Fine Tuning Large Language Models - Interview Questions and Answers & Solved Quiz Questions

In this post, I explain Fine Tuning Large Language Models: Fine Tuning, Transfer Learning, Pretraining vs Fine-Tuning, Dataset Curation, Classification, Generation, Entity Matching, Sequence Instructioning), Annotation, Labeling Strategies & Synthetic Data for Domain Adaptation, Fine-Tuning Workflows, Parameter-Efficient Fine-Tuning, Instruction Tuning & Sequential Instruction Fine-Tuning, RLHF, Reward Modeling, and Safety Tuning, Fine-Tuning for Specialized Use Cases: Domain Adaptation & Entity Matching, Adaptive Machine Translation, Model Architectures & Scaling Considerations for Fine-Tuning, Hyperparameters, Optimizers & Practical Recipes (LR, Schedules, Batch Size), Mixed Precision, Memory Optimization, and Distributed Training. If you want my full Fine Tuning LLMs document also including the following topics, you can use the Contact Form (in the right pane) or message me in LinkedIn:
Tooling & Frameworks, Offline Metrics, Human Evaluation, and Task-Specific Benchmarks, Testing & QA for Fine-Tuned Models: Unit Tests, Regression Tests, and Red-Teaming, Reproducibility, Experiment Tracking & Versioning, Bias, Fairness & Ethics in Fine-Tuning, Safety, Guardrails & Safe Deployment Practices (Rate Limits, Filters, Monitors), Performance Monitoring & Production Observability for Fine-Tuned LLMs, Cost Estimation & Optimization, Parameter Distillation & Model Compression After Fine-Tuning, CI/CD & Automation for Fine-Tuning Pipelines, Security & Intellectual Property Considerations in Fine-Tuning, and Consolidated Questions and Answers on Fine-Tuning LLMs. Follow me in Kaggle.

Question: What does fine-tuning a large language model mean? How does it differ from prompting or retrieval-augmented generation?
Answer: Fine-tuning updates model parameters (all or a subset) on a curated dataset so the model internalizes new task- or domain-specific behavior; prompting crafts inputs at inference time without changing weights; RAG augments inference by retrieving external context but does not change model weights. Fine-tuning is chosen when persistent, repeatable behavior across many inputs is required, when latency of retrieval is unacceptable, or when regulatory/audit requirements demand the model hold specific knowledge.

Question: When should an AI engineer choose to fine-tune rather than rely on prompt engineering?
Answer: Prefer fine-tuning when you need: consistent style/terminology across outputs, better sample efficiency for repeated tasks, improved performance on distributional shifts that prompts can’t fix, or the ability to embed domain knowledge for offline auditing. If the requirement is ad-hoc or highly dynamic (rapidly changing facts), prefer RAG or prompt orchestration.

Question: What are the practical trade-offs of fine-tuning (cost, latency, maintainability)?
Answer: Fine-tuning increases model maintenance (retraining, versioning), storage (new checkpoints), and potentially inference cost if a larger model is required. It reduces runtime complexity (no external retrieval) and can improve latency. Operationally, it requires dataset governance, additional testing, and rollback procedures.

Question: What decision criteria should AI testers and AI engineers use to justify fine-tuning?
Answer: Use measurable criteria: target metric lift vs baseline prompting, reproducibility of desired behavior across holdouts, cost of errors in production, audit and compliance needs, and dataset readiness. If the validated lift per retraining cost is favorable and governance processes exist, proceed to fine-tune.

Follow Inder P Singh https://www.linkedin.com/in/inderpsingh

Quiz
1. Fine-tuning changes which of the following?
A. Model architecture only
B. Model weights (Correct)
C. Only the inference prompt
D. Tokenizer vocabulary only

2. The situation best favoring RAG over fine-tuning is:
A. Need for consistent terminology across outputs
B. Rapidly changing factual knowledge where freshness matters (Correct)
C. Requirement for offline audit of model decisions
D. Low-latency constraints at inference

3. Which is NOT a typical cost of fine-tuning?
A. Additional checkpoint storage
B. Need for dataset governance
C. Elimination of inference latency entirely (Correct)

Question: What is transfer learning in LLMs?
Answer: Transfer learning uses representations learned during pretraining on massive corpora; these representations capture syntax, semantics, and world knowledge that can be adapted to downstream tasks via fine-tuning, enabling sample-efficient learning compared to training from scratch.

