Fine-Tune the biggest open-source models (even with a bad PC)

The video begins by introducing the Kimi K2.7 Code model as a powerful and cost-effective alternative for fine-tuning large language models, comparing its performance and cost favorably against models like Claude Opus 4.8. The presenter, David Ondrej, emphasizes the importance of using open-source models and highlights the challenges of fine-tuning large models due to hardware and cost constraints. He then transitions to a demonstration using the Fireworks AI platform, outlining the necessary components: an open-source model (Kimi K2.7 Code), powerful GPUs (which Fireworks AI provides), and a suitable dataset. The process involves acquiring Hugging Face access tokens, downloading a dataset (Fable 5 Chain-of-Fold merge dataset), converting it to the required JSON format using a provided script, and then initiating the fine-tuning job on Fireworks AI. The demonstration includes setting up the environment variables with the API key and configuring the fine-tuning parameters. Finally, the presenter shows how to deploy the fine-tuned model on Fireworks AI, illustrating the ease of use and cost benefits compared to self-hosting.