Local Code Arena MBPP: Qwen3 1.7B Benchmark Telemetry

Local Code Arena Telemetry captures raw evaluation metrics and execution logs from running the Mostly Basic Python Problems benchmark against the Qwen3 1.7B parameter model. The dataset was created by ShahzebKhoso and last updated on 2026-05-29. It provides a direct point of comparison for evaluating next-generation AI models on consumer hardware.

Use Cases

• Benchmarking code generation models based on MBPP problem-solving performance.
• Analyzing execution telemetry logs for model inference behavior.
• Comparing generalist model architectures against specialized ones based on benchmark results.
• Studying structural syntax outputs from language models on Python coding tasks.

Strengths

• Provides raw evaluation metrics from a standardized benchmark (MBPP).
• Focuses on a specific, named model architecture (Qwen3 1.7B).
• Captures execution telemetry logs, which may include detailed runtime information.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

huggingface

Collection Method

Evaluation of the MBPP benchmark against the Qwen3 1.7B model.

Freshness

Last updated 2026-05-29 06:20:02; freshness should be verified.