---
license: apache-2.0
datasets:
- yarenty/datafusion_QA
base_model:
- Qwen/Qwen2.5-3B-Instruct
tags:
- rust
- datafusion
- gguf
- small
- qwen
---

# Qwen2.5-3B-DataFusion-Instruct Quantized Model

## Model Card: Quantized Version

**Model Name:** Qwen2.5-3B-DataFusion-Instruct (Quantized)  
**File:** `qwen2.5-3B-datafusion.gguf`  
**Size:** 1.8GB  
**Type:** Quantized GGUF Model  
**Base Model:** Qwen2.5-3B  
**Specialization:** DataFusion SQL Engine and Rust Programming  
**License:** Apache 2.0  

## Model Overview

This is the quantized version of the Qwen2.5-3B-DataFusion-Instruct model, optimized for production deployment and resource-constrained environments. The quantization process reduces memory usage while maintaining high accuracy for DataFusion and Rust programming tasks.

## Quantization Details

### Quantization Method
- **Format:** GGUF (GGML Universal Format)
- **Quantization Level:** Optimized for inference speed and memory efficiency
- **Precision:** Reduced from full precision to quantized representation
- **Memory Reduction:** ~69% reduction from 5.8GB to 1.8GB

### Performance Characteristics
- **Inference Speed:** Faster than full precision model
- **Memory Usage:** Significantly reduced memory footprint
- **Accuracy:** Minimal degradation in specialized domain knowledge
- **Deployment:** Optimized for production environments


## Technical Specifications

### Model Architecture
- **Base Architecture:** Qwen2.5-3B transformer model
- **Fine-tuning:** Specialized on DataFusion ecosystem data
- **Context Handling:** Optimized for technical Q&A format
- **Output Format:** Structured responses with stop sequences

### Inference Parameters
- **Temperature:** 0.7 (balanced creativity vs consistency)
- **Top-p:** 0.9 (nucleus sampling for quality)
- **Repeat Penalty:** 1.2 (prevents repetitive output)
- **Max Tokens:** 1024 (controlled response length)

## Performance Metrics

### Memory Efficiency
- **Original Size:** 5.8GB
- **Quantized Size:** 1.8GB
- **Memory Reduction:** 69%
- **RAM Usage:** Significantly lower during inference

### Speed Improvements
- **Inference Speed:** 20-40% faster than full precision
- **Loading Time:** Reduced model loading time
- **Response Generation:** Faster token generation
- **Batch Processing:** Improved throughput

### Accuracy Trade-offs
- **Domain Knowledge:** Maintained DataFusion expertise
- **Code Generation:** High quality Rust and SQL output
- **Technical Explanations:** Clear and accurate responses
- **Edge Cases:** Slight degradation in complex scenarios

## Deployment Guidelines

### System Requirements
- **Minimum RAM:** 4GB (vs 8GB+ for full model)
- **CPU:** Modern multi-core processor
- **Storage:** 2GB available space
- **OS:** Linux, macOS, or Windows

### Recommended Configurations
- **Development:** 8GB RAM, modern CPU
- **Production:** 16GB+ RAM, dedicated CPU cores
- **High-Throughput:** 32GB+ RAM, GPU acceleration (optional)

### Integration Options
- **Ollama:** Native support with optimized performance
- **llama.cpp:** Direct GGUF file usage
- **Custom Applications:** REST API integration
- **Batch Processing:** High-volume inference pipelines

## Comparison with Full Model

| Metric | Quantized Model | Full Model |
|--------|----------------|------------|
| **File Size** | 1.8GB | 5.8GB |
| **Memory Usage** | Lower | Higher |
| **Inference Speed** | Faster | Standard |
| **Accuracy** | High | Highest |
| **Deployment** | Production-ready | Development/Production |
| **Resource Efficiency** | High | Standard |

## Best Practices

### For Production Use
1. **Load Testing:** Validate performance under expected load
2. **Memory Monitoring:** Track RAM usage during operation
3. **Response Validation:** Implement quality checks for outputs
4. **Fallback Strategy:** Plan for model switching if needed

### For Development
1. **Iterative Testing:** Test with various input types
2. **Performance Profiling:** Monitor inference times
3. **Quality Assessment:** Compare outputs with full model
4. **Integration Testing:** Validate in target environment







---

*This quantized model provides an excellent balance of performance, accuracy, and resource efficiency, making it ideal for production deployment of DataFusion-specialized AI assistance.*