Let's fine-tune OpenAI gpt-oss (100% locally):

Let's fine-tune OpenAI gpt-oss (100% locally):

1️⃣ Load the model We start by loading the gpt-oss (20B variant) model and its tokenizer using Unsloth. Check this 👇

2️⃣ Define LoRA config We'll use LoRA for efficient fine-tuning. To do this, we use Unsloth's PEFT and specify: - The model - LoRA low-rank (r) - Layers for fine-tuning, etc. Check this code 👇

3️⃣ Load dataset We'll fine-tune gpt-oss and help it develop multi-lingual reasoning capabilities. So we load the multi-lingual thinking dataset, which has: - User query in English. - Reasoning in different languages. - Response in English. Check this 👇

4️⃣ Prepare dataset Before fine-tuning, we must prepare the dataset in a conversational format: - We standardize the dataset. - We pick the messages field. - We apply the chat template to it. Check the code and a data sample 👇

5️⃣ Define Trainer Here, we create a Trainer object by specifying the training config, like learning rate, model, tokenizer, and more. Check this out 👇

6️⃣ Train With that done, we initiate training. The loss is generally decreasing with steps, which means the model is being fine-tuned correctly. Check this code and training logs 👇

Finally, the video shows prompting the LLM before and after fine-tuning. After fine-tuning, the model is able to generate the reasoning tokens in French before generating the final response in English. Check this 👇