A Gentle Introduction to Language Model Fine-tuning

What’s Happening

Let’s talk about This article is divided into four parts; they are: • The Reason for Fine-tuning a Model • Dataset for Fine-tuning • Fine-tuning Procedure • Other Fine-Tuning Techniques Once you train your decoder-only transformer model, you have a text generator.

A Gentle Introduction to Language Model Fine-tuning By Adrian Tam on in Training Transformer Models 0 Post After pretraining, a language model learns about human languages. You can enhance the models domain-specific understanding on additional data. (shocking, we know)

You can also train the model to perform specific tasks when you provide a specific instruction.

The Details

These additional training after pretraining is called fine-tuning. In this article, you will learn how to fine-tune a language model.

Specifically, you will learn: Different examples of fine-tuning and what their goals are How to convert a pretraining script to perform fine-tuning Lets get kicked off! Overview This article is divided into four parts; they are: The Reason for Fine-tuning a Model Dataset for Fine-tuning Fine-tuning Procedure Other Fine-Tuning Techniques The Reason for Fine-tuning a Model Once you train your decoder-only transformer model, you have a text generator.

Why This Matters

You can provide any prompt, and the model will generate some text. What it generates depends on the model you have. Lets consider a simple generation algorithm: …

The AI space continues to evolve at a wild pace, with developments like this becoming more common.

Key Takeaways

• Def apply_repetition_penalty(logits: Tensor, tokens: list[int], penalty: float) - Tensor: """Apply repetition penalty to the logits.
• """ for tok in tokens: if logits[tok] 0: logits[tok] /= penalty else: logits[tok] *= penalty return logits @torch.
• No_grad() def generate(model, tokenizer, prompt, max_tokens=100, temperature=1.
• 0, repetition_penalty_range=10, top_k=50, device=None) - str: """Generate text autoregressively from a prompt.

The Bottom Line

No_grad() def generate(model, tokenizer, prompt, max_tokens=100, temperature=1. 0, repetition_penalty_range=10, top_k=50, device=None) - str: """Generate text autoregressively from a prompt.

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