References: Fine-tune a pretrained model

Source material

Source curriculum (structural mirror, cited as further study):
• Hugging Face, "LLM Course", Chapter 3: "Fine-tuning a pretrained model"
  Authors: the Hugging Face team (Lewis Tunstall, Leandro von Werra,
    Lysandre Debut, Sylvain Gugger, Merve Noyan, and others)
  Course page: https://huggingface.co/learn/llm-course/chapter3
  Code and notebooks: https://github.com/huggingface/course
  License: Apache 2.0 (prose and code)
  Required attribution: "Based on the Hugging Face LLM Course
    (huggingface.co/learn/llm-course), © Hugging Face, used under the
    Apache 2.0 license. This is an independent structural mirror;
    Hugging Face does not endorse it."
This lesson mirrors the structure of Chapter 3 (processing the data, the
data collator, the Trainer API, and evaluation with compute_metrics).
Clawdemy's lessons are original prose that follows the pedagogical arc of
the course. We do not reproduce or transcribe the course; we cite it as the
recommended companion. Course materials are used under the Apache 2.0 license
with the attribution above, which requires a link to the license and an
indication of changes, and does not permit implying endorsement.

Read this next

• Hugging Face LLM Course, Chapter 3: Fine-tuning a pretrained model. The chapter this lesson mirrors. It also walks the data-processing section in full and shows the same fine-tune written as a pure PyTorch training loop, which is worth seeing once to know what the Trainer is doing for you.

Going deeper

A short, durable list. Each link is a specific next step, not a generic pile.

• The transformers training guide. The official docs version of this lesson, with more detail on preparing datasets and the full set of training options.

• TrainingArguments API reference. Every knob the configuration object exposes. Skim it once so you know what is available before you need it (learning rate, schedules, logging, checkpointing, mixed precision).

• The evaluate library. The metrics catalog used in compute_metrics. Browse it to see which metric is standard for your task before you invent your own.

Adjacent topics

Where this connects inside the track.

• Run a model in a few lines (lesson 2). Fine-tuning works at the lower level you opened up there (tokenizer, model, logits). The argmax-then-softmax intuition and the Auto classes carry straight over.

• Share your work on the Hub (lesson 4). The next lesson publishes the model you just fine-tuned, with a model card, closing Phase 1’s run-adapt-share arc. Passing push_to_hub=True in TrainingArguments is the bridge.