Inspiration, then our bar
Sai Rahul (@sairahul1) published a widely circulated X article, How To Fine-Tune a Small LLM on Your Own Data (Full Guide). The piece is worth reading on your own time. This chapter does not paste it, does not walk his notebook cell-for-cell, and does not treat a blog as the unlock.
PromptDojo keeps the thesis — small model, your data, one specialist — and grades the parts that usually get skipped: the prompt/RAG/FT gate, the job sentence, human review, company-level splits, and an eval receipt that can fail. You already practiced the first two forks in chapter 22. This chapter is the third fork with a shippable artifact.
The academy bar for the rest of the path still applies. Multiple-choice distractors are real engineering mistakes (fine-tune for a knowledge gap, leaky splits, unreviewed rows, "loss went down so we shipped"). Code in the browser is explained in plain English and runs without a GPU. The Colab notebook is the place a T4 actually trains.
If you came here hoping the 1.5B model becomes a generally smarter teammate, stop and go back to prompting and retrieval. If you have a named job, a reviewer, and a T4 hour (or a fixture you will not lie about), continue.
Inspiration, then our bar
Sai Rahul (@sairahul1) published a widely circulated X article, How To Fine-Tune a Small LLM on Your Own Data (Full Guide). The piece is worth reading on your own time. This chapter does not paste it, does not walk his notebook cell-for-cell, and does not treat a blog as the unlock.
PromptDojo keeps the thesis — small model, your data, one specialist — and grades the parts that usually get skipped: the prompt/RAG/FT gate, the job sentence, human review, company-level splits, and an eval receipt that can fail. You already practiced the first two forks in chapter 20. This chapter is the third fork with a shippable artifact.
The academy bar for the rest of the path still applies. Multiple-choice distractors are real engineering mistakes (fine-tune for a knowledge gap, leaky splits, unreviewed rows, "loss went down so we shipped"). Code in the browser is explained in plain English and runs without a GPU. The Colab notebook is the place a T4 actually trains.
If you came here hoping the 1.5B model becomes a generally smarter teammate, stop and go back to prompting and retrieval. If you have a named job, a reviewer, and a T4 hour (or a fixture you will not lie about), continue.
Inspiration, then our bar
Sai Rahul (@sairahul1) published a widely circulated X article, How To Fine-Tune a Small LLM on Your Own Data (Full Guide). The piece is worth reading on your own time. This chapter does not paste it, does not walk his notebook cell-for-cell, and does not treat a blog as the unlock.
PromptDojo keeps the thesis — small model, your data, one specialist — and grades the parts that usually get skipped: the prompt/RAG/FT gate, the job sentence, human review, company-level splits, and an eval receipt that can fail. You already practiced the first two forks in the context and retrieval chapter. This chapter is the third fork with a shippable artifact.
The academy bar for the rest of the path still applies. Multiple-choice distractors are real engineering mistakes (fine-tune for a knowledge gap, leaky splits, unreviewed rows, "loss went down so we shipped"). Code in the browser is explained in plain English and runs without a GPU. The Colab notebook is the place a T4 actually trains.
If you came here hoping the 1.5B model becomes a generally smarter teammate, stop and go back to prompting and retrieval. If you have a named job, a reviewer, and a T4 hour (or a fixture you will not lie about), continue.
Inspiration, then our bar
Sai Rahul (@sairahul1) published a widely circulated X article, How To Fine-Tune a Small LLM on Your Own Data (Full Guide). The piece is worth reading on your own time. This chapter does not paste it, does not walk his notebook cell-for-cell, and does not treat a blog as the unlock.
PromptDojo keeps the thesis — small model, your data, one specialist — and grades the parts that usually get skipped: the prompt/RAG/FT gate, the job sentence, human review, company-level splits, and an eval receipt that can fail. You already practiced the first two forks in the context and retrieval chapter. This chapter is the third fork with a shippable artifact.