Question: How does pretraining objective influence fine-tuning outcomes?
Answer: Pretraining objectives (causal language modeling, masked LM, next-sentence prediction) shape the inductive biases of the model. A model pretrained with an autoregressive objective adapts naturally to generation tasks, while masked LM pretraining may require architectural or head modifications for some generative tasks.

Question: Why does fine-tuning sometimes cause catastrophic forgetting? How can you mitigate it?
Answer: Catastrophic forgetting occurs when model weights move away from general representations toward task-specific patterns, degrading performance on previously learned capabilities. Mitigations include lower learning rates, regularization (e.g., L2), rehearsal with mixed-in pretraining data, and parameter-efficient adapters that preserve base weights.

Question: How can AI Testers validate that fine-tuning preserved general competence, while improving task-specific metrics?
Answer: By evaluating sanity checks on general capabilities (language fluency, toxicity, factuality benchmarks) and task-specific holdouts. Also, by using cross-task validation suites and monitor regressions in core abilities.

Follow Company: Software and Testing Training

Quiz
Transfer learning primarily provides:
A. Fresh training data
B. Reusable pretrained representations (Correct)
C. Instant model compression
D. A way to avoid dataset curation

Catastrophic forgetting is best mitigated by:
A. Using a very large batch size only
B. Regularization and mixed rehearsal data (Correct)
C. Removing the tokenizer
D. Increasing learning rate

A model pretrained with a causal LM objective is naturally suited for:
A. Image classification
B. Generative text tasks (Correct)
C. Mask prediction tasks only
D. Speech recognition only

Question: What are the core dataset quality checks done by AI Testers before fine-tuning?
Answer: Verify label correctness, removal of near-duplicates, confirm representative coverage of subpopulations, inspect distribution of lengths and tokens, and validate that no leakage of test/production secrets exists. Produce metadata for provenance and partitioning.

Question: How should data be chunked for long-document fine-tuning?
Answer: Segment into semantically coherent chunks (paragraphs, sections) with overlap (to preserve context at boundaries). Include chunk-level metadata (source id, position) so outputs can be traced back for audits.

Question: When is synthetic data helpful? What are the risks of synthetic data?
Answer: Synthetic Q&A pairs can augment scarce domain examples. Synthetic data helps bootstrap instruction behavior but risks introducing artifacts and biases; validate synthetic examples with human review and mix ratios that favor real data for final tuning.

Follow my Kaggle Profile: https://www.kaggle.com/inderpsingh

Question: What labeling strategies speed up dataset creation for large tasks?
Answer: Use active learning to prioritize uncertain examples, weak supervision to combine noisy heuristics, and annotation guidelines with seed examples to ensure consistency. Track inter-annotator agreement and iteratively refine label schemas.

Quiz
Chunking long documents with overlap helps prevent:
A. Tokenizer failures
B. Loss of context at chunk boundaries (Correct)
C. Faster tokenization
D. Increased vocabulary size

A core risk of synthetic data is:
A. Reduced storage needs
B. Introduction of non-natural artifacts or biases (Correct)
C. Immediate improvement in all metrics
D. Elimination of the need for human validation

Active learning primarily reduces:
A. Model size
B. Annotation cost by selecting informative examples (Correct)
C. The requirement for chunking
D. Need for caching

Question: How do you convert an entity matching problem into a fine-tuning format for an LLM?
Answer: Frame pairs (recordA, recordB) as input with a deterministic label (match / no-match) and include normalized fields. Optionally provide provenance and candidate attributes to help the model learn token-level alignment patterns; structure prompts to make the task explicit (e.g., “Do these entries refer to the same entity? Answer: Yes/No”).

Question: What is the difference between framing a generation task vs a classification task?
Answer: Generation requires sequence-to-sequence or causal fine-tuning with loss over token outputs; classification often uses a head over a pooled representation and optimizes cross-entropy over classes. Choose loss and sampling strategies accordingly.

