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Let's fine-tune DeepSeek-R1 (distilled Llama) 100% locally:
Before we begin, here's what we'll be doing. We'll fine-tune our private and locally running DeepSeek-R1 (distilled Llama variant). To do this, we'll use: - @UnslothAI for efficient fine-tuning. - @ollama to run it locally. Let's begin!
1) Load the model We start by loading the Distilled Llama-8B model and the tokenizer of DeepSeek-R1 using Unsloth:
2) Define LoRA config We must use efficient techniques like LoRA to avoid fine-tuning the entire model weights. In this code, we use Unsloth's PEFT by specifying: - The model - LoRA low-rank (r) - Modules for fine-tuning - and a few more parameters.
3) Prepare dataset Next, we use the Alpaca dataset to prepare a conversation dataset. The conversation_extension parameter defines the number of user messages in a single conversation.
4) Define Trainer Here, we create a Trainer object by specifying the training config like learning rate, model, tokenizer, and more. Check this out👇
5) Train With that done, we initiate training. We notice a decreasing loss, which means the model is fine-tuning well. Check this code and output👇
6) Export to Ollama Finally, we export the model to Ollama as follows. Done!
We have fine-tuned DeepSeek (distilled Llama). Now we can interact with it like any other model running on Ollama using: - The CLI - Ollama's Python package - Ollama's LlamaIndex integration, etc.
That's a wrap! If you enjoyed this tutorial: Find me → @_avichawla Every day, I share tutorials and insights on DS, ML, LLMs, and RAGs.