sriq-sft

ShareGPT-compatible supervised fine-tuning conversations. Questions are generated in many languages. Assistant targets contain compressed Simplified Chinese reasoning and the original final answer. The final answer is not rewritten by the compression step.

• conversations: a human turn followed by one GPT turn.
• Human turn: from is human; value contains the question.
• GPT turn: from is gpt; value contains <think>Chinese reasoning</think> followed by the final answer.
• Extra columns: topic, difficulty, language (the requested question language).

There are no rejected responses in the SFT export.

Token statistics

Counts use tiktoken cl100k_base, not the training model's tokenizer.

Slice	Total tokens
Reasoning	725,302
Final answer	3,129,384
Reasoning + final answer, including think tags	3,868,471

Rows: 2703. Languages: Arabic, Bengali, Czech, Dutch, English, French, German, Greek, Hebrew, Hindi, Indonesian, Italian, Japanese, Korean, Persian, Polish, Portuguese, Romanian, Russian, Simplified Chinese, Spanish, Swahili, Swedish, Tamil, Thai, Traditional Chinese, Turkish, Ukrainian, Urdu, Vietnamese.

Load

from datasets import load_dataset
dataset = load_dataset("sriq-ai/sriq-sft", split="train")
conversation = dataset[0]["conversations"]

Format for Qwen3.8 training

The export uses standard ShareGPT fields. Qwen3.8's native template expects reasoning in a separate field, so convert the record before applying that template:

from transformers import AutoTokenizer

question = conversation[0]["value"]
response = conversation[1]["value"]
reasoning, answer = response.removeprefix("<think>").split("</think>", 1)
messages = [
    {"role": "user", "content": question},
    {"role": "assistant", "reasoning_content": reasoning, "content": answer.strip()},
]
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3.8-27B")
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=False,
    preserve_thinking=True,
)

For other models, adapt the ShareGPT roles/fields to the trainer's expected conversation schema while retaining the complete GPT value. Do not place the same reasoning in both content and reasoning_content for Qwen's template.

For SFT, include the assistant's reasoning and final answer in the loss and mask user/prompt tokens. Check that the rendered training text retains the Chinese reasoning and that the assistant loss mask covers it. Use the target tokenizer to check lengths and keep repeated questions in the same train/evaluation split.