rex099/fable-5-traces-messages

Fable 5 Traces — messages format

A conversion of Glint-Research/Fable-5-traces (fable5_cot_merged.jsonl) into the standard HF/OpenAI conversational messages format, ready to drop into Unsloth or TRL SFTTrainer notebooks with no extra conversion step.

Format

Each row:

{
  "messages": [
    {"role": "user", "content": "<flattened prior transcript / context>"},
    {"role": "assistant", "content": "<think>...cot...</think>\nASSISTANT (tool call) Read input={...}"}
  ],
  "uid": "source session id#index",
  "session": "source session id",
  "source_file": "original raw trace path",
  "model": "claude-fable-5",
  "output_type": "tool_use | text",
  "origin": "local | hf"
}

The assistant turn is the original completion field from the source dataset: a <think>...</think> reasoning block (from cot) followed by either a tool call (ASSISTANT (tool call) <Tool> input={...}) or a plain assistant message (ASSISTANT (message): ...).

Loading with Unsloth / TRL

from datasets import load_dataset

dataset = load_dataset("rex099/fable-5-traces-messages", split="train")
# dataset[0]["messages"] -> [{"role": "user", ...}, {"role": "assistant", ...}]

# TRL's SFTTrainer auto-detects the "messages" column and applies the
# tokenizer's chat template automatically:
from trl import SFTTrainer, SFTConfig
trainer = SFTTrainer(
    model=model,
    tokenizer=tokenizer,
    train_dataset=dataset,
    args=SFTConfig(dataset_text_field=None, ...),
)

Provenance

Derived entirely from the merged training rows of Glint-Research/Fable-5-traces (fable5_cot_merged.jsonl), reshaped 1:1 into messages — no content was altered. See the source dataset card for full details on collection, fields, and caveats (license: AGPL-3.0, coding-agent telemetry — not sanitized benchmark data).