Question: How are sequential instructions represented for fine-tuning instruction-following models?
Answer: Encode instruction chains as structured inputs (step1; step2; ...), include desired intermediate states, and possibly supervise intermediate outputs if you want the model to produce stepwise reasoning or multi-step plans.

Question: For entity matching, how should negative examples be sampled?
Answer: Use hard negatives (close but non-matching candidates) and stratified sampling to reflect real-world class imbalance; avoid trivial negatives that make the task too easy.

Quiz

Entity matching fine-tuning commonly uses inputs of:
A. Single records only
B. Pairs of records with a match/no-match label (Correct)
C. Only precomputed embeddings
D. Image pairs

Sequential instruction tuning benefits models that must:
A. Compress embeddings
B. Produce stepwise, multi-turn plans (Correct)
C. Only classify sentiment
D. Reduce vocabulary size

Hard negatives are useful because they:
A. Slow training significantly
B. Improve discrimination by providing challenging non-matches (Correct)
C. Increase model size
D. Reduce the need for validation

Question: What annotation workflows suit fine-tuning high-stakes domain models (e.g., legal, medical)?
Answer: Use multi-stage review: primary annotation, secondary expert adjudication, and a reconciliation step for disagreements. Maintain detailed guidelines, example anchors, and periodic calibration sessions to keep labelers consistent.

Question: When should weak supervision be used and how to combine sources?
Answer: Use weak supervision to scale labels via heuristics, rules, and model outputs. Combine sources with label-modeling (e.g., Snorkel-like approaches) to estimate per-example true labels and track source reliabilities.

Question: How do you validate synthetic labels or augmented examples?
Answer: Separate a validation set of human-labeled examples, sample synthetic examples for human spot-checking, and measure distributional divergence between synthetic and real examples to detect artifacts.

Question: What governance artifacts should be available along with annotated datasets?
Answer: You should keep annotation schemas, worker metadata, disagreement logs, versioned dataset snapshots, and provenance records so QA and auditors can trace why a particular training example influenced model behavior.

Quiz
A multi-stage annotation workflow typically includes:
A. Single annotator only
B. Primary annotation plus adjudication by experts (Correct)
C. Only synthetic labels
D. No validation

Weak supervision should be combined using:
A. Simple majority vote only
B. Label modeling to estimate true labels and source reliability (Correct)
C. Random sampling
D. Noisy channel model only

Spot-checking synthetic data ensures:
A. That synthetic data always outperforms real data
B. That synthetic artifacts are detected and corrected (Correct)
C. That no human labels are needed afterward
D. That the model is fully calibrated

Question: What is the difference between full-model fine-tuning and partial / layered training?
Answer: Full-model fine-tuning updates every parameter of the LLM on your task dataset; partial / layered training freezes most base weights and updates only a subset (such as last N layers, task heads, or adapter modules). Full fine-tuning gives maximum representational flexibility but requires more memory, computation, and produces larger checkpoints. Partial training reduces resource needs and risk of catastrophic forgetting by constraining parameter drift.

Question: What are the resource implications (GPU memory, checkpoint storage, training time) of each workflow?
Answer: Full fine-tuning demands GPUs with large memory budgets or heavy use of model parallelism / gradient checkpointing; checkpoint sizes equal the full model (GBs to TBs). Partial approaches reduce optimizer state and checkpoint sizes dramatically - often by orders of magnitude - because only a small parameter subset is stored/updated. Training time per step can be similar, but effective throughput and wall-clock time favor parameter-efficient methods on the same hardware.

Question: In LLMOps, how do you decide which workflow to use?
Answer: Base the decision on (1) magnitude of expected metric lift from full updates vs partial; (2) available infra and budget; (3) need to preserve base capabilities; (4) frequency of retraining. If rapid iterations, limited infra, or many variants are expected, prefer layered or adapter-based approaches. If you require maximal accuracy and have resources and governance to manage large checkpoints, choose full fine-tuning.

