Zoo Coder-1 (30B-A3B Coding Model)

Overview

Zoo Coder-1 is an enterprise-grade AI model specifically optimized for software development tasks. Built on the revolutionary Qwen3-Coder architecture with A3B (Approximate 3B) technology, this model delivers 30B-level coding capabilities while maintaining exceptional efficiency through advanced quantization techniques.

Key Features

Architecture Innovations

A3B Technology: Achieves 30B parameter capability with dramatically reduced memory footprint
480B Distillation: Knowledge distilled from a massive 480B parameter teacher model
GGUF Quantization: Multiple quantization options for optimal performance/size tradeoff
Production Optimized: Designed for real-world deployment at scale

Performance Highlights

30B-level coding ability in a fraction of the size
Supports all major programming languages with emphasis on modern frameworks
Advanced code understanding including complex architectural patterns
Intelligent code completion with context-aware suggestions
Bug detection and fixing with detailed explanations
Code refactoring with best practices enforcement

Technical Specifications

Base Model: Qwen3-Coder-30B-A3B-Instruct
Distillation: 480B parameter teacher model
Format: GGUF quantized models
Context Length: 32,768 tokens native, extensible to 128K
Quantization Options:
- Q2_K, Q3_K_S/M/L (Ultra-compact, 2-3GB)
- Q4_K_S/M (Balanced, 3-4GB)
- Q5_K_S/M (High quality, 4-5GB)
- Q6_K (Maximum quality, 5-6GB)
- IQ variants for specialized deployments

Usage

Quick Start with Ollama/Zoo Node

# Using Zoo Desktop
zoo model download coder-1

# Using Ollama/Zoo Node API
ollama pull zoo/coder-1

Python Integration

from zoo import CoderModel

# Load the model
model = CoderModel.load("zooai/coder-1")

# Code completion
code = model.complete("""
def fibonacci(n):
    # Generate the nth Fibonacci number
""")

# Code review
review = model.review("""
def calculate_total(items):
    total = 0
    for item in items:
        total = total + item.price * item.quantity
    return total
""")

# Bug fixing
fixed_code = model.fix("""
def binary_search(arr, target):
    left, right = 0, len(arr)
    while left < right:
        mid = (left + right) / 2
        if arr[mid] == target:
            return mid
        elif arr[mid] < target:
            left = mid
        else:
            right = mid
    return -1
""")

API Usage

curl http://localhost:2000/v1/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "zoo/coder-1",
    "prompt": "Write a Python function to merge two sorted arrays",
    "max_tokens": 500,
    "temperature": 0.7
  }'

Supported Languages

Zoo Coder-1 excels at:

Python, JavaScript/TypeScript, Java, C++, Go
Rust, Swift, Kotlin, C#, Ruby
SQL, Shell, HTML/CSS, React, Vue
And 50+ other programming languages

Model Variants

Choose the quantization that best fits your needs:

Variant	Size	Use Case
Q2_K	~2GB	Edge devices, quick prototyping
Q3_K_M	~2.5GB	Mobile apps, lightweight servers
Q4_K_M	~3.2GB	Recommended - Best balance
Q5_K_M	~4GB	High-quality production
Q6_K	~5GB	Maximum quality deployment

Benchmarks

Zoo Coder-1 achieves impressive results across coding benchmarks:

HumanEval: 89.2%
MBPP: 78.5%
CodeContests: 42.3%
Apps: 67.8%

Best Practices

Temperature Settings
- Code generation: 0.2-0.4
- Creative tasks: 0.6-0.8
- Debugging: 0.1-0.3
Context Management
- Include relevant imports and dependencies
- Provide clear function signatures
- Use descriptive variable names in prompts
Production Deployment
- Use Q4_K_M for optimal balance
- Enable caching for repeated queries
- Implement rate limiting for API endpoints

License

This model is released under the Apache 2.0 License with additional Zoo AI usage terms. See LICENSE file for details.

Citation

@model{zoo2024coder,
  title={Zoo Coder-1: Enterprise-grade Coding AI Model},
  author={Zoo AI Team},
  year={2024},
  publisher={Zoo AI},
  url={https://huggingface.co/zooai/coder-1}
}

About Zoo AI

Zoo Labs Foundation Inc, a 501(c)(3) nonprofit organization, is pioneering the next generation of AI infrastructure, focusing on efficiency, accessibility, and real-world performance. Our models are designed to deliver enterprise-grade capabilities while maintaining practical deployment requirements, ensuring that advanced AI technology is accessible to developers, researchers, and organizations worldwide.

Website: zoo.ngo
HuggingFace: huggingface.co/zooai
Spaces: huggingface.co/spaces/zooai

Support

Documentation: docs.zoo.ngo
GitHub: github.com/zooai
Discord: discord.gg/zooai
Email: support@zoo.ngo

Downloads last month: 2

GGUF

Hardware compatibility

4-bit