Source progress
12-week average12%
Read-only; derived from the canonical vault.
UNSLOTH STUDIO · LORA · QLORA
Learn fine-tuning.
Separate the evidence.
A sanitized, bilingual 12-week path distilled from 50 source files. Simulations, observations, and verified outcomes are never presented as the same thing.
50/50source coverage
12week path
20mixed questions
2full languages
My progress
Completed records0%
No account. Stored on this device.
LEARNING PATH
Concept map
Each concept lays the groundwork for the next. Click to open that lesson.
ASERDARGUN.COM
Next in the learning system
USL provides the foundations of model adaptation. These are learning links; they do not transfer models, data or progress.
AIA
AI map
Choose foundations and a learning path.
↗ADPAdaptation lab
Compare Full FT, LoRA and QLoRA through synthetic experiments.
↗LLMModel runtime
Explore token generation and inference behavior.
↗EVLEvaluation
Explore independent measurement, quality and acceptance decisions.
↗LCLLocal deployment
Explore local model runtime options and hardware constraints.
↗CURRENT FOCUS
Setup and Verification
02
Verify a reproducible Studio environment on CachyOS that genuinely uses the GPU.
- NVIDIA/CUDA environment table
- 4-bit path
- GPU inference evidence
- Turkish token measurement
Source state45%
CORE LESSONS
From concept to decision rule
01Verified
Base, Instruct, and Reasoning
Choose a model by its starting behavior and the capability gap—not by a fixed row count.
↗02VerifiedTokens, Context, and Attention
Unify token budgets, fixed weights, and temporary attention state in one mental model.
↗03VerifiedLoRA and QLoRA
Adapt a frozen base with a low-rank update; measure memory fit on the target hardware.
↗04VerifiedRank, Alpha, and Target Modules
Separate capacity, scale, and intervention location; change one variable at a time.
↗05VerifiedEpochs, Steps, and Gradient Accumulation
Separate micro-steps from real weight updates and calculate the correct OOM response.
↗06VerifiedLoss, Overfitting, and Forgetting
Do not confuse loss with business quality; combine domain, format, safety, and retention metrics.
↗07VerifiedChat Template Mismatch
The same semantic dataset does not mean forcing the same rendered token sequence on every model.
↗08PlannedDataset and Evaluation Discipline
Do not claim quality without an independent test set, stable schema, and safety examples.
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