LLM Training on SoloSoft

LlamaFactory: Open-Source LLM Fine-Tuning Framework

Fri, 01 May 2026 00:00:00 +0000

Fine-tuning large language models was once a complex, resource-intensive process reserved for organizations with large GPU clusters. LlamaFactory has democratized this capability, providing an accessible, feature-rich framework that makes fine-tuning hundreds of LLM architectures practical on consumer-grade hardware.

Created by the research community (hiyouga/LlamaFactory), this framework has grown into one of the most popular open-source fine-tuning tools, supporting everything from a simple LoRA adjustment on a single GPU to full distributed training across multiple nodes. It abstracts away the complexity of training infrastructure, letting practitioners focus on data, configuration, and evaluation.

What makes LlamaFactory particularly valuable is its comprehensive support for parameter-efficient fine-tuning methods. Full fine-tuning of a 70B model requires over 140GB of GPU memory. Using QLoRA in LlamaFactory, the same task can be accomplished on a single 24GB GPU with minimal quality loss – a 6x reduction in hardware requirements.

llm.c: Karpathy's Minimal C Implementation of LLM Training

Fri, 01 May 2026 00:00:00 +0000

Most developers and researchers who work with large language models interact with them through high-level frameworks like PyTorch or Hugging Face Transformers. These frameworks hide immense complexity behind elegant APIs, but they also obscure the fundamental mechanics of how these models actually learn. llm.c tears away that abstraction, providing a complete, working implementation of GPT-2 training in pure C.

Created by Andrej Karpathy (formerly Director of AI at Tesla, co-founder of OpenAI), llm.c is first and foremost an educational project. It implements the entire forward pass, backward pass, and training loop for a transformer language model using nothing but standard C libraries, without a single dependency on PyTorch, TensorFlow, or any machine learning framework.

olmOCR: AI2's Open-Source PDF-to-Markdown Toolkit for LLM Training Data

Fri, 01 May 2026 00:00:00 +0000

Converting PDFs to clean, machine-readable text at scale is one of the foundational challenges in LLM dataset preparation. Traditional PDF parsers struggle with complex layouts, tables, and mixed content, while commercial OCR services are expensive at scale. olmOCR by Allen AI (AI2) solves this problem using a 7B parameter Vision-Language Model that converts PDF pages into clean Markdown with remarkable accuracy and cost efficiency.

The key insight behind olmOCR is treating PDF conversion as a vision-language task rather than a text extraction problem. Instead of parsing the underlying PDF structure (which is often unreliable for complex layouts), olmOCR renders each page to an image and uses its VLM to read and transcribe the content, preserving layout, structure, and semantics.