Fine-Tune Your First LLM
Fine-tune an LLM with LoRA in three commands using a pipeline template
The fastest way to fine-tune an LLM on Clarifai is to scaffold a project from the lora-pipeline-unsloth-quick-start pipeline template, then upload and run it. The template uses public data, sensible defaults, and auto-provisions compute — no dataset setup or hyperparameter tuning required for the first run.
Prerequisites
Install the Clarifai CLI and authenticate:
- CLI
pip install --upgrade clarifai
clarifai login
clarifai login auto-detects your user ID and saves your Personal Access Token (PAT) locally.
Fine-Tune the Model
Scaffold a project from the LoRA quick-start template, then upload and run it:
- CLI
clarifai pipeline init --template lora-pipeline-unsloth-quick-start
cd lora-pipeline-unsloth-quick-start
clarifai pipeline upload
clarifai pipeline run --instance=g5.xlarge
This trains a LoRA fine-tune of unsloth/Qwen3-0.6B on the mlabonne/FineTome-100k dataset using Unsloth. --instance=g5.xlarge auto-provisions a compute cluster and nodepool — no separate cluster setup required, and the same nodepool can later serve inference on the fine-tuned model.
When training completes, the fine-tuned model is registered in your Clarifai model registry, ready to serve, evaluate, or refine further.
Customize Before Running
To override defaults at init time — a different base model, more epochs, custom LoRA rank — pass --set key=value flags. For example, to fine-tune Llama 3.2 1B for three epochs:
- CLI
clarifai pipeline init --template lora-pipeline-unsloth-quick-start \
--set base_model_name="unsloth/Llama-3.2-1B-Instruct" \
--set num_epochs=3
See the Pipeline Templates reference for all customizable parameters.
What to Explore Next
- Train a Visual Classifier — Train an image classifier (ResNet) using a different pipeline template.
- Train a Visual Detector — Train an object detector (YOLOF) using a different pipeline template.
- Clarifai Pipelines — The Pythonic-first pipeline authoring story, including the DSL for custom workflows.
- Run inference on the fine-tuned model — Use the trained LLM for predictions.