Question: What best practices reduce risk when performing full or partial fine-tuning?
Answer: Use low initial learning rates, gradient clipping, and mixed precision; keep a checkpointed baseline of the unmodified model; run holdout suites that test general language competence; limit catastrophic forgetting via rehearsal or regularization; and automate validation and rollback. For partial training, verify that updated layers actually affect task metrics by ablation runs and maintain a small validation set reflecting production distribution.

© Inder P Singh

Quiz
What is the main advantage of partial / layered training over full fine-tuning?
A. It always produces higher accuracy
B. Reduced memory and checkpoint storage (Correct)
C. Eliminates the need for validation
D. Requires no tuning of hyperparameters

Which practice helps prevent catastrophic forgetting during full-model fine-tuning?
A. Using very large learning rates
B. Rehearsal with mixed pretraining examples (Correct)
C. Removing validation sets
D. Freezing the entire model

When would you prefer full-model fine-tuning?
A. When you have severe infra constraints
B. When maximum task performance outweighs cost and you can manage larger checkpoints (Correct)
C. When you need many rapid, low-cost experiments
D. When you must preserve the base model’s exact behavior

Question: What are LoRA, adapters, prompt-tuning, and PEFT and why are they used?
Answer: These are parameter-efficient fine-tuning techniques. LoRA (Low-Rank Adaptation) injects low-rank update matrices into weight updates, letting you learn small additional matrices instead of full weights. Adapters insert small bottleneck modules between layers and update just those. Prompt-tuning optimizes a small continuous prompt vector while keeping the base model frozen. PEFT is an umbrella term (Parameter-Efficient Fine-Tuning) encompassing these methods. They reduce storage, enable many task variants, and simplify deployment.

Question: How do the trade-offs between LoRA, adapters, and prompt-tuning work out in practice?
Answer: LoRA often yields near full-fine-tune performance with small parameter budgets and is straightforward to apply to linear projection layers. Adapters provide modularity and can be stacked per domain; they are robust and interpretable but sometimes slightly less performant than LoRA. Prompt-tuning is lowest cost but can underperform on complex structured outputs and typically requires long tuning runs. Choice depends on task complexity, allowed parameter budget, and how many task variants you intend to maintain.

Question: How does one manage many domain adapters or LoRA modules in production?
Answer: Version adapters and LoRA checkpoints independently, load the appropriate module dynamically at inference time, and keep a compatibility matrix linking base model versions to adapter versions. Maintain a registry mapping domain names to adapter checkpoints and use lazy loading to minimize memory footprint in multi-tenant services.

Question: For AI Testers, what are quick sanity checks when using PEFT (Parameter-Efficient Fine-Tuning) methods?
Answer: Run small ablation tests: compare a frozen baseline, the PEFT module, and a tiny full-fine-tune on a validation slice; measure latency and memory; assert that the module yields non-trivial metric lift; and verify no gross regressions on general capability tests.

Quiz
Which method inserts small bottleneck modules between layers for task adaptation?
A. LoRA
B. Adapter tuning (Correct)
C. Full fine-tuning
D. Token pruning

What is a typical benefit of LoRA?
A. Eliminates the need for a base model
B. Near full-fine-tune performance with far fewer trained parameters (Correct)
C. Always faster inference
D. No hyperparameters to tune
Prompt-tuning is best suited when:

A. You need high performance on complex structured outputs
B. You want the smallest possible trained parameter footprint and can accept possible performance limits (Correct)
C. You must change tokenizer vocabulary
D. You require full checkpoint snapshots only

Question: What is instruction tuning? Why is it important for instruction-following behavior?
Answer: Instruction tuning fine-tunes an LLM on a corpus of (instruction → response) pairs so the model learns to map natural language directives into desired outputs. It shapes response style, adherence to constraints, and the model’s ability to follow diverse prompts, improving usability for LLM’s client applications.

Question: How do you represent sequential instructions during fine-tuning to teach multi-step behavior?
Answer: Encode multi-step workflows as structured sequences: include the initial instruction, desired intermediate outputs, and explicit step delimiters. You can supervise intermediate steps (teacher forcing) so the model learns intermediate states, or fine-tune on end-to-end examples with chain-of-thought style annotations if you want the model to generate reasoning traces.

