n"

    s += "Summary: "
    s += sglang.gen("summary", max_tokens=100)

    return s

Guided Generation

@sglang.function
def generate_story(s, theme, character):
    s += f"Write a story about {character} with the theme of {theme}.\n\n"

    # Structured story generation
    s += "Title: "
    s += sglang.gen("title", max_tokens=20, stop=["\n"])
    s += "\n\n"

    s += "Setting: "
    s += sglang.gen("setting", max_tokens=50, stop=["\n"])
    s += "\n\n"

    s += "Plot:\n"

    for i in range(3):
        s += f"Chapter {i+1}: "
        s += sglang.gen(f"chapter_{i+1}", max_tokens=200)
        s += "\n\n"

    s += "Conclusion: "
    s += sglang.gen("conclusion", max_tokens=100)

    return s

Advanced Features

Custom Templates

# Define custom reasoning template
reasoning_template = """
Problem: {problem}

Analysis Framework:
1. Context: What background information is relevant?
2. Constraints: What limitations or requirements exist?
3. Options: What are the possible approaches or solutions?
4. Evaluation: What are the pros and cons of each option?
5. Conclusion: What is the best approach and why?

Let me work through this systematically:
"""

chute = build_sglang_chute(
    username="myuser",
    model_name="microsoft/DialoGPT-medium",
    custom_templates={"reasoning": reasoning_template},
    guidance_scale=1.5  # Higher guidance for structured output
)

Constraint-Based Generation

# Configure constraints for specific output formats
chute = build_sglang_chute(
    username="myuser",
    model_name="microsoft/DialoGPT-medium",
    constraints={
        "json_format": True,
        "max_length": 500,
        "required_fields": ["summary", "key_points", "conclusion"],
        "stop_sequences": ["END", "STOP"]
    }
)

# Enable multi-modal capabilities
chute = build_sglang_chute(
    username="myuser",
    model_name="microsoft/DialoGPT-medium",
    multimodal=True,
    vision_enabled=True,
    audio_enabled=False
)

Model Recommendations

Small Models (< 7B parameters)

# Good for basic structured generation
NodeSelector(
    gpu_count=1,
    min_vram_gb_per_gpu=8,
    include=["rtx4090", "rtx3090"]
)

# Recommended models:
# - microsoft/DialoGPT-medium
# - google/flan-t5-base
# - microsoft/DialoGPT-small

Medium Models (7B - 13B parameters)

# Optimal for complex reasoning
NodeSelector(
    gpu_count=1,
    min_vram_gb_per_gpu=16,
    include=["rtx4090", "a100"]
)

# Recommended models:
# - microsoft/DialoGPT-large
# - google/flan-t5-large
# - meta-llama/Llama-2-7b-chat-hf

Large Models (13B+ parameters)

# Best for advanced reasoning
NodeSelector(
    gpu_count=1,
    min_vram_gb_per_gpu=24,
    include=["a100", "h100"]
)

# Recommended models:
# - meta-llama/Llama-2-13b-chat-hf
# - microsoft/DialoGPT-xlarge
# - google/flan-ul2

Use Cases

1. Educational Tutoring

tutoring_chute = build_sglang_chute(
    username="myuser",
    model_name="microsoft/DialoGPT-medium",
    tagline="AI Tutor with structured explanations",
    custom_templates={
        "explanation": "Explain {topic} step by step with examples",
        "quiz": "Create 5 questions about {topic} with explanations"
    }
)

2. Business Analysis

analysis_chute = build_sglang_chute(
    username="myuser",
    model_name="microsoft/DialoGPT-medium",
    structured_output=True,
    constraints={
        "format": "business_report",
        "sections": ["executive_summary", "analysis", "recommendations"]
    }
)

3. Creative Writing

writing_chute = build_sglang_chute(
    username="myuser",
    model_name="microsoft/DialoGPT-medium",
    temperature=0.9,  # Higher creativity
    top_p=0.95,
    enable_sampling=True
)

4. Code Generation

code_chute = build_sglang_chute(
    username="myuser",
    model_name="microsoft/DialoGPT-medium",
    temperature=0.3,  # Lower for more precise code
    structured_output=True,
    constraints={
        "language": "python",
        "include_comments": True,
        "include_tests": True
    }
)

Performance Optimization

Memory Optimization

# Optimize for memory efficiency
chute = build_sglang_chute(
    username="myuser",
    model_name="microsoft/DialoGPT-medium",
    max_new_tokens=256,  # Limit generation length
    batch_size=4,        # Smaller batches
    gradient_checkpointing=True
)

Speed Optimization

# Optimize for speed
chute = build_sglang_chute(
    username="myuser",
    model_name="microsoft/DialoGPT-medium",
    temperature=0.0,     # Deterministic (faster)
    top_p=1.0,          # No nucleus sampling
    enable_caching=True, # Cache intermediate results
    compile_model=True   # JIT compilation
)

Testing Your SGLang Chute

Python Client

import requests

# Test basic generation
response = requests.post(
    "https://myuser-sglang-chute.chutes.ai/generate",
    json={
        "prompt": "Analyze the benefits of renewable energy",
        "max_tokens": 300,
        "structured": True
    }
)

result = response.json()
print(result["generated_text"])

Complex Reasoning Test

# Test multi-step reasoning
response = requests.post(
    "https://myuser-sglang-chute.chutes.ai/reason",
    json={
        "problem": "Should companies adopt remote work policies?",
        "reasoning_steps": [
            "identify_stakeholders",
            "analyze_benefits",
            "analyze_drawbacks",
            "consider_implementation",
            "provide_recommendation"
        ]
    }
)

reasoning = response.json()
for step in reasoning["steps"]:
    print(f"{step['name']}: {step['output']}")

Troubleshooting

Common Issues

Generation too slow?

Reduce
Lower for deterministic output
Disable sampling with

Output not structured enough?

Increase
Enable
Add custom constraints

Memory errors?

Reduce batch size
Use smaller model
Increase GPU VRAM requirements

Inconsistent outputs?

Lower temperature for more deterministic results
Use seed for reproducible generation
Add stronger constraints

Best Practices

1. Template Design

# Good: Clear, structured templates
template = """
Task: {task}

Requirements:
- Be specific and detailed
- Provide examples
- Explain reasoning

Response:
"""

# Bad: Vague, unstructured
template = "Do {task}"

2. Constraint Configuration

# Effective constraints
constraints = {
    "max_length": 500,
    "required_sections": ["introduction", "analysis", "conclusion"],
    "format": "markdown",
    "tone": "professional"
}

3. Prompt Engineering

# Structure prompts for better results
def create_analysis_prompt(topic):
    return f"""
    Analyze the topic: {topic}

    Please structure your response as:
    1. Overview (2-3 sentences)
    2. Key factors (bullet points)
    3. Analysis (detailed explanation)
    4. Conclusion (summary and implications)

    Analysis:
    """

Next Steps

VLLM Template - High-performance LLM serving
Custom Templates Guide - Build custom templates
Advanced Prompting - Prompt engineering techniques
Multi-Model Workflows - Combine multiple models