Prepare a JSONL dataset of instruction/query/response

To instruction‑tune your base LLM, you need training data that shows how you want your assistant to behave. The most common format is a JSON Lines file (.jsonl):

• Each line = one training example

• Each example = instruction, input (optional), ideal response

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✅ What Should Each Example Look Like?

Each entry should answer:

• What does the user want? (instruction)

• Any extra input? (like a text passage)

• What’s the best possible reply? (response)

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📌 Minimal Example

{"instruction": "Explain what a Python function is.", "input": "", "output": "A Python function is a reusable block of code that performs a specific task. It can accept inputs, called arguments, and can return a value."}

{"instruction": "Summarize this paragraph.", "input": "Python is a popular programming language known for its readability and community support.", "output": "Python is a user-friendly programming language with strong community backing."}

{"instruction": "Give a beginner-friendly example of a for loop in Python.", "input": "", "output": "Here’s an example:\n\n```\nfor i in range(5):\n    print(i)\n```\nThis prints numbers from 0 to 4."}

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✅ Recommended Structure

Field	Description
instruction	The task or question you want the model to handle.
input	Optional supporting text, context, or passage.
output	The ideal response your assistant should generate.

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✅ Tips for Writing Good Examples

✔️ Make them clear, realistic, and polite. ✔️ Cover typical tasks your assistant should handle. ✔️ Include edge cases: politely refuse harmful or off-topic requests. ✔️ Keep outputs short and direct if your assistant should be concise.

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✅ How Many Examples Do I Need?

• For quick demos: 10–50 examples can show basic behavior.

• For real improvement: 500–5,000+ examples are better.

• Many open datasets (like Alpaca, Dolly, OpenAssistant) use 10,000+ instructions.

Start small, test, then scale up.

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✅ Save Your Dataset

1. Put your examples in a text file, one JSON per line. Example filename: my_instructions.jsonl

2. Test that it loads:

   from datasets import load_dataset
   
   dataset = load_dataset("json", data_files="my_instructions.jsonl")
   print(dataset)

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⚙️ Where to Get More Data

• Write your own instructions.

• Use public instruction datasets: ➜ tatsu-lab/alpaca ➜ databricks/databricks-dolly-15k ➜ OpenAssistant/oasst1

• Combine and adapt them for your assistant’s style.

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✅ Key Takeaway

A good dataset = clear instructions ➜ clear outputs. This is what teaches your model how to respond politely, helpfully, and on topic.

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➡️ Next: You’ll learn how to fine-tune your model using this dataset with transformers and accelerate!