Question: What datasets and augmentation strategies work well for instruction tuning?
Answer: Use diverse instruction corpora (few-shot style prompts, task templates, human-generated examples), synthetic instructions generated by LLMs and reviewed by humans, and negative or adversarial instructions (to improve robustness). Balance examples by instruction complexity and domain breadth to avoid overfitting style.

Question: How do you evaluate instruction-tuned models for sequential tasks?
Answer: Evaluate at multiple levels: robustness to instruction paraphrases, fidelity of intermediate steps (if applicable), and correctness of final output. Use human ratings for subjective aspects and automated checks for stepwise constraints (e.g., does step 2 follow from step 1).

Follow on Kaggle at https://www.kaggle.com/inderpsingh

Quiz
Instruction tuning primarily optimizes the model to:
A. Reduce model size
B. Better map natural language instructions to desired outputs (Correct)
C. Change tokenizer embeddings only
D. Increase inference latency

When teaching multi-step workflows, which approach helps the model learn intermediate reasoning?
A. Ignoring intermediate states
B. Supervising intermediate outputs and using step delimiters (Correct)
C. Only using single-turn examples
D. Removing instructions

A good QA evaluation for sequential instruction models should include:
A. Only final output correctness
B. Robustness to instruction paraphrases, fidelity of intermediate steps, and correctness of final output (Correct)
C. No human evaluation
D. Only perplexity on training data

Question: What is the role of RLHF (Reinforcement Learning from Human Feedback) in fine-tuning?
Answer: RLHF refines model behavior by training a reward model on human preference data and then optimizing the policy (the LLM) to maximize that reward, typically via PPO (Proximal Policy Optimization) or other policy-optimization algorithms. It aligns outputs with human desirability signals (style, helpfulness) that are hard to encode as supervised labels.

Question: What are common risks and safety considerations when applying RLHF?
Answer: RLHF can overfit to annotator biases, amplify undesirable behaviors if reward data is narrow, and create reward hacking where the model finds loopholes to score highly on proxy metrics without genuine improvement. Safety requires diverse annotator pools, reward regularization, constraint modeling, and robust evaluation on adversarial cases.

Question: How does reward modeling scale to complex tasks?
Answer: For complex tasks you may need hierarchical reward models or multi-objective rewards (factuality, helpfulness, safety). Collect richer human preference data with scenario-based annotations and calibrate reward weights carefully. Use offline evaluation to detect reward-gaming behaviors before deploying online.

Question: In Ops, what operational controls should be used for alignment tuning?
Answer: Maintain human-in-the-loop review for high-risk outputs, implement rollback and canary deployments, log model decisions and reward signals, and run red-teaming to surface adversarial exploits. Monitor for distributional drift in reward effectiveness and retrain the reward model periodically.

Quiz
RLHF primarily optimizes a model to:
A. Minimize parameter count
B. Maximize a human-trained reward signal to align behavior (Correct)
C. Reduce dataset size
D. Increase tokenization speed

A major risk of RLHF is:
A. It always reduces performance
B. Reward hacking and amplification of annotator bias (Correct)
C. It eliminates the need for evaluation
D. It never requires human reviewers

Which mitigation helps prevent reward hacking?
A. Narrow the annotator pool to a single expert
B. Use diverse annotator data, multi-objective rewards, and red-teaming security process (Correct)
C. Remove safety checks
D. Train longer without human feedback

Question: How do you approach fine-tuning for domain adaptation (for domains such as legal, medical or finance) differently from general tasks?
Answer: Domain adaptation requires curated, high-quality domain data, strict privacy controls, domain expert annotations, and often higher penalties for errors. Use domain ontologies, terminology normalization, and incorporate external domain knowledge (tables, taxonomies) in prompts. Fine-tuning should prioritize precision and conservative outputs where accuracy is important.

