Train Your Own GPT Model in Your Language Without Starting From Scratch
In this video, you'll learn how to fine-tune the GPT OSS model — a powerful open-source language model released by OpenAI under the Apache 2.0 license — using Hugging Face Transformers and the `trl` + `peft` libraries. https://mer.vin/2025/08/gpt-oss-finetuning/ https://github.com/openai/openai-cookbook/blob/main/articles/gpt-oss/fine-tune-transfomers.ipynb https://cookbook.openai.com/articles/gpt-oss/fine-tune-transfomers 0:00 - Fine-tuning GPT-OSS with Hugging Face 1:15 - System config and setup 2:00 - Step 2: Prepare the dataset 3:36 - Step 3: Prepare the model 4:59 - Step 4: Fine-tuning the model 6:05 - Step 5: Inference and integration 6:43 - Summary By default, GPTOSS reasons in English even when prompted in other languages. To overcome this, we demonstrate how to fine-tune it using a multilingual dataset so it can think and respond in your target language. The tutorial is broken down into 5 clear steps: 1. **Setup** – Install required libraries like `torch`, `transformers`, `trl`, and `peft`. 2. **Prepare the Dataset** – Use the `HuggingFaceH4/Multilingual-Thinking` dataset, tokenize the inputs, and structure them for training. 3. **Prepare the Model** – Load the GPTOSS 20B model with a LoRA configuration to efficiently fine-tune only a small portion of the model (~15M parameters). 4. **Fine-tuning** – Configure and train the model using `SFTTrainer`, monitor training loss, and push the model to Hugging Face Hub. 5. **Inference** – Load the fine-tuned model and run inference in your own applications using Python and the Hugging Face `pipeline`. You'll also learn: - How to log into Hugging Face from Colab or your terminal using `huggingface-cli login`. - How tokenization works before and after model prediction. - How to store and retrieve the model from the Hugging Face Hub. - How to integrate the fine-tuned model into your own apps using a few lines of Python code. GPU used: RTX A6000 (via M Compute) Discount: Use the coupon mentioned in the video for 50% off on GPU rental. All the code, configuration, and Hugging Face model links are provided in the description. Watch till the end to also see a mention of Unsloth for faster fine-tuning! Try it out, integrate it into your app, and let me know what you build! Explore how to fine-tune a GPT-OSS model using Hugging Face Transformers for specific languages. This detailed guide covers dataset preparation, model setup, and the fine-tuning process, and shows how to run the trained model. Learn about **natural language processing** and **llm** capabilities to enhance your **machine learning** projects using **ai tools** and **artificial intelligence**.
