"Vibe coding" happens when a developers is conducting not just pair programming but deep collaboration with AI assistants to produce functional code. Instead of manually writing and debugging each line, developers increasingly rely on AI models to output, refactor, debug and iterate on the code.
"I 'Accept All' always—I don't read the diffs anymore." - Andrej Karpathy
On the flip-side an overreliance on AI-generated code might lead to a lack of understanding of underlying system architectures, potential security vulnerabilities, and the accumulation of technical debt.
What is the difference between distillation and quantization of a LLM model?
DeepSeek's rapid progress likely comes from efficient knowledge distillation combined with strong pretraining techniques. So what is distillation....
- Distillation: In knowledge distillation, a smaller (student) model learns to replicate the behavior of a larger (teacher) model. This is typically a supervised approach where the teacher’s outputs serve as “labels” for the student. The student is trained to match these outputs without directly using reinforcement learning. The main goal is to preserve performance while reducing model size and computational complexity. Most modern deep learning models, especially large language models (LLMs), provide token probability distributions rather than just returning the top token. So a base model (e.g. ChatGPT) is queried with various inputs, producing soft labels or probability distributions over possible outputs rather than just hard labels. The dataset for the student model consists of pairs of (input, teacher-generated output).Instead of just the final correct answer, the student learns from the full distribution over possible answers.
- Quantization: Quantization changes the numerical precision of model parameters (for example, from 32-bit floating-point to 8-bit integer). This reduces the memory footprint and can speed up inference on specialized hardware. Unlike distillation, quantization does not rely on a teacher–student paradigm; it directly alters how the model weights and activations are stored and computed.
Distillation and quantization are both model optimization techniques used to make deep learning models more efficient for deployment, especially on edge devices and resource-constrained environments.
What is the difference between open weights and an open model? Why does it matter?
- Open Weights: Only the trained parameters (numerical values) of the model are accessible. The underlying architecture, training scripts, or inference code might remain proprietary or unavailable. Users can sometimes use these weights in a compatible architecture but lack full insight or control if the model’s internals aren’t open.
- Open Model: Both the architecture (layers, connections, hyperparameters) and the trained weights are fully published. Allows for deeper understanding, customization, or retraining of the model.
- Why It Matters: If only weights are open, one might be restricted in modifying or extending the model. An open model ensures full transparency and flexibility for research, commercial deployment, or further development.
What is the difference between an open model and a closed model?
- Open Model: Architecture and weights are available, making it straightforward to reproduce, modify, or improve the model. Often fosters innovation in the community (e.g., open-source NLP or vision models).
- Closed Model: The architecture, weights, or both remain proprietary. Users can typically only access it via limited APIs or services, with minimal insight into how the model functions internally.
What are open-source models, and how do they vary in terms of architecture, weights, and licensing?
Open-source models are machine learning models whose architecture and/or weights are made publicly available, often under an open or permissive license. However, they vary in three key aspects:
- Architecture – Fully open-source models (e.g., Mistral, Falcon, GPT-J) share both the model structure and implementation, allowing modifications. Some models, like LLaMA, share their architecture but with restrictions.
- Weights – Some models provide pretrained weights for fine-tuning (Mistral, Falcon, LLaMA), while others (e.g., GPT-4, Gemini) only offer API access without releasing weights.
- Licensing – Truly open-source models use permissive licenses (e.g., Apache 2.0) that allow unrestricted commercial use. Others, like LLaMA, release weights but under restrictive terms that limit commercial applications.
Why does it Matter?
- Fully open-source: Architecture + weights + permissive license (e.g., Mistral, Falcon).
- Partially open: Architecture + weights but with restrictions (e.g., LLaMA).
- Closed-source: No architecture or weights, only API access (e.g., GPT-4, Gemini).
What is a frontier model? What are the different classifications?
- Frontier Model: A highly advanced machine learning model that pushes state-of-the-art performance. These models often require massive computational resources and large datasets to train.
- Research Frontier: Cutting-edge experimental models primarily explored in academic or industrial research labs.
- Industry Frontier: Models optimized for production use, often with considerations for reliability, scalability, and commercialization.
Frontier models—those at the cutting edge of research or industry—can be open, closed, or something in between. In practice:
- Closed Frontier Models: Many state-of-the-art commercial models (e.g., GPT-4) keep both their architecture and weights proprietary.
- Partially Open Frontier Models: Some models release portions of their code or weights with usage restrictions (e.g., open weights but closed architecture, or vice versa).
- Fully Open (Open-Source) Frontier Models: A few frontier models make both their weights and code available under a license that permits free use, modification, and distribution (e.g., Falcon, Llama 2 under certain conditions).
What is fine-tuning, parameter fine-tuning, and other fine-tuning options?
- Fine-tuning: Adapting a pre-trained model to a specific task by continuing training on a task-relevant dataset. The goal is to leverage the model’s general learned features and specialize them for the new task.
- Parameter Fine-tuning: Directly updating the weights (parameters) of the model during fine-tuning. This often requires substantial computing resources and may risk overfitting if the dataset is small.
Other Fine-tuning Techniques:
?Retrieval-Augmented Fine-tuning: Incorporating external data sources or knowledge bases during inference or training so the model can “look up” information rather than memorizing it.
?Reinforcement Learning (e.g., RLHF): Fine-tuning a model based on a reward signal (often from human feedback) to optimize specific behaviors (e.g., more factual answers, safer outputs).
