Large Language Model tutorials focusing on local inference, model quantization, context window optimization, and structured JSON tool calling. Build reliable LLM applications with deterministic outputs, custom system prompts, and high-throughput inference servers.
When deploying DeepSeek-R1 (such as DeepSeek-R1-Distill-Qwen-14B or 32B) using vLLM, encountering a sudden torch.cuda.OutOfMemoryError: CUDA out of memory during startup…
The release of DeepSeek-R1 has transformed open-source artificial intelligence. By matching or exceeding OpenAI o1 on complex reasoning, mathematics, and…
When building enterprise Retrieval-Augmented Generation (RAG) systems in 2026, relying purely on dense vector embeddings (like OpenAI text-embedding-3 or BGE)…
Running Large Language Models locally has evolved from basic chat interfaces into sophisticated autonomous systems capable of executing deterministic software…
While simple sequential chains (like those in early LangChain) work for basic question-answering, production AI applications require loops, conditional branching,…