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README.md

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+---
+library_name: transformers
+license: apache-2.0
+license_link: https://huggingface.co/zooai/coder-1/blob/main/LICENSE
+pipeline_tag: text-generation
+tags:
+  - zoo
+  - coder
+  - coding
+  - a3b
+  - enterprise
+  - gguf
+  - 30b
+---
+
+# Zoo Coder-1 (30B-A3B Coding Model)
+<a href="https://zoo.ngo/" target="_blank" style="margin: 2px;">
+    <img alt="Zoo AI" src="https://img.shields.io/badge/💻%20Zoo%20Coder--1%20-EF4444" style="display: inline-block; vertical-align: middle;"/>
+</a>
+<a href="https://zoo.ngo/" target="_blank" style="margin: 2px;">
+    <img alt="501(c)(3)" src="https://img.shields.io/badge/501(c)(3)-Nonprofit-blue" style="display: inline-block; vertical-align: middle;"/>
+</a>
+
+## 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
+
+```bash
+# Using Zoo Desktop
+zoo model download coder-1
+
+# Using Ollama/Zoo Node API
+ollama pull zoo/coder-1
+```
+
+### Python Integration
+
+```python
+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
+
+```bash
+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
+
+1. **Temperature Settings**
+   - Code generation: 0.2-0.4
+   - Creative tasks: 0.6-0.8
+   - Debugging: 0.1-0.3
+
+2. **Context Management**
+   - Include relevant imports and dependencies
+   - Provide clear function signatures
+   - Use descriptive variable names in prompts
+
+3. **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
+
+```bibtex
+@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](https://zoo.ngo)
+- **HuggingFace**: [huggingface.co/zooai](https://huggingface.co/zooai)
+- **Spaces**: [huggingface.co/spaces/zooai](https://huggingface.co/spaces/zooai)
+
+## Support
+
+- Documentation: [docs.zoo.ngo](https://docs.zoo.ngo)
+- GitHub: [github.com/zooai](https://github.com/zooai)
+- Discord: [discord.gg/zooai](https://discord.gg/zooai)
+- Email: [email protected]