Question: How is entity matching implemented as a fine-tuning pattern for LLMs?
Answer: Transform entity matching into pairwise classification or as a generative decision problem: present normalized fields and ask the model to output a canonical match id or “no-match.” Use hard negatives, field-alignment augmentation, and augment training with synthetic variants. Evaluate with precision@k and pairwise F1.

Question: What are testing and validation best practices for specialized domains?
Answer: Build domain-specific validation suites that include edge cases, rare classes, and adversarial examples. Involve domain experts in labeling and acceptability thresholds. Run a post-deployment feedback process to collect label corrections and retrain iteratively.

Question: How do governance and audit differ when fine-tuning domain models?
Answer: Require stricter provenance records, legal review of training data, access controls on checkpoints, and mandatory human-review paths for high-risk decisions. Document rationales for threshold choices and ensure appeal workflows exist for affected users.

View videos in our YouTube channel, Software and Testing Training

Quiz
For entity matching fine-tuning, which strategy improves discrimination on borderline pairs?
A. Using only trivial negatives
B. Using hard negatives and field normalization (Correct)
C. Ignoring field values
D. Removing validation

When adapting to a medical domain you should:
A. Use random web data only
B. Curate expert-labeled data, enforce privacy, and include domain ontologies (Correct)
C. Skip validation entirely
D. Always prefer prompt engineering over fine-tuning

Which governance artifact is crucial for high-risk domain models?
A. A list of all random seeds used only
B. Provenance records, access controls, and documented thresholds (Correct)

Question: How do you approach adaptive machine translation when fine-tuning LLMs for MT (Machine Translation) and multilingual tasks?
Answer: Adaptive MT requires framing fine-tuning as a conditional generation problem where the model learns to map source text in language A to target text in language B, while preserving domain style. Use parallel corpora where available and back-translation to augment scarce bilingual data. For domain adaptation, include in-domain monolingual corpora and apply continued pretraining on source/target-side monolingual data before supervised fine-tuning on parallel pairs. Carefully control tokenization and vocabulary handling for multilingual inputs to avoid sub-word fragmentation.

Question: What strategies help low-resource transfer for a target language?
Answer: Use multilingual pretraining transfer: fine-tune a strongly multilingual base on high-resource language pairs and then adapt on small in-language examples. Apply transfer learning via multilingual adapters or shared encoder representations, and use back-translation to synthesize parallel examples. Curriculum fine-tuning - start with related high-resource language data then gradually introduce low-resource examples - to stabilize training.

Question: How do you measure quality for adaptive MT beyond BLEU (Bilingual Evaluation Understudy)?
Answer: Complement BLEU metric with adequacy and fluency metrics: use chrF for morphologically rich languages, COMET framework or BLEURT metric for learned semantic evaluation, and human post-editing cost to capture practical workload. Evaluate domain-specific terminology preservation and perform targeted tests on named entities and numerical fidelity.

You are welcome to connect with Inder P Singh in LinkedIn at https://www.linkedin.com/in/inderpsingh

Question: Operationally, how do you handle domain-specific terminology and glossary constraints during inference?
Answer: Inject constraint-aware decoding: use constrained decoding or dictionary biasing to prefer glossary terms, and fine-tune with augmented examples that demonstrate desired terminology mapping. For hostile inflection cases, include paraphrase and morphological variants in training so the model learns robust term realization.

Quiz
Which technique is especially useful for low-resource language adaptation?
A. Training from scratch on only the tiny low-resource corpus
B. Back-translation and curriculum fine-tuning starting from related high-resource data (Correct)
C. Removing sub-word tokenization entirely
D. Using BLEU only for evaluation

How does continued pretraining help adaptive MT?
A. It reduces vocabulary size permanently
B. It adapts model priors to domain language distributions before supervised fine-tuning (Correct)
C. It eliminates need for parallel data
D. It always improves BLEU by a fixed amount

What evaluation metric is recommended for semantic adequacy in MT?
A. Accuracy
B. COMET or BLEURT (Correct)
C. Perplexity only
D. Token count

When ensuring glossary term fidelity, which approach is effective?
A. Ignoring glossary and hoping the model learns it
B. Constrained decoding and fine-tuning with glossary-augmented examples (Correct)

Question: What does model scaling imply for fine-tuning cost and expected gains?
Answer: Larger models tend to require more compute and memory but often yield higher few-shot and fine-tuned performance, subject to diminishing returns and cost constraints.

