カテゴリ
技術
LLM チュートリアル
大規模言語モデルに関する最新ニュース、手法、リソースを追いかけましょう。実践的な手順解説やスキルアップに役立つユースケースを豊富に掲載しています。
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SGLang Tutorial: Serving Mistral Medium 3.5 Locally
Set up a multi-GPU Docker environment with tensor parallelism and EAGLE speculative decoding to serve Mistral Medium 3.5 128B through an OpenAI-compatible API.
Abid Ali Awan
2026年6月1日
Multi-Token Prediction Tutorial: How To Speed Up LLMs
Run Qwen3.6 27B on an RTX 3090 and learn how Multi-Token Prediction (MTP) with llama.cpp can boost local LLM inference by almost 2x without upgrading your GPU.
Abid Ali Awan
2026年5月14日
GPT-Realtime-2 API Tutorial: Three Tests, Three Verdicts
Learn how OpenAI's gpt-realtime-2, gpt-realtime-translate, and gpt-realtime-whisper differ, then test each one with working Python WebSocket code.
Khalid Abdelaty
2026年5月12日
How to Run DeepSeek V4 Flash Locally
Learn how to run the full DeepSeek V4 Flash model on a single GPU using a modified llama.cpp build and a compatible GGUF file in this hands-on tutorial.
Abid Ali Awan
2026年5月5日
DeepSeek V4 API Tutorial: Building a Thinking Mode Arena
Learn how DeepSeek V4's three reasoning modes work, and build a Streamlit comparison app that shows when each mode actually wins on quality, speed, and cost.
Aashi Dutt
2026年4月30日
Fine-Tuning NVIDIA Nemotron-3-Nano On Psychology Q&A Data
Learn to fine-tune NVIDIA Nemotron-3-Nano-4B on a psychology Q&A dataset using an RTX 3090 GPU using LoRA and TRL after downloading the model from Hugging Face.
Abid Ali Awan
2026年4月29日
Qwen 3.6 Plus API Tutorial: Building an Invoice Processing Pipeline in Python
Learn how to use Alibaba's Qwen 3.6 Plus, Python, and the OpenAI SDK to build an automated invoice processing pipeline with native vision and tool calling.
Bex Tuychiev
2026年4月27日
Fine-Tuning Qwen3.6 On a Medical Q&A Dataset
Learn how to fine-tune Qwen3.6 on an H100 NVL GPU using SFT, from dataset preparation and 4-bit loading to training and evaluation.
Abid Ali Awan
2026年4月21日
LLM As a Judge: A Complete Guide With Hands-On RAG Example
Learn how to build an automated LLM-as-a-judge system to evaluate your RAG pipelines for faithfulness and relevance at scale and bridge the gap in AI testing.
Josep Ferrer
2026年4月20日
Claude Opus 4.7 API Tutorial: Building a Chart Digitizer
Learn the capabilities of Anthropic’s best publicly available model, Claude Opus 4.7, and build a data science tool that can turn a chart into raw data.
François Aubry
2026年4月20日
Running MiniMax M2.7 Locally for Agentic Coding
In this guide, we will rent an H200 GPU and install llama.cpp, download MiniMax M2.7 GGUF, run it locally, test it in the WebUI, and connect it to OpenCode.
Abid Ali Awan
2026年4月17日
Ollama Python Library: Getting Started with LLMs Locally
Master the Ollama Python SDK for local LLM development. Learn to generate text, handle multi-turn chats, use vision models, and build AI apps securely.
Austin Chia
2026年4月17日