jsonl-format

name	jsonl-format
description	JSONL format guide for LLM fine-tuning. Covers OpenAI, Anthropic, and Llama formats, format validation rules, conversion between formats, and quality checklist.
triggers	["jsonl format","fine-tune format","openai training format","anthropic training format","llama format","jsonl conversion","training data format"]

JSONL Format Guide for LLM Fine-Tuning

What is JSONL

JSONL (JSON Lines) is a text format where each line is a valid JSON object. Fine-tuning datasets use JSONL because it is easy to stream line-by-line without loading the entire file into memory.

{"messages": [{"role": "user", "content": "Hello"}, {"role": "assistant", "content": "Hi!"}]}
{"messages": [{"role": "user", "content": "How are you?"}, {"role": "assistant", "content": "I am doing well."}]}

Rules:

• One JSON object per line
• No trailing commas
• Lines must not span multiple lines (no pretty-printed JSON)
• Empty lines are ignored
• UTF-8 encoding

OpenAI Format (ChatML)

Used for GPT-3.5-turbo, GPT-4, and any model that accepts the messages API.

{
  "messages": [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "What is the capital of France?"},
    {"role": "assistant", "content": "The capital of France is Paris."}
  ]
}

Rules:

• messages key required, must be an array
• Each message must have role and content
• Valid roles: system, user, assistant
• Array must end with an assistant message
• system message is optional but must appear first if present
• content must not be an empty string

Anthropic Format

Used for Claude fine-tuning via the Anthropic API.

{
  "prompt": "Human: What is the capital of France?\n\nAssistant:",
  "completion": " The capital of France is Paris."
}

Rules:

• prompt key required, must be a string
• completion key required, must be a string
• prompt should end with \n\nAssistant:
• completion conventionally starts with a space character
• Human turns use the prefix Human: or \n\nHuman:

Llama Format (Instruction-Following)

Used for Llama, Mistral, and similar instruction-tuned models.

{
  "instruction": "Translate the following sentence to French.",
  "input": "The weather is beautiful today.",
  "output": "Le temps est magnifique aujourd'hui."
}

Rules:

• instruction key required, non-empty string
• input key optional; use empty string if no context needed
• output key required, non-empty string
• Typically formatted with Alpaca template at training time: ### Instruction:\n{instruction}\n\n### Input:\n{input}\n\n### Response:\n{output}

Format Conversion

OpenAI to Anthropic

messages[0] (system) -> prepend to first Human turn
messages[1] (user)   -> "Human: {content}\n\nAssistant:"
messages[2] (asst)   -> " {content}"

Multi-turn:

"Human: {turn1}\n\nAssistant: {turn1_response}\n\nHuman: {turn2}\n\nAssistant:"

OpenAI to Llama

messages[0] (system) -> instruction
messages[1] (user)   -> input
messages[-1] (asst)  -> output

Note: multi-turn conversations lose context when converted to Llama single-turn format.

Anthropic to OpenAI

Split prompt on \n\nHuman: and \n\nAssistant: boundaries to reconstruct messages array.

Quality Checklist

Before submitting for training:

• Every line is valid JSON (no trailing commas, no syntax errors)
• Every sample matches the target format exactly
• No empty content or output fields
• Assistant turns are non-trivial (not just "OK" or "I see")
• No near-duplicate pairs (similarity > 0.8)
• Response length is appropriate (10-2000 chars for most tasks)
• Token count per sample is within model context window
• Dataset has diverse vocabulary (not the same phrasing repeated)
• Train/eval split created with fixed random seed for reproducibility
• No PII (names, emails, phone numbers) unless the dataset purpose requires it

Token Count Estimation

Quick estimation (not exact):

tokens ≈ len(text_in_chars) / 4

More accurate: use the model's tokenizer. For GPT models, tiktoken library; for Llama, transformers tokenizer.

Typical limits:

• GPT-3.5-turbo fine-tune: 4,096 tokens per sample
• GPT-4 fine-tune: 4,096 tokens per sample
• Claude: 200,000 context, training data varies
• Llama 3 8B: 8,192 context window