Qwen2.5-1.5B-Auto-FunctionCaller

Model Details

• Model Name: Qwen2.5-1.5B-Auto-FunctionCaller
• Base Model: Qwen/Qwen2.5-1.5B
• Model Type: Language Model fine-tuned for Function Calling.
• Recommended Quantization: Qwen2.5-1.5B-Auto-FunctionCaller.Q4_K_M_I.gguf
  • This GGUF file using Q4_K_M quantization with Importance Matrix is recommended as offering the best balance between performance and computational efficiency (inference speed, memory usage) based on evaluation.

Intended Use

• Primary Use: Function calling extraction from natural language queries within an automotive context. The model is designed to identify user intent and extract relevant parameters (arguments/slots) for triggering vehicle functions or infotainment actions.
• Research Context: This model was specifically developed and fine-tuned as part of a research publication investigating the feasibility and performance of Small Language Models (SLMs) for function-calling tasks in resource-constrained automotive environments.
• Target Environment: Embedded systems or edge devices within vehicles where computational resources may be limited.
• Out-of-Scope Uses: General conversational AI, creative writing, tasks outside automotive function calling, safety-critical vehicle control.

Performance Metrics

The following metrics were evaluated on the Qwen2.5-1.5B-Auto-FunctionCaller.Q4_K_M_I.gguf model:

• Evaluation Setup:
  • Total Evaluation Samples: 2074
• Performance:
  • Exact Match Accuracy: 0.8414
  • Average Component Accuracy: 0.9352
• Efficiency & Confidence:
  • Throughput: 10.31 tokens/second
  • Latency (Per Token): 0.097 seconds
  • Latency (Per Instruction): 0.427 seconds
  • Average Model Confidence: 0.9005
  • Calibration Error: 0.0854

Note: Latency and throughput figures are hardware-dependent and should be benchmarked on the target deployment environment.

Limitations

• Domain Specificity: Performance is optimized for automotive function calling. Generalization to other domains or complex, non-structured conversations may be limited.
• Quantization Impact: The Q4_K_M_I quantization significantly improves efficiency but may result in a slight reduction in accuracy compared to higher-precision versions (e.g., FP16).
• Complex Queries: May struggle with highly nested, ambiguous, or unusually phrased requests not well-represented in the fine-tuning data.
• Safety Criticality: This model is not intended or validated for safety-critical vehicle operations (e.g., braking, steering). Use should be restricted to non-critical systems like infotainment and comfort controls.
• Bias: Like any model, performance and fairness depend on the underlying data. Biases present in the fine-tuning or evaluation datasets may be reflected in the model's behavior.

Training Data (Summary)

The model was fine-tuned on a synthetic dataset specifically curated for automotive function calling tasks. Details will be referenced in the associated publication.

Citation

• Systematic Deployment of Small Language Models to Edge Devices - FEV.io
• 2025 JSAE Annual Congress (Spring) / Publication code : 20255372