We might not train the models with full dataset or very long epochs in these
videos since the purpose is to demonstrate platform usage. If you wish to
share a use case, feel free to post an issue on our
GitHub!.
Language Modeling with SFT
Videos in this section cover supervised fine-tuning (SFT), the most common method for adapting large language models to specific tasks using labeled datasets.Improving Gemma 270M by 50% at emotion classification
This example uses emotions dataset from Hugging Face Hub to fine-tune Gemma 270M for emotion classification. In just 20 minutes of training, evaluation performance improved significantly and we are able to quickly try it out locally with Ollama.Coming Soon
- Deploying models to cloud with vLLM
- LoRA on 4B models with vision datasets
- Fine tune 1B model on datasets with different language
- Using local datasets and dataset augmentation / synthesis
- Quickly benchmark Gemma models on common benchmark datasets from Hub
Reinforcement Learning with GRPO
- Coming soon with reasoning examples…
Preference Tuning with DPO / ORPO
- Coming soon…