AI and LLM Development

Perform vector and matrix operations while tracking tensor shapes.

Interpret matrix multiplication as a composition of linear transformations.

Calculate vector norms, dot products, and cosine similarity.

Compute derivatives of common scalar functions used in machine learning.

Apply the chain rule to a small computational graph.

Calculate gradients of a multivariable function with respect to its parameters.

Compute expectation, variance, and conditional probability for simple distributions.

Compute softmax cross-entropy using numerically stable operations and distinguish cross-entropy from KL divergence.

Frame a software problem as a supervised, unsupervised, or generative learning task.

Split a dataset into training, validation, and test sets without introducing data leakage.

Create a reproducible baseline experiment by controlling random seeds and recording configurations and metrics.

Fit and interpret a linear regression model using gradient descent.

Fit and interpret a logistic classifier using cross-entropy loss.

Diagnose underfitting and overfitting from training and validation curves.

Apply L1, L2, and early-stopping regularization to a model.

Select evaluation metrics that match a model's task and error costs.

Create PyTorch tensors with correct shapes, dtypes, and device placement.

Use automatic differentiation to calculate and inspect parameter gradients.

Implement a multilayer neural network as a custom PyTorch module.

Build dataset and data-loader objects for mini-batch training.

Write a complete PyTorch training and validation loop.

Configure an optimizer and learning-rate scheduler for a neural network.

Save and restore model, optimizer, and scheduler state from a checkpoint.

Use mixed-precision training and gradient accumulation within a fixed VRAM budget.

Distinguish word-level, character-level, and subword tokenization trade-offs.

Train a byte-pair encoding tokenizer on a small text corpus.

Measure tokenizer fertility, vocabulary coverage, and unknown-token behavior.

Convert variable-length text examples into padded or packed token batches.

Implement token embeddings and verify their input and output shapes.

Construct shifted next-token inputs and labels for causal language modeling.

Construct padding-aware loss masks for causal language-model batches.

Apply language-model weight initialization and tie compatible embedding weights.

Distinguish encoder-only, decoder-only, and encoder-decoder transformer architectures and identify when each is appropriate.

Construct query, key, and value projections from an input sequence.

Calculate scaled dot-product attention for a small sequence by hand and in code.

Apply causal and padding masks to attention scores correctly.

Reshape query, key, and value tensors into multiple heads and verify the combined output shape.

Compare LayerNorm and RMSNorm and explain how residual pre-normalization affects gradient flow.

Implement a position-wise feed-forward network using GELU or SwiGLU activation.

Apply rotary positional embeddings to query and key tensors.

Implement and unit-test causal multi-head self-attention with rotary positional embeddings using tensor operations rather than a high-level transformer class.

Assemble attention, normalization, and feed-forward layers into a transformer block.

Construct a decoder-only language model that produces next-token logits.

Train the decoder-only model on a small corpus and interpret its learning curves.

Implement temperature, top-k, and nucleus sampling for text generation.

Implement and validate KV-cached autoregressive generation.

Diagnose tensor-shape errors, exploding gradients, and stalled learning in a training run.

Compare the small model's architecture with a modern Llama-style model configuration.

Assess a candidate pretraining dataset for licensing, provenance, quality, and privacy risks.

Implement rule-based text normalization and heuristic quality filtering for a text corpus.

Apply MinHash and Locality-Sensitive Hashing to identify approximate document duplicates.

Chunk and pack tokenized documents while preventing unintended cross-document attention.

Configure AdamW, warmup, and learning-rate decay for language-model pretraining.

Resume an interrupted training run reproducibly by restoring model state, optimizer state, random-number state, and data position.

Use scaling-law estimates to allocate a fixed compute budget between model size and training tokens.

Compare data, tensor, pipeline, and fully sharded parallelism by their communication and memory trade-offs.

Load and inspect model configurations, tokenizers, and weights with Transformers Auto classes.

Process a public text dataset with the Datasets library using batched and streaming operations.

Configure TrainingArguments to train and evaluate a causal language model using the Hugging Face Trainer API.

Launch a hardware-aware training job using Accelerate.

Save and load model weights safely using the safetensors format.

Create and manage versioned model and dataset repositories on the Hugging Face Hub.

Write a model card that documents intended use, training data, evaluation, limitations, and license.

Publish a reproducible model checkpoint, tokenizer, configuration, and evaluation results to the Hub.

Select an open base model that fits the task, license constraints, and 16 GB VRAM budget.

Format instruction and conversational examples with the model's required chat template.

Tokenize supervised fine-tuning examples and mask padding or prompt tokens that should not contribute to the loss.

Configure LoRA adapters by selecting target modules, rank, scaling, and dropout.

Fine-tune a quantized model using QLoRA within the available GPU memory.

Construct valid chosen-and-rejected response pairs for preference optimization.

Calculate the DPO objective for a response pair and explain the roles of the reference model and beta parameter.

Configure a small Direct Preference Optimization run and interpret its training metrics.

Calculate token-level loss and perplexity on a held-out corpus.

Compare base and adapted checkpoints on a task benchmark using reproducible prompts, decoding settings, and metrics.

Create a rubric and perform structured error analysis on generated responses.

Check an evaluation set for likely overlap or contamination with training data.

Probe a model for unsafe behavior, bias, and memorization using documented test cases.

Measure generation latency, throughput, and peak memory under controlled settings.

Compare multi-head, multi-query, and grouped-query attention using KV-cache memory requirements.

Quantize a model and measure the resulting quality, memory, and speed trade-offs.

Use repository contribution guidelines to select a well-scoped Hugging Face issue.

Create an editable development environment and run the repository's targeted test suite.

Trace a model feature from configuration through implementation, tests, and documentation.

Reproduce a reported defect or missing behavior with a minimal failing example.

Implement a targeted code or documentation fix in a Hugging Face repository.

Add unit tests that demonstrate the improvement and prevent regression.

Run formatting, linting, and relevant tests before submitting a change.

Prepare a pull request with a clear rationale, evidence, documentation, and responses to review feedback.