NO MORE TRAINING COMPUTE WASTED

Loss functions don't tell the full story.

Measure your
LLM on what
actually matters—

CUSTOMER SERVICE

continuously as you train.

Audit real-world skills in sync with every parameter update.

>> Get Started with Just 3 Lines of Code

Plug and play with common LLM frameworks.

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                        from traintrack import TrainTrackHook
                    
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                        # > Initialize the hook
                    
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                        hook = TrainTrackHook(
                    
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                        model=model,
                    
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                        tokenizer=tokenizer,
                    
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                        run_name="my-finetune-v1",
                    
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                        dataset="reasoning",
                    
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                        )
                    
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                        hook.step(step, epoch=epoch) 
                    
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                        from traintrack import TrainTrackCallback
                    
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                        # > Add callback to Trainer
                    
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                        callback = TrainTrackCallback(
                    
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                        run_name="my-hf-run",
                    
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                        dataset="reasoning"
                    
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                        )
                    
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                        trainer = Trainer(..., callbacks=[callback]) 
                    
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                        from traintrack import CreateTrainTrackTinkerEvaluator
                    
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                        # > Set cadence once in evaluator config
                    
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                        evaluator = CreateTrainTrackTinkerEvaluator(
                    
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                        run_name="my-tinker-run",
                    
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                        model_name="meta-llama/Llama-3.1-8B",
                    
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                        categories=["reasoning"],
                    
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                        eval_every_steps=10,
                    
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                        )
                    
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                        evaluator.run(training_client=training_client, step=step)

Systematic visibility for the next generation of LLMs.

TrainTrack isn't just another logging tool. It's a behavioral observability platform designed to catch failing experiments before they waste your budget. By creating high-resolution Behavior Curves, we turn opaque training runs into queryable data streams.

analytics

Pareto Frontier

Visualize trade-offs between speed, cost, and quality across multiple behavioral dimensions.

compare_arrows

Side-by-Side Diffs

Instantly compare checkpoints. See how training shifts behaviors like coding or math.

WIN RATE DROP DETECTED

Run: qwen-math-finetune

balance

LLM as a Judge

> Go beyond cross-entropy.
> Semantic evaluation with state-of-the-art judge models.

notifications_active

Real-time Alerting

> Custom regression rules.
> Instant Slack/Email notifications.

trending_down

Cost Optimization

> Terminate zombie runs.
> Save GPU compute resources.

>> Why Teams Choose TrainTrack

TrainTrack gives ML teams the visibility they need to train better models, faster.

visibility

Full Visibility

See exactly how your model's behavior evolves.

savings

Cost Savings

Stop bad runs hours early. Save $$$ on GPU.

Deep Debugging

Search and explore individual generations.

group

Team Collaboration

Share projects with role-based access control.

dashboard

Rich UI

Dashboard, search, and checkpoint diff tools.

dataset

Built-in Datasets

Prompt packs for reasoning and detection.