Reasoning models are language models that think before they answer. Instead of predicting the next token immediately from the input, they generate an extended internal monologue — working through a problem step by step, checking their own logic, backtracking when something does not hold, and only then producing a final response. This is what “chain … Read more
Reasoning models and standard language models are not competing products — they are different tools with different strengths, costs, and failure modes. Choosing between them is a matter of matching the tool to the task, not a one-time decision about which technology is superior. The practical question is: given a specific task, does it benefit … Read more
Chain-of-thought and zero-shot prompting sit at opposite ends of the reasoning depth spectrum. Zero-shot asks a model for a direct answer with no examples or reasoning scaffolding. Chain-of-thought prompts the model to work through its reasoning before producing a final answer. For standard language models, the choice between them has a measurable impact on accuracy … Read more
QwQ is Alibaba’s open-weights reasoning model, released in late 2024 as part of the Qwen model family. It is a 32B parameter model trained specifically to reason through hard problems using extended chain-of-thought, and it achieved benchmark scores competitive with OpenAI o1-preview and DeepSeek R1 at the time of release. QwQ is notable for a … Read more
TensorFlow and PyTorch were once genuinely competing frameworks where the choice had significant practical consequences. In 2026, that competition is largely settled: PyTorch is the dominant framework for research and model development, and TensorFlow has a narrower but legitimate production deployment story. Understanding what each does well — and what the landscape actually looks like … Read more
bitsandbytes makes it possible to load large language models in 8-bit or 4-bit precision, slashing the GPU memory required without retraining. A 7B parameter model in float16 needs ~14GB VRAM; in 4-bit it needs ~4GB. This is the primary reason practitioners can now fine-tune LLaMA, Mistral, and similar models on a single consumer GPU. This … Read more
FlashAttention-2 is a rewritten attention algorithm that computes the exact same result as standard attention but 2–4x faster and with memory usage that scales linearly with sequence length instead of quadratically. It achieves this by tiling the attention computation to stay within GPU SRAM rather than reading from the slower HBM memory repeatedly. For anyone … Read more
Parameter-efficient fine-tuning (PEFT) methods adapt a pretrained large model to a new task by training only a small fraction of its parameters rather than the full model. The most widely used method is LoRA (Low-Rank Adaptation), which freezes the original weights and adds trainable low-rank matrices to specific layers. Hugging Face’s PEFT library provides a … Read more
The DataLoader is often the bottleneck that prevents your GPU from training at full utilisation. If your GPU sits at 30–60% utilisation between batches, the problem is almost certainly data loading — the CPU cannot prepare and transfer batches fast enough to keep the GPU busy. This guide covers every meaningful optimisation: worker count, pinned … Read more
PyTorch Lightning is a high-level wrapper around PyTorch that removes boilerplate from training loops while keeping you in control of the actual model logic. Vanilla PyTorch gives you maximum flexibility and transparency — every part of training is code you wrote. The decision between them is not about which is better in absolute terms: it … Read more
