traintrack-ai 0.1.3

TrainTrack Client: PyTorch training-time evaluation

TrainTrack

Training-time evaluation and win-rate tracking for LLMs.

TrainTrack helps you monitor model behavior during training by running automated LLM-as-a-Judge evaluations on every checkpoint.

Features

• 🚀 Real-time Metrics: Get immediate feedback on conciseness, helpfulness, and reasoning quality.
• 📊 Win-rate Tracking: Automatically track win-rates against an anchor checkpoint (baseline) or the previous step.
• 📚 Built-in Benchmarks: Integrated support for GPQA, MMLU-Pro, IFEval, and TruthfulQA.
• 🛠️ Seamless Integration: Works with standard PyTorch loops and HuggingFace Trainer.

Quick Installation

pip install traintrack-ai

Minimal Example

from traintrack import TrainTrackHook

# 1. Initialize the hook
hook = TrainTrackHook(
    model=model,
    tokenizer=tokenizer,
    run_name="my-first-run",
    datasets=["reasoning", "helpfulness"]
)

# 2. Capture a baseline (optional)
hook.capture_anchor()

# 3. Add to your training loop
for step, batch in enumerate(train_dataloader):
    # ... training logic ...

    hook.step(step)

Documentation

For full documentation and advanced configuration (custom metrics, rubrics, and category-based evaluation), visit: github.com/traintrack/traintrack