> For the complete documentation index, see [llms.txt](https://unsloth.ai/docs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://unsloth.ai/docs/blog.md). # Blog - [3x Faster LLM Training with Unsloth Kernels + Packing](https://unsloth.ai/docs/blog/3x-faster-training-packing.md): Learn how Unsloth increases training throughput and eliminates padding waste for fine-tuning. - [500K Context Length Fine-tuning](https://unsloth.ai/docs/blog/500k-context-length-fine-tuning.md): Learn how to enable >500K token context window fine-tuning with Unsloth. - [Quantization-Aware Training (QAT)](https://unsloth.ai/docs/blog/quantization-aware-training-qat.md): Quantize models to 4-bit with Unsloth and PyTorch to recover accuracy. - [Fine-Tuning LLMs on NVIDIA DGX Station with Unsloth](https://unsloth.ai/docs/blog/dgx-station.md): NVIDIA DGX Station tutorial on how to fine-tune with notebooks from Unsloth. - [How to Fine-tune LLMs with Unsloth & Docker](https://unsloth.ai/docs/blog/how-to-fine-tune-llms-with-unsloth-and-docker.md): Learn how to fine-tune LLMs or do Reinforcement Learning (RL) with Unsloth's Docker image. - [Fine-tuning LLMs with NVIDIA DGX Spark and Unsloth](https://unsloth.ai/docs/blog/fine-tuning-llms-with-nvidia-dgx-spark-and-unsloth.md): Tutorial on how to fine-tune and do reinforcement learning (RL) with OpenAI gpt-oss on NVIDIA DGX Spark. - [Fine-tuning LLMs with Blackwell, RTX 50 series & Unsloth](https://unsloth.ai/docs/blog/fine-tuning-llms-with-blackwell-rtx-50-series-and-unsloth.md): Learn how to fine-tune LLMs on NVIDIA's Blackwell RTX 50 series and B200 GPUs with our step-by-step guide. - [How to Run Diffusion Image GGUFs in ComfyUI](https://unsloth.ai/docs/blog/comfyui.md): Guide for running Unsloth Diffusion GGUF models in ComfyUI. - [AI Engineer's 2025](https://unsloth.ai/docs/blog/ai-engineers-2025.md): Slides to our AI Engineer's Worlds Fair 2025 Workshop. - [Unsloth - AI Engineer World's Fair 2026](https://unsloth.ai/docs/blog/ai-engineer-2026.md): Slides to our AI Engineer's Worlds Fair 2026 Workshop. - [GPU Mode - Reinforcement Learning Mini Conference 2026](https://unsloth.ai/docs/blog/gpu-mode-conference.md): Slides to our PyTorch Conference 2025 Talks. - [Unsloth AMD PyTorch Synthetic Data Hackathon](https://unsloth.ai/docs/blog/unsloth-amd-pytorch-synthetic-data-hackathon.md): Tips & tricks, troubleshooting and guide to run Unsloth on an AMD GPU. - [PyTorch Conference 2025 - Unsloth](https://unsloth.ai/docs/blog/pytorch-conference-2025-unsloth.md): Slides to our PyTorch Conference 2025 Talks. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://unsloth.ai/docs/blog.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.