Question: How do tokenizer choices affect fine-tuning, especially for multilingual settings?
Answer: Suboptimal tokenization can fragment frequent domain tokens into many subwords, harming both quality and inference latency; prefer tokenizers trained or adapted to target languages and domains.

Question: What architecture variant considerations help when fine-tuning for generation vs classification?
Answer: For generation prefer encoder-decoder or causal-decoder architectures; for classification prefer a pooled representation with a task head.

Question: What is a scalability trade-off to consider when selecting model size for fine-tuning?
Answer: Larger models may improve accuracy but increase serving cost, latency, and complexity of distributed training.

Question: What hyperparameter is most critical to start tuning first for stable fine-tuning?
Answer: Learning rate schedule and initial learning rate.

Question: Why are learning rate schedules important in fine-tuning?
Answer: Proper schedules (warmup, decay) prevent instability and reduce catastrophic forgetting; learning rate determines step size and must be tuned relative to batch size and model size.

Question: What is a practical batch size strategy if GPU memory is limited?
Answer: Use gradient accumulation to simulate larger batch sizes while keeping per-step memory small.

Quiz
Which optimizer is commonly effective for LLM fine-tuning?
A. SGD with momentum only
B. AdamW family (with decoupled weight decay) and variants like AdamW with correct bias correction (Correct)
C. RMSprop only
D. Adagrad only

What is a sensible initial recipe for hyperparameters on a medium-sized model?
A. LR = 1, batch size = 1024
B. LR = 1e-5 to 3e-5 with warmup and decay, gradient accumulation to achieve effective batch, AdamW, and weight decay tuned modestly (Correct)
C. No warmup and extremely high LR
D. Only change optimizer, keep other defaults

Question: How does mixed precision (FP16/BF16) speed up fine-tuning? What must you watch for?
Answer: Mixed precision reduces memory usage and increases throughput by storing activations and weights in lower precision while maintaining master FP32 weights for updates. Watch for numerical underflow/overflow and ensure loss scaling is used to maintain stability. Use BF16 where supported for simpler stability; FP16 often requires dynamic loss scaling.

Question: What memory optimizations help fit large models on limited GPUs?
Answer: Use gradient checkpointing to trade compute for memory, activation offloading to CPU where feasible, ZeRO optimizations to shard optimizer states, and parameter-efficient methods (LoRA/adapters) to reduce trainable state. Also use model parallelism (tensor/model parallel) frameworks, if multiple GPUs are available.

Question: What does DeepSpeed ZeRO enable for large-scale fine-tuning?
Answer: ZeRO shards optimizer states, gradients, and parameters across devices, reducing per-GPU memory footprint and enabling training of larger models on the same hardware. It requires orchestration but might provide significant scale-up benefits.

Question: What operational strategy reduces OOM and maximizes throughput when using FSDP or DeepSpeed?
Answer: Start with small scale tests, enable mixed precision, use appropriate shard strategies (stage 1/2/3), monitor communication overhead, and tune micro-batch sizes and accumulation steps; ensure deterministic behavior by controlling seeds and enabling consistent checkpointing.

Quiz
Which technique reduces peak memory by recomputing activations during backward pass?
A. Model pruning
B. Gradient checkpointing (Correct)
C. Token pruning
D. Increasing batch size

What is a major benefit of ZeRO optimizations?
A. Eliminates need for GPUs
B. Shards optimizer state and parameters to reduce per-device memory (Correct)
C. Always reduces training time by 10x without trade-offs
D. Removes need for validation

Which precision option often avoids the need for dynamic loss scaling on modern GPUs?
A. FP8
B. BF16 (Correct)
C. INT4
D. FP32 only

Send me a message using the Contact Us (left pane) or message Inder P Singh (6 years' experience in AI and ML) in LinkedIn at https://www.linkedin.com/in/inderpsingh/ if you want deep-dive Artificial Intelligence and Machine Learning projects-based Training.