# #finetuning — MDRSS hashtag feed

> Public MDRSS cards tagged #finetuning.
> Canonical feed: https://mdrss.com/feeds/finetuning

## Cards (5)

### [LLMTools: Run & Finetune LLMs on Consumer GPUs](https://mdrss.com/llm-engineering/inference-and-quantization/901083/901083.md)

LLMTools is a user-friendly library for running and finetuning LLMs in low-resource settings. Features include: 🔨 LLM finetuning in 2-bit, 3-bit, 4-bit precision using the ModuLoRA algorithm 🐍 Easy-to-use Python API for quantization, inference, and finetuning 🤖 Modular supp. Use it to navigate the topic and choose relevant methods, papers or tools.

Classification: llm-engineering/inference-and-quantization · Feed: llm-engineering · Updated: 2026-08-04T13:54:51.641Z · Version: 1

### [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

### [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

### [XTuring](https://mdrss.com/llm-engineering/models-and-training/1740/1740.md)

Fine‑tune, evaluate, and run private, personalized LLMs xTuring makes it simple, fast, and cost‑efficient to fine‑tune open‑source LLMs (e.g., GPT‑OSS, LLaMA/LLaMA 2, Qwen3, MiniMax M2, GPT‑J, GPT‑2, DistilGPT‑2, Mamba) on your own data — locally or in your private cloud. Why xTuring: Run a small, CPU‑friendly example first: Want bigger models and reasoning controls?

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

### [LazyLLM: A Low-code Development Tool For Building Multi-agent LLMs Applications](https://mdrss.com/ai-agents/agent-frameworks/1376/1376.md)

中文 | EN LazyLLM is a low-code development tool for building multi-agent large language model applications. It assists developers in creating complex AI applications at very low costs and enables continuous iterative optimization.

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