# How to Train GPT-OSS for Your Language in 5 Easy Steps!

Source: https://www.youtube.com/watch?v=B5JWdtdM05w
Recap page: https://rapidrecap.app/video/B5JWdtdM05w
Generated: 2025-08-07T10:31:38.862+00:00

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## Quick Overview

This tutorial demonstrates how to fine-tune the OpenAI gpt-oss-20b model for your own language using Hugging Face Transformers. It covers setup, data preparation, model loading, fine-tuning with LoRA, and inference, enabling users to create multilingual reasoning models.

**Key Points:**
- Fine-tune OpenAI's gpt-oss-20b model for custom languages using Hugging Face Transformers and TRL.
- The process involves installing libraries, preparing a multilingual dataset, logging into Hugging Face, and loading the base model.
- LoRA (Low-Rank Adaptation) is used for efficient fine-tuning, targeting specific model layers.
- Training arguments are configured, including learning rate, batch size, and epochs.
- The fine-tuned model is saved and pushed to the Hugging Face Hub for accessibility.
- Inference is performed by setting the `REASONING_LANGUAGE` and running prompts in the desired language.

![Screenshot at 00:01: The video title slide clearly states the tutorial's focus: 'Fine-tuning with gpt-oss and Hugging Face Transformers', outlining the core subject matter.](https://ss.rapidrecap.app/screens/B5JWdtdM05w/00-00-01.png)

**Context:** Large reasoning models like OpenAI's o3 typically reason in English. This tutorial demonstrates how to adapt OpenAI's open-weight reasoning model, gpt-oss-20b, to effectively reason in multiple languages. It utilizes the Hugging Face ecosystem, including the Transformers library and the TRL library, to achieve this multilingual capability through fine-tuning.

## Detailed Analysis

This video tutorial guides users through fine-tuning OpenAI's gpt-oss-20b model to support custom languages, leveraging the Hugging Face Transformers library. The process begins with setting up the environment by installing necessary Python libraries like PyTorch, transformers, peft, and trl. It then proceeds to preparing the dataset, using the 'Multilingual-Thinking' dataset from Hugging Face, which contains reasoning tasks in multiple languages. The tutorial explains how to log into Hugging Face using a token, either through the notebook or command line. Next, it covers loading the pre-trained gpt-oss-20b model and configuring it for fine-tuning using LoRA (Low-Rank Adaptation), specifying target modules and parameters. The fine-tuning process itself is detailed, including setting training arguments such as learning rate, batch size, and epochs. The tutorial shows how to initiate the training using the SFTTrainer from the TRL library. Finally, it demonstrates how to save the fine-tuned model and push it to the Hugging Face Hub, making it available for inference. The video concludes by showing how to use the fine-tuned model for inference, including setting the `REASONING_LANGUAGE` to a desired language and running a sample prompt.

### Tutorial Steps

- Setup and Library Installation
- Data Preparation with Multilingual-Thinking Dataset
- Hugging Face Login
- Model Loading and Configuration with LoRA
- Fine-tuning Process with SFTTrainer
- Saving and Uploading the Model
- Inference with the Fine-tuned Model

### Key Libraries Used

- PyTorch
- Hugging Face Transformers
- TRL (Transformer Reinforcement Learning)
- PEFT (Parameter-Efficient Fine-Tuning)

### Fine-tuning Parameters

- LoRA Configuration (r=8, lora_alpha=16, target_modules='all-linear')
- Training Arguments (learning_rate=2e-4, num_train_epochs=1, per_device_train_batch_size=4, gradient_accumulation_steps=4, max_length=2048)

### Model Performance

- The fine-tuned model can generate reasoning steps in multiple languages, achieving results on par with OpenAI's proprietary models.

![Screenshot at 00:01: Title slide: 'Fine-tuning with gpt-oss and Hugging Face Transformers'](https://ss.rapidrecap.app/screens/B5JWdtdM05w/00-00-01.png)
![Screenshot at 00:26: Hugging Face model page for 'mervinpraison/gpt-oss-20b-multilingual-reasoner'](https://ss.rapidrecap.app/screens/B5JWdtdM05w/00-00-26.png)
![Screenshot at 00:30: Python code snippet showing how to initialize the text generation pipeline with the fine-tuned model.](https://ss.rapidrecap.app/screens/B5JWdtdM05w/00-00-30.png)
![Screenshot at 01:20: Terminal output showing successful installation of PyTorch and other required libraries.](https://ss.rapidrecap.app/screens/B5JWdtdM05w/00-01-20.png)
![Screenshot at 01:33: Hugging Face login prompt within the notebook, asking for a token.](https://ss.rapidrecap.app/screens/B5JWdtdM05w/00-01-33.png)
![Screenshot at 02:09: Hugging Face dataset page for 'HuggingFaceH4/Multilingual-Thinking', displaying multilingual data samples.](https://ss.rapidrecap.app/screens/B5JWdtdM05w/00-02-09.png)
![Screenshot at 02:47: Diagram illustrating the process of tokenizing input text, passing it to the LLM, and generating output probabilities.](https://ss.rapidrecap.app/screens/B5JWdtdM05w/00-02-47.png)
![Screenshot at 03:15: Python code showing how to load the AutoTokenizer from the pre-trained 'openai/gpt-oss-20b' model.](https://ss.rapidrecap.app/screens/B5JWdtdM05w/00-03-15.png)
![Screenshot at 03:37: Python code defining quantization configuration and loading the model with specified parameters.](https://ss.rapidrecap.app/screens/B5JWdtdM05w/00-03-37.png)
![Screenshot at 04:42: Python code defining the LoRA configuration with parameters like 'r', 'lora\_alpha', and 'target\_modules'. Trainable parameters count displayed: 15,040,512 out of 20,929,797,696 total parameters. Training loss decreasing over epochs shown in a table format.](https://ss.rapidrecap.app/screens/B5JWdtdM05w/00-04-42.png)