# #large-language-models — MDRSS hashtag feed

> Public MDRSS cards tagged #large-language-models.
> Canonical feed: https://mdrss.com/feeds/large-language-models

## Cards (9)

### [A Hands-On Guide to Fine-Tuning LLMs with PyTorch and Hugging Face](https://mdrss.com/llm-engineering/models-and-training/2517/2517.md)

Kindle | Paperback | PDF  Leanpub  | PDF  Gumroad  You can easily load the notebooks directly from GitHub using Colab and run them using a GPU provided by Google. You need to be logged in a Google Account of your own.

Classification: llm-engineering/models-and-training · Feed: llm-engineering · Updated: 2026-08-04T12:22:38.168Z · Version: 1

### [LLMVault](https://mdrss.com/security/threat-and-defense/2365/2365.md)

The Ultimate Hands-On OWASP LLM Top 10 Training Platform Learn • Exploit • Defend A deliberately-vulnerable, CTF-style training range for the OWASP Top 10 for LLM Applications (2025) — WebGoat / KubeGoat, but for AI. 25 labs across three tiers: ten core one-per-category labs; ten advanced, multi-turn labs (jailbreaking, data poisoning, agent exploitation, model extraction); and five expert labs modelling real-world attack classes.

Classification: security/threat-and-defense · Feed: security · Updated: 2026-08-04T12:22:38.168Z · Version: 1

### [ChainForge](https://mdrss.com/ai-agents/prompting/2004/2004.md)

An open-source visual environment for battle-testing prompts to LLMs. ChainForge is a data flow prompt engineering environment for analyzing and evaluating LLM responses.

Classification: ai-agents/prompting · Feed: ai-agents · Updated: 2026-08-04T12:22:38.168Z · Version: 1

### [LLM Finetuning Toolkit](https://mdrss.com/llm-engineering/models-and-training/1963/1963.md)

LLM Finetuning toolkit is a config-based CLI tool for launching a series of LLM fine-tuning experiments on your data and gathering their results. From one single yaml config file, control all elements of a typical experimentation pipeline - prompts, open-source LLMs, optimization strategy and LLM testing.

Classification: llm-engineering/models-and-training · Feed: llm-engineering · Updated: 2026-08-04T12:22:38.168Z · Version: 1

### [DLLM RL](https://mdrss.com/llm-engineering/serving-and-retrieval/1891/1891.md)

We also introduce a diffusion-based value model that reduces variance and improves stability during optimization. Based on TraceRL, we derive a series of diffusion language models, TraDo, which achieve state-of-the-art performance on math and coding reasoning tasks.

Classification: llm-engineering/serving-and-retrieval · Feed: llm-engineering · Updated: 2026-08-04T12:22:38.168Z · Version: 1

### [Demo](https://mdrss.com/llm-engineering/models-and-training/1557/1557.md)

A self-contained image bundles the tool + the Claude Code and Codex CLIs. You log in once and the sessions persist in named volumes — no re-login on later runs.

Classification: llm-engineering/models-and-training · Feed: llm-engineering · Updated: 2026-08-04T12:22:38.168Z · Version: 1

### [What you'll learn](https://mdrss.com/llm-engineering/serving-and-retrieval/1425/1425.md)

LLM Twin Course: Building Your Production-Ready AI Replica Learn to architect and implement a production-ready LLM & RAG system by building your LLM Twin From data gathering to productionizing LLMs using LLMOps good practices. by Decoding AI By finishing the "LLM Twin: Building Your Production-Ready AI Replica" free course, you will learn how to design, train, and deploy a production-ready LLM twin of yourself powered by LLMs, vector DBs, and LLMOps good practices.

Classification: llm-engineering/serving-and-retrieval · Feed: llm-engineering · Updated: 2026-08-04T12:22:38.168Z · Version: 1

### [PowerInfer: Fast Large Language Model Serving with a Consumer-grade GPU](https://mdrss.com/llm-engineering/serving-and-retrieval/1244/1244.md)

PowerInfer is a CPU/GPU LLM inference engine leveraging activation locality for your device. Project Kanban https://github.com/SJTU-IPADS/PowerInfer/assets/34213478/fe441a42-5fce-448b-a3e5-ea4abb43ba23 PowerInfer v.s.

Classification: llm-engineering/serving-and-retrieval · Feed: llm-engineering · Updated: 2026-08-04T12:22:38.168Z · Version: 1

### [LLM Course](https://mdrss.com/llm-engineering/serving-and-retrieval/1089/1089.md)

𝕏 Follow me on X • 🤗 Hugging Face • 💻 Blog • 📙 LLM Engineer's Handbook The LLM course is divided into three parts: 1. 🧩 LLM Fundamentals is optional and covers fundamental knowledge about mathematics, Python, and neural networks.

Classification: llm-engineering/serving-and-retrieval · Feed: llm-engineering · Updated: 2026-08-04T12:22:38.168Z · Version: 1