Fine-tuning a language model doesn’t have to be daunting.In our
previous post on fine-tuning models with Docker Offload and
Unsloth, we walked through how to train small, local models
efficiently using Docker’s familiar workflows.This time, we’re
narrowing the focus. Instead of asking a model to be good at
everything, we can specialize it:teaching it a
narrow but valuable skill, like consistently masking personally
identifiable information (PII) in text.Thanks to techniques like
LoRA (Low-Rank Adaptation), this process is not Fine-tuning a language model doesn’t have to be daunting.In our
previous post on fine-tuning models with Docker Offload and
Unsloth, we walked through how to train small, local models
efficiently using Docker’s familiar workflows.This time, we’re
narrowing the focus. Instead of asking a model to be good at
everything, we can specialize it:teaching it a
narrow but valuable skill, like consistently masking personally
identifiable information (PII) in text.Thanks to techniques like
LoRA (Low-Rank Adaptation), this process is not 
Docker Captains are leaders from the developer community that are
both experts in their field and are passionate about sharing their
Docker knowledge with others.“From the Captain’s Chair” is a blog
series where we get a closer look at one Captain to learn more
about them and their experiences. Today, we are interviewing
Pradumna Saraf.He is an Open Source Developer with a
passion for DevOps.He is also a Golang developer and loves
educating people through social media and blogs about various
DevOps tools Running large language models (LLMs) on your local machine is one
of the most exciting frontiers in AI development.At Docker, our
goal is to make this process as simple and accessible as
possible.That’s why we built Docker Model Runner, a tool to help you
download and run LLMs with a single command. Until now,
GPU-accelerated inferencing with Model Runner was limited to CPU,
NVIDIA GPUs (via CUDA), and Apple Silicon (via Metal).Today, we’re
thrilled to announce a major step forward in democratizing local 
This is part of the Powered by Docker series, where we feature
use cases and success stories from Docker partners and
practitioners.This story was contributed by Ryan Wanner.Ryan has
more than fifteen years of experience as an entrepreneur and 3
years in AI space developing software and is the founder of Open
Source Genius. Open Source Genius is a
start-up that helps organizations navigate an AI-powered future by
building practical, human-centered AI systems.In early 2025, OSG
had a good problem:demand.With multiple ventures 
Developers can now discover and run IBM’s latest open-source
Granite 4.0 language models from the Docker Hub
model catalog, and start building in minutes with Docker
Model Runner.Granite 4.0 pairs strong, enterprise-ready
performance with a lightweight footprint, so you can prototype
locally and scale confidently. The Granite 4.0 family is designed
for speed, flexibility, and cost-effectiveness, making it easier
than ever to build and deploy generative AI applications.About Docker
Hub
Docker Hub is the world’s largest registry for Weitere Beiträge ...
- Unlimited access to Docker Hardened Images: Because security should be affordable, always
- Docker at AI Engineer Paris: Build and Secure AI Agents with Docker
- Llama.cpp Gets an Upgrade: Resumable Model Downloads
- From Shell Scripts to Science Agents: How AI Agents Are Transforming Research Workflows
- Fine-Tuning Local Models with Docker Offload and Unsloth
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