?Parameter-Efficient Fine-tuning (e.g., LoRA, Rank-based Methods): Adjusting a small subset of parameters (or adding adapter layers) to reduce memory usage and training overhead.
?Freezing Layers: Keeping certain layers static while only training select layers or modules.
?Hyperparameter Tuning: Changing settings like learning rates, batch sizes, etc., without altering the fundamental architecture or large parts of the model.
What are examples of the different types of LLM's and their training objectives?
There are different reasons to choose different LLM's and it depends on how they were trained. In the news we heard about InstructGPT and Reinforcement Learning from Human Feedback (RLHF) trained models because they are fine-tuned in specific ways.
For example if yo need need precise, instruction-following AI, InstructGPT, Claude, or Falcon Instruct are the best choices. If reasoning or retrieval is more important, alternatives like GPT-4, Gemini, or ChatGLM may be better suited.
- InstructGPT by OpenAI was one of the first major RLHF-trained instruction-following models, setting the standard for alignment techniques.
- Other models, such as Claude and LLaMA-2-Chat, adopt similar instruction-following strategies but may use different alignment techniques.
- Reasoning, Retrieval-Augmented, and RLAIF models focus on specific non-instructional optimizations, such as improving accuracy, logical consistency, or reducing reliance on human feedback.
- PEFT-based GPTs offer fine-tuning efficiency, allowing specialized model adaptation without full retraining.
What is OpenAI's InstructGPT ?
InstructGPT is a fine-tuned version of GPT-3 that was optimized specifically for following user instructions. InstructGPT stands out because it is fine-tuned using Reinforcement Learning from Human Feedback (RLHF) to optimize instruction-following behavior. Other GPTs may prioritize different capabilities, such as general reasoning, creative generation, or retrieval-augmented knowledge. InstructGPT is unique because it was one of the first major RLHF-trained instruction-following models, setting the standard for alignment techniques.
The training process involved multiple steps:
Pretraining (Base Model - GPT-3)
- Trained on a diverse dataset from books, articles, and websites.
- Learned general language understanding and generation skills.
Supervised Fine-Tuning (SFT)
- A smaller dataset of human-written instruction-response pairs was used.
- This helped the model learn direct responses to user instructions.
Reinforcement Learning from Human Feedback (RLHF)
- Humans ranked multiple model-generated responses.
- The model was fine-tuned to prefer responses that aligned with human preferences (e.g., correctness, helpfulness, and safety).
- This RLHF process helped improve alignment while reducing harmful or irrelevant outputs.
What are the alternatives to OpenAI's InstructGPT?
What happens in the MLOps stage?
- MLOps (Machine Learning Operations) involves: Model Versioning: Managing different iterations and checkpoints. Deployment: Moving models into production (e.g., via containers, serverless functions). Monitoring: Tracking performance metrics, data drift, and errors in real time. Governance & Compliance: Ensuring models meet regulatory and ethical standards (e.g., bias detection). Tooling: Platforms like Azure ML, Amazon Bedrock, etc., for end-to-end model lifecycle management.
What happens in the data engineering stage?
- Ingestion: Gathering raw data from varied sources (databases, logs, APIs).
- Storage: Organizing data in warehouses, lakes, or databases optimized for big data.
- Transformation & Preparation: Cleaning, normalizing, and structuring data to be model-ready (e.g., feature engineering, handling missing values).
What are agentic systems?
- Systems capable of autonomous decision-making and action in pursuit of specified objectives.
- Often powered by AI or ML models that can plan, reason, and adapt to changes in their environment.
- A framework for building “chained” applications around language models (LLMs).
- It orchestrates interactions between LLMs and external tools (e.g., APIs, databases) for tasks like retrieval augmentation or multi-step reasoning.
- Example usage includes hooking a GPT-based model to a database for context retrieval before generating an answer.
- A popular platform and Python library offering: Pre-trained Models: Transformers for NLP, vision, audio, etc. Model Hub: A repository for sharing and downloading community-developed models. Tools & APIs: Pipelines, tokenizers, and utilities for training/inference.
What is a Transformer model?
- A deep learning architecture characterized by attention mechanisms, enabling: Parallel processing of sequences. Handling long-range dependencies more effectively than RNNs or LSTMs.
- Commonly used in NLP but also adapted for vision, speech, and more.
- GPT (Generative Pre-trained Transformer): A family of large language models (e.g., GPT-3, GPT-4) developed by OpenAI. They generate human-like text and can be adapted to tasks like question answering, summarization, coding assistance, etc.
What part of the “code” is missing when you have only the weights?
- Architecture Definition: How layers and operations are structured.
- Preprocessing/Postprocessing Steps: Tokenization, normalization, or output formatting.
- Training Hyperparameters: Learning rates, batch sizes, or code for custom layers.
Some file formats (e.g., ONNX, GGUF, Ollama) may include partial or complete architecture details, but many released weights come without the full environment setup needed to reproduce training or run inference seamlessly.
What is the run-time environment for different model file formats?
- ONNX (Open Neural Network Exchange): Designed for interoperability across frameworks like PyTorch or TensorFlow. Can be run via ONNX Runtime on CPUs, GPUs, or specialized hardware.
- GGUF / GUFF (depending on naming conventions): Typically platform-specific optimizations for GPU or specialized inferencing hardware. Requires a compatible runtime that supports the specific model format.
- Ollama: Usually refers to a particular deployment or packaging format optimized for certain LLM-serving environments. Might require custom or proprietary runtime tooling for best performance.