The academy bar for the rest of the path still applies. Multiple-choice distractors are real engineering mistakes (fine-tune for a knowledge gap, leaky splits, unreviewed rows, "loss went down so we shipped"). Code in the browser is explained in plain English and runs without a GPU. The Colab notebook is the place a T4 actually trains.
If you came here hoping the 1.5B model becomes a generally smarter teammate, stop and go back to prompting and retrieval. If you have a named job, a reviewer, and a T4 hour (or a fixture you will not lie about), continue.
Inspiration, then our bar
Sai Rahul (@sairahul1) published a widely circulated X article, How To Fine-Tune a Small LLM on Your Own Data (Full Guide). The piece is worth reading on your own time. This chapter does not paste it, does not walk his notebook cell-for-cell, and does not treat a blog as the unlock.
PromptDojo keeps the thesis — small model, your data, one specialist — and grades the parts that usually get skipped: the prompt/RAG/FT gate, the job sentence, human review, company-level splits, and an eval receipt that can fail. You already practiced the first two forks in the context and retrieval chapter. This chapter is the third fork with a shippable artifact.
The academy bar for the rest of the path still applies. Multiple-choice distractors are real engineering mistakes (fine-tune for a knowledge gap, leaky splits, unreviewed rows, "loss went down so we shipped"). Code in the browser is explained in plain English and runs without a GPU. The Colab notebook is the place a T4 actually trains.
If you came here hoping the 1.5B model becomes a generally smarter teammate, stop and go back to prompting and retrieval. If you have a named job, a reviewer, and a T4 hour (or a fixture you will not lie about), continue.
Inspiration, then our bar
Sai Rahul (@sairahul1) published a widely circulated X article, How To Fine-Tune a Small LLM on Your Own Data (Full Guide). The piece is worth reading on your own time. This chapter does not paste it, does not walk his notebook cell-for-cell, and does not treat a blog as the unlock.
PromptDojo keeps the thesis — small model, your data, one specialist — and grades the parts that usually get skipped: the prompt/RAG/FT gate, the job sentence, human review, company-level splits, and an eval receipt that can fail. You already practiced the first two forks in chapter 10. This chapter is the third fork with a shippable artifact.
The academy bar for the rest of the path still applies. Multiple-choice distractors are real engineering mistakes (fine-tune for a knowledge gap, leaky splits, unreviewed rows, "loss went down so we shipped"). Code in the browser is explained in plain English and runs without a GPU. The Colab notebook is the place a T4 actually trains.
If you came here hoping the 1.5B model becomes a generally smarter teammate, stop and go back to prompting and retrieval. If you have a named job, a reviewer, and a T4 hour (or a fixture you will not lie about), continue.
Inspiration, then our bar
Sai Rahul (@sairahul1) published a widely circulated X article, How To Fine-Tune a Small LLM on Your Own Data (Full Guide). The piece is worth reading on your own time. This chapter does not paste it, does not walk his notebook cell-for-cell, and does not treat a blog as the unlock.
PromptDojo keeps the thesis — small model, your data, one specialist — and grades the parts that usually get skipped: the prompt/RAG/FT gate, the job sentence, human review, company-level splits, and an eval receipt that can fail. You already practiced the first two forks in chapter 13. This chapter is the third fork with a shippable artifact.
The academy bar for the rest of the path still applies. Multiple-choice distractors are real engineering mistakes (fine-tune for a knowledge gap, leaky splits, unreviewed rows, "loss went down so we shipped"). Code in the browser is explained in plain English and runs without a GPU. The Colab notebook is the place a T4 actually trains.
If you came here hoping the 1.5B model becomes a generally smarter teammate, stop and go back to prompting and retrieval. If you have a named job, a reviewer, and a T4 hour (or a fixture you will not lie about), continue.
Inspiration, then our bar
Sai Rahul (@sairahul1) published a widely circulated X article, How To Fine-Tune a Small LLM on Your Own Data (Full Guide). The piece is worth reading on your own time. This chapter does not paste it, does not walk his notebook cell-for-cell, and does not treat a blog as the unlock.
