datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Multi-turn_Long-context_Benchmark_for_LLMs
LoopServe: An Adaptive Dual-phase LLM Inference Acceleration System for Multi-Turn Dialogues
Arxiv: https://www.arxiv.org/abs/2507.13681
Huggingface: https://huggingface.co/papers/2507.13681
Introduction
LoopServe Multi-Turn Dialogue Benchmark is a comprehensive evaluation dataset comprising multiple diverse datasets designed to assess large language model performance in realistic conversational scenarios.
Unlike traditional benchmarks that place queries only at the end… See the full description on the dataset page: https://huggingface.co/datasets/TreeAILab/Multi-turn_Long-context_Benchmark_for_LLMs.glm-4.7-multiturn-CoT
glm-4.7-multiturn-CoT
Dataset Summary
glm-4.7-multiturn-CoT is a ShareGPT-style multi-turn reasoning distillation dataset generated with GLM-4.7 as the teacher model.
This release focuses on preserving multi-turn dialogue continuity while injecting explicit chain-of-thought style responses in assistant turns.
Key Features
Multi-turn conversation format (human / gpt)
Assistant responses stored as <think>...</think> + final answer
Resume-safe distillation… See the full description on the dataset page: https://huggingface.co/datasets/Jackrong/glm-4.7-multiturn-CoT.thai-qa-multiturn-answer-dataset
Thai QA Multiturns Answer Synthesis Dataset
Rows: 11,992 rows (Cleaned)
Generated by Kobkrit Viriyayudhakorn (kobkrit@iapp.co.th)
Examples
{"instruction": "[{\"human\": \"หวัดดี มีเรื่องอยากสอบถามเกี่ยวกับวิวัฒนาการของมนุษย์\"}]", "output": "สวัสดีค่ะ ยินดีเลยค่ะ มีคำถามอะไร ถามได้่เลยนะคะ"}
{"instruction": "[{\"human\": \"หวัดดี มีเรื่องอยากสอบถามเกี่ยวกับวิวัฒนาการของมนุษย์\"}, {\"assistant\": \"สวัสดีค่ะ ยินดีเลยค่ะ มีคำถามอะไร ถามได้่เลยนะคะ\"}, {\"human\":… See the full description on the dataset page: https://huggingface.co/datasets/openthaigpt/thai-qa-multiturn-answer-dataset.HuatuoGPT_sft_data_v1_multiturn
describe
process FreedomIntelligence/HuatuoGPT-sft-data-v1 to multiturn format
{
"instruction": "听起来很不错。人工智能可能在哪些方面面临挑战呢？",
"input": "",
"output": "人工智能面临的挑战包括数据隐私、安全和道德方面的问题，以及影响就业机会的自动化等问题。",
"history": [
["你好，你能帮我解答一个问题吗？", "当然，请问有什么问题？"],
["我想了解人工智能的未来发展方向，你有什么想法吗？", "人工智能在未来的发展方向可能包括更强大的机器学习算法，更先进的自然语言处理技术，以及更加智能的机器人。"]
]
}
which can be used at LLaMA-Efficient-Tuning
example
tiny-multiturn-chat-koC-Language-Chat-Debug-Multiturn-Zh约 1300 条 C 语言 场景的 user - assistant 多轮对话。每段对话已经组织成了单行的格式。一条样例如下：
{
"id": 1045,
"conversation": [
{
"user": "你好，AI助手。我最近在写一个C语言程序，但是遇到了一些问题，希望你能帮我检查一下。",
"assistant": "你好，我很乐意帮助你。请把你的代码发给我，我会尽快检查并给出建议。"
},
{
"user": "好的，这是我的代码。这段代码的主要功能是计算斐波那契数列的前n项。",
"assistant": "让我看一下......嗯，这里有一个小错误。在第10行，你应该使用`++i`而不是`i++`来递增i的值。修改后的代码应该是这样的\\n```c\\nfor (int i = 0; i < n; ++i) {\\n if (i == 0 || i == 1) {\\n… See the full description on the dataset page: https://huggingface.co/datasets/Mxode/C-Language-Chat-Debug-Multiturn-Zh.