Updates and recent posts about Unsloth..

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@work4bots started using tool Spring , 2 weeks, 2 days ago.

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@work4bots started using tool Helm , 2 weeks, 2 days ago.

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@work4bots started using tool Azure Pipelines , 2 weeks, 2 days ago.

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@work4bots started using tool Azure Kubernetes Service (AKS) , 2 weeks, 2 days ago.

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@work4bots started using tool Azure , 2 weeks, 2 days ago.

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@work4bots added a new tool Bicep , 2 weeks, 2 days ago.

Story FAUN.dev() Team

@eon01 shared a post, 2 weeks, 3 days ago

Founder, FAUN.dev

AWX in Action is out, and there's a course

#red hat... #ansible #Tower #AWX

"AWX in Action: Ansible Orchestration at Scale" is now available in print and ebook. It covers running AWX on Kubernetes for real, not a sandbox demo that falls over the moment you add a second execution node.

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@varbear shared a link, 2 weeks, 3 days ago

FAUN.dev()

GitHub breach: The development ecosystem is in the hot seat

GitHub is reeling from an infrastructure breach by TeamPCP, highlighting the vulnerability of developer environments. Privileged access was achieved not through traditional perimeter exploitation, but by targeting trusted developer tools like IDE extensions. This incident serves as a stark reminder .. read more

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@varbear shared a link, 2 weeks, 3 days ago

FAUN.dev()

When Code Becomes Cheap, What's Left?

Teams that use Claude Opus 4.6 for spec-driven development generate code at low cost, so they spend scarce developer time on review and QA. Developers create more value by judging code than by typing it... read more

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@varbear shared a link, 2 weeks, 3 days ago

FAUN.dev()

Design Patterns Are Dead. Long Live Design Patterns.

Design patterns were created for human comprehension, not machines, serving as a shared vocabulary to communicate complex ideas quickly, manage working memory, and standardize solutions. Even in the era of AI-generated code, design patterns are crucial for containing the limitations of AI models and.. read more

Unsloth is an open-source toolkit for training and fine-tuning large language models faster and with less memory than a standard Hugging Face stack. Its core library replaces PyTorch's default autograd with custom backpropagation kernels written in OpenAI's Triton language, which is where most of its speed and memory savings come from. It supports LoRA, QLoRA, full fine-tuning, reinforcement learning, pretraining, and 4-bit, 16-bit, and FP8 training, across more than 500 text, vision, audio, and embedding models.

The practical draw is hardware reach. QLoRA workflows in Unsloth let you fine-tune an 8B model on a single 12 GB consumer GPU, and the project headlines roughly 2x faster training with about 70 percent less VRAM versus baseline implementations, though the exact figures vary by model, GPU, and config. A 2026 update added faster mixture-of-experts training, with models like Qwen3-30B-A3B fine-tunable on about 17.5 GB of VRAM. It runs on NVIDIA (including Blackwell and DGX Spark), AMD, and Intel GPUs, with free Colab and Kaggle notebooks for trying it without local hardware.

It fits cleanly into the local-AI workflow. Unsloth integrates with Hugging Face transformers and TRL, and uses llama.cpp to save and run models, exporting to GGUF for Ollama or LM Studio as well as safetensors. As of 2026 it also ships Unsloth Studio, a local no-code GUI that covers the full lifecycle from dataset creation to training to running and comparing GGUF and safetensors models, with tool-calling, web search, and an OpenAI-compatible API, all running offline on Mac and Windows, with the core library under the Apache 2.0 license.