README.md

metadata

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

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