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

Model Card: Quantized Version

Model Name: Qwen2.5-3B-DataFusion-Instruct (Quantized)
File: qwen_datafusion
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

Training Data

Dataset Composition

Total QA Pairs: 265,180
Source Projects: 36 different repositories
Content Types: Code implementation, documentation, usage examples
Coverage: Comprehensive DataFusion ecosystem

Training Projects

Core DataFusion: datafusion, datafusion-ballista, datafusion-federation
DataFusion Extensions: datafusion-functions-json, datafusion-postgres, datafusion-python
Arrow Ecosystem: arrow-rs, arrow-zarr
Related Tools: blaze, exon, feldera, greptimedb, horaedb, influxdb
Modern Data Stack: iceberg-rust, LakeSoul, lance, openobserve, parseable

Data Quality Features

Structured JSONL format with source attribution
Code examples with best practices and common pitfalls
Error handling guidance and troubleshooting solutions
Performance optimization tips and best practices

Model Capabilities

Primary Strengths

Rust Programming Expertise
- Idiomatic Rust code generation
- DataFusion API usage patterns
- Error handling and testing best practices
- Performance optimization techniques
DataFusion SQL Mastery
- Complex SQL query construction
- Table provider implementations
- UDF (User-Defined Function) development
- Query optimization and execution planning
Data Processing Knowledge
- Arrow format operations
- Parquet file handling
- Data transformation pipelines
- Streaming and batch processing
System Architecture Understanding
- Distributed query execution
- Federation and integration patterns
- Observability and tracing
- Performance monitoring

Technical Domains

SQL Engine Internals: Query planning, optimization, execution
Data Formats: Arrow, Parquet, JSON, CSV, Avro
Storage Systems: Object storage, databases, file systems
Distributed Computing: Ray, Ballista, cluster management
Streaming: Real-time data processing, windowing, aggregations

Usage Instructions

System Prompt

The model is configured with a specialized system prompt:

You are a helpful, concise, and accurate coding assistant specialized in Rust and the DataFusion SQL engine. Always provide high-level, idiomatic Rust code, DataFusion SQL examples, clear documentation, and robust test cases. Your answers should be precise, actionable, and end with '### End'.

Prompt Template

### Instruction:
{{ .Prompt }}

### Response:

Stop Sequences

### Instruction:
### Response:
### End

Generation Parameters

num_predict: 1024 (maximum tokens to generate)
repeat_penalty: 1.2 (prevents repetitive output)
temperature: 0.7 (balanced creativity vs consistency)
top_p: 0.9 (nucleus sampling for quality)

Performance Characteristics

Accuracy

Code Generation: High accuracy for Rust and DataFusion patterns
SQL Queries: Correct syntax and best practices
Documentation: Clear, actionable explanations
Error Handling: Comprehensive coverage of common issues

Efficiency

Main Model: Highest accuracy, larger memory footprint
Quantized Model: Optimized inference, reduced memory usage
Response Time: Fast generation with proper stop sequences
Memory Usage: Efficient token management

Use Cases

Development

Code Generation: Generate Rust functions and DataFusion queries
Debugging: Identify and fix common issues
Documentation: Create clear technical explanations
Testing: Generate test cases and validation code

Learning

Tutorial Creation: Step-by-step learning materials
Best Practices: Learn recommended approaches
Pattern Recognition: Understand common design patterns
API Exploration: Discover available functionality

Production Support

Query Optimization: Improve SQL performance
Troubleshooting: Resolve runtime issues
Integration: Connect different data sources
Monitoring: Set up observability and tracing

Limitations and Considerations

Technical Limitations

Context Window: Limited to training data scope
Real-time Updates: May not reflect latest API changes
Complex Queries: Very complex scenarios may require human review
Edge Cases: Unusual configurations may need manual intervention

Best Practices

Verify Output: Always review generated code before deployment
Test Thoroughly: Validate generated queries and functions
Stay Updated: Check for newer model versions
Human Oversight: Use as assistant, not replacement for expertise

Installation and Setup

Ollama (Recommended)

# Pull the model
ollama pull jaro/qwen_datafusion

# Run inference
ollama run jaro/qwen_datafusion

Model Comparison

Aspect	Main Model (5.8GB)	Quantized Model (1.8GB)
Accuracy	Highest	High (slight degradation)
Memory Usage	Higher	Lower
Inference Speed	Standard	Faster
Deployment	Development/Production	Production/Resource-constrained
Use Case	Maximum quality	Balanced performance

Resources

DataFusion Documentation: https://docs.datafusion.org/
Apache Arrow: https://arrow.apache.org/
Rust Programming Language: https://www.rust-lang.org/
Training Dataset: Available in https://huggingface.co/datasets/yarenty/datafusion_QA

Citation

When using this model in research or publications, please cite:

@software{qwen_datafusion,
  title={Qwen2.5-3B-DataFusion-Instruct: A Specialized Model for DataFusion Ecosystem},
  author={Fine-tuned on DataFusion Ecosystem QA Dataset},
  year={2025},
  url={https://github.com/yarenty/trainer},
  license={Apache-2.0}
}

License

This model is licensed under the Apache 2.0 License. See the LICENSE file for full details.

This model represents a significant advancement in specialized AI assistance for the DataFusion ecosystem, combining the power of large language models with domain-specific expertise in data processing and Rust programming.