PromptDojo keeps the thesis — small model, your data, one specialist — and grades the parts that usually get skipped: the prompt/RAG/FT gate, the job sentence, human review, company-level splits, and an eval receipt that can fail. You already practiced the first two forks in the context and retrieval chapter. This chapter is the third fork with a shippable artifact.
The academy bar for the rest of the path still applies. Multiple-choice distractors are real engineering mistakes (fine-tune for a knowledge gap, leaky splits, unreviewed rows, "loss went down so we shipped"). Code in the browser is explained in plain English and runs without a GPU. The Colab notebook is the place a T4 actually trains.
If you came here hoping the 1.5B model becomes a generally smarter teammate, stop and go back to prompting and retrieval. If you have a named job, a reviewer, and a T4 hour (or a fixture you will not lie about), continue.
Inspiration, then our bar
Sai Rahul (@sairahul1) published a widely circulated X article, How To Fine-Tune a Small LLM on Your Own Data (Full Guide). The piece is worth reading on your own time. This chapter does not paste it, does not walk his notebook cell-for-cell, and does not treat a blog as the unlock.
PromptDojo keeps the thesis — small model, your data, one specialist — and grades the parts that usually get skipped: the prompt/RAG/FT gate, the job sentence, human review, company-level splits, and an eval receipt that can fail. You already practiced the first two forks in the context and retrieval chapter. This chapter is the third fork with a shippable artifact.
The academy bar for the rest of the path still applies. Multiple-choice distractors are real engineering mistakes (fine-tune for a knowledge gap, leaky splits, unreviewed rows, "loss went down so we shipped"). Code in the browser is explained in plain English and runs without a GPU. The Colab notebook is the place a T4 actually trains.
If you came here hoping the 1.5B model becomes a generally smarter teammate, stop and go back to prompting and retrieval. If you have a named job, a reviewer, and a T4 hour (or a fixture you will not lie about), continue.
Inspiration, then our bar
Sai Rahul (@sairahul1) published a widely circulated X article, How To Fine-Tune a Small LLM on Your Own Data (Full Guide). The piece is worth reading on your own time. This chapter does not paste it, does not walk his notebook cell-for-cell, and does not treat a blog as the unlock.
PromptDojo keeps the thesis — small model, your data, one specialist — and grades the parts that usually get skipped: the prompt/RAG/FT gate, the job sentence, human review, company-level splits, and an eval receipt that can fail. You already practiced the first two forks in the context and retrieval chapter. This chapter is the third fork with a shippable artifact.
The academy bar for the rest of the path still applies. Multiple-choice distractors are real engineering mistakes (fine-tune for a knowledge gap, leaky splits, unreviewed rows, "loss went down so we shipped"). Code in the browser is explained in plain English and runs without a GPU. The Colab notebook is the place a T4 actually trains.
If you came here hoping the 1.5B model becomes a generally smarter teammate, stop and go back to prompting and retrieval. If you have a named job, a reviewer, and a T4 hour (or a fixture you will not lie about), continue.
Inspiration, then our bar
Sai Rahul (@sairahul1) published a widely circulated X article, How To Fine-Tune a Small LLM on Your Own Data (Full Guide). The piece is worth reading on your own time. This chapter does not paste it, does not walk his notebook cell-for-cell, and does not treat a blog as the unlock.
PromptDojo keeps the thesis — small model, your data, one specialist — and grades the parts that usually get skipped: the prompt/RAG/FT gate, the job sentence, human review, company-level splits, and an eval receipt that can fail. You already practiced the first two forks in chapter 10. This chapter is the third fork with a shippable artifact.
The academy bar for the rest of the path still applies. Multiple-choice distractors are real engineering mistakes (fine-tune for a knowledge gap, leaky splits, unreviewed rows, "loss went down so we shipped"). Code in the browser is explained in plain English and runs without a GPU. The Colab notebook is the place a T4 actually trains.
If you came here hoping the 1.5B model becomes a generally smarter teammate, stop and go back to prompting and retrieval. If you have a named job, a reviewer, and a T4 hour (or a fixture you will not lie about), continue.