📊 Quality and evolution
Measure errors, calibrate expectations, and plan specialization.
Track map
📊 Benchmarks, calibration, and evaluation
Compare what was actually measured.
📊 Specialization and final project
Define when to evolve and when to stop.
Detailed content
Benchmarks, calibration, and evaluation
Compare what was actually measured.
What it is
The sources combine results from different benchmarks, tasks, and checkpoints.
Why learn
Before repeating a percentage, note the model, review, dataset, number of classes, number of examples, and method.
Key concepts
conditions
used examples
measured objective
aligned conditions
What it is
The current report shows baseline checkpoints below the majority class baseline in typed-decisions, while the specialized version improves.
Why learn
In the lab, the small set of tickets is a functional smoke test.
Key concepts
task without fine-tuning
simple reference
fine-tuned model
new data
What it is
Accuracy is the fraction of correct predictions.
Why learn
In support, confusing sales with other may have a different impact than diverting a critical incident.
Key concepts
correct fraction
error between classes
unequal classes
dominant class
What it is
Multiclass Brier sums the squared error between probabilities and a one-hot label.
Why learn
State the formula: some reports divide Brier by the number of classes, while others sum it.
Key concepts
error of the distribution
difference by bins
single label
probability interval
What it is
Temperature scaling divides logits by a temperature before softmax.
Why learn
The multilingual checkpoint is described as not calibrated for the user’s domain.
Key concepts
logits scale
reserved data
accepted fraction
test misuse
What it is
The evaluate command reads JSONL, runs real weights, and writes results per example—accuracy, baseline, Brier, and ECE.
Why learn
Use the report as a starting point for your own set.
Key concepts
one case per line
results per case
a diagnostic opportunity
unseen data
Specialization and final project
Define when to evolve and when to stop.
What it is
Fine-tuning has a cost and only makes sense when the errors found justify changing the model.
Why learn
Classify errors before acting: human-label error, schema error, context error, language error, or capacity error.
Key concepts
likely cause
controlled change
needed learning
total effort
What it is
A domain dataset needs to represent language, class frequency, and the operation’s difficult cases.
Why learn
Record the rubric version and the labeling process.
Key concepts
partition
separation unit
label source
shared information
What it is
The repository includes a fine-tuning notebook for typed-decisions on two T4 GPUs.
Why learn
Read the cells, output checkpoints, and publishing destination before running.
Key concepts
cell-by-cell execution
data pass
new weights
separate stage
What it is
The work uses distribution-based rewards, including a logarithmic and spherical component, and an ordinal term for the score.
Why learn
A training objective with desirable properties does not guarantee calibration after finite training or domain shift.
Key concepts
probabilistic incentive
training signal
distance between levels
new data
What it is
A reasonable evolution starts in shadow mode: the model recommends, humans decide, and divergences are analyzed.
Why learn
Define metrics and stop criteria before the pilot.
Key concepts
no action
data drift
version rollback
set threshold
What it is
Deliver the project working, the contract of the four questions, a versioned dataset, the inference report, and limitations analysis.
Why learn
Compare three examples: one clear, one ambiguous, and one out of the domain.
Key concepts
repeat
find the source
known boundary
hypothesis