Let's fine-tune DeepSeek-R1 (distilled Llama) 100% locally:

41 views Jan 28, 2025 ~1 min read

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Let's fine-tune DeepSeek-R1 (distilled Llama) 100% locally:

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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!

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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.

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That's a wrap!

If you enjoyed this tutorial:

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