Fine-Tuning: LoRA, QLoRA & Distillation
Interactive technical curriculum synthesized from FAANG and Tier-1 AI startup interview rounds.
Fine-Tuning: LoRA, QLoRA, Quantization & Tokenomics
Comprehensive coverage of QA-24, QA-28, QA-29, QA-34, and QA-42. Master low-rank adaptation matrix decomposition, NormalFloat4 double quantization, and decision trees for formatting regressions.
Interactive Decision Tree: Prompting vs Fine-Tuning vs RAG
Select your production symptom to determine whether you need prompt engineering, constrained decoding, RAG, or LoRA fine-tuning:
3% invalid braces or keys
Model outputs wrong prices/specs
System prompt is 4,000 tokens long
BPE token splits multi-digits
Scenario: Malformed JSON / Schema Drops
Recommendation: DO NOT FINE-TUNE! Use Constrained Decoding (Grammar-guided sampling with Outlines / vLLM guided_json).
Why: Masking invalid tokens from the logit vocabulary at runtime mathematically guarantees 100% schema validity at zero training cost.
# Constrained Decoding in vLLM
sampling_params = SamplingParams(
guided_json=MyPydanticSchema.model_json_schema()
)
outputs = llm.generate(prompt, sampling_params)Base frozen weights W_0 ∈ R^{d × k} are preserved. The update ΔW is factorized into B ∈ R^{d × r} and A ∈ R^{r × k} where rank r ≪ min(d, k):
h = W_0 * x + (alpha / r) * (B * A) * x- Matrix A: Initialized with Gaussian random values ∼ N(0, σ²).
- Matrix B: Initialized to zero, ensuring ΔW = 0 at training onset.
- QLoRA Innovation: Base model is quantized to 4-bit NormalFloat4 (NF4) with Double Quantization, enabling a 70B parameter model to be fine-tuned on a single 48GB GPU!