datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Nemotron-SFT-Instruction-Following-Chat-v3
Dataset Description:
The Nemotron-Instruction-Following-Chat-v3 dataset is designed to strengthen multi-turn, interactive capabilities, including open-ended chat and precise instruction following.
The chat subset uses human written prompts from sources like lmarena, lmsys, and wildchat as seed prompts. Responses are generated with GLM-5. Multiple responses are sampled from the model and the best response as judged by pairwise comparisons using Qwen3-Nemotron-235B-A22B-GenRM-2603… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-SFT-Instruction-Following-Chat-v3.Nemotron-SFT-Instruction-Following-Chat-v2
Dataset Description:
The Nemotron-Instruction-Following-Chat-v2 dataset is designed to broadly strengthen the model’s interactive capabilities, including open-ended chat and precise instruction following.The dataset is a refreshed version of Nemotron-Instruction-Following-Chat-v1 with synthetic dialogues generated from Kimi-K2-Thinking, GLM-4.6, Qwen3-235B-A22B-Thinking-2507, GPT-OSS-120b, Kimi-K2-Instruct-0905, and Qwen3-235B-A22B-Instruct-2507.
This dataset is ready for commercial… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-SFT-Instruction-Following-Chat-v2.InternVL-Chat-V1-2-SFT-Data
Data Card for InternVL-Chat-V1-2-SFT-Data
Overview
Inspired by LLaVA-NeXT, we adopted a data-efficient SFT strategy to train InternVL-Chat-V1-2, utilizing approximately 1.2M of visual instruction tuning samples in total, all of which are fully open-source. In a macro sense, we build upon ShareGPT-4V and additionally integrate LLaVA-ZH, DVQA, ChartQA, AI2D, DocVQA, GeoQA+, and SynthDoG-EN. Most of the data remains consistent with LLaVA-NeXT.
Citation
If you use… See the full description on the dataset page: https://huggingface.co/datasets/OpenGVLab/InternVL-Chat-V1-2-SFT-Data.agent-sft-stitch-zh-tts-taste-codec-chat-sample
Gemma 4 E2B Taste-S multi-turn codec SFT
This dataset contains 37,362 complete Traditional Chinese agent
dialogues selected from voidful/agent-sft-stitch-zh-tts. It covers
229,434 synthesized speech segments, approximately
520.5 hours of audio before codec extraction.
Every assistant speech segment is represented without Gemma native audio tags:
<SAY> text_token <a_code> <b_code> ... <p_code> ... </SAY>
The first assistant output starts immediately with <SAY>.
[SOPR]...[EOPR]… See the full description on the dataset page: https://huggingface.co/datasets/voidful/agent-sft-stitch-zh-tts-taste-codec-chat-sample.InternVL_Chat_V12_SFT_Datafundusnap-fundustalk-v1-chatsft-11k
📢 Domain & Email Migration Notice
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📈 Status Pages:… See the full description on the dataset page: https://huggingface.co/datasets/fundusnap/fundusnap-fundustalk-v1-chatsft-11k.details_robinsmits__Qwen1.5-7B-Dutch-Chat-Sft-Bf16
Dataset Card for Evaluation run of robinsmits/Qwen1.5-7B-Dutch-Chat-Sft-Bf16
Dataset automatically created during the evaluation run of model robinsmits/Qwen1.5-7B-Dutch-Chat-Sft-Bf16 on the Open LLM Leaderboard.
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train"… See the full description on the dataset page: https://huggingface.co/datasets/open-llm-leaderboard-old/details_robinsmits__Qwen1.5-7B-Dutch-Chat-Sft-Bf16.sft-safe-openai-chat-10k
SFT Safe OpenAI Chat 10K
This dataset is formatted for chat SFT training. Each JSONL row contains a messages field compatible with OpenAI-style chat fine-tuning data:
{"messages":[{"role":"system","content":"..."},{"role":"user","content":"..."},{"role":"assistant","content":"..."}]}
Files:
train.jsonl: 10,000 training examples
validation.jsonl: 200 validation examples
eval.jsonl: same content as validation.jsonl, provided as an evaluation alias
Example usage:
fromdatasets import… See the full description on the dataset page: https://huggingface.co/datasets/yxx123456/sft-safe-openai-chat-10k.multi-turn-chat-sft-50k
Multi-Turn Chat SFT (50K ShareGPT Format)
50,000 multi-turn conversations in ShareGPT format for supervised fine-tuning of chat models.
Format
Standard ShareGPT format — drop-in compatible with LLaMA-Factory, Axolotl, and Unsloth:
{
"conversations": [
{"from": "system", "value": "You are a helpful assistant."},
{"from": "human", "value": "Write a Python function to implement binary search."},
{"from": "gpt", "value": "Here's a clean… See the full description on the dataset page: https://huggingface.co/datasets/stindardlogic/multi-turn-chat-sft-50k.Nemotron-SFT-Instruction-Following-Chat-v2-prompt-only
Nemotron-SFT-Instruction-Following-Chat-v2-prompt-only
Prompt-only extraction from nvidia/Nemotron-SFT-Instruction-Following-Chat-v2.
Files:
prompts.csv: one prompt extraction record per source row. Records include
prompt, separated system_prompt, and structured tools when the source row
defines available tools. Nested values are JSON-encoded inside CSV cells.
summary.md: source row counts, extracted row counts, count deltas, and failed prompt counts.
null_or_empty_rows.md: row… See the full description on the dataset page: https://huggingface.co/datasets/jamesdborin/Nemotron-SFT-Instruction-Following-Chat-v2-prompt-only.olmo-code-sft-chatnanochat-d24-sft-chat-eval-v1
nanochat d24 SFT Chat Eval Capture
Per-example outputs for the nanochat chat eval tasks across dense, nested, and MatFormer SFT models.
Each dataset config corresponds to one model. Each split corresponds to one eval task.
Important columns include input_prompt, rendered_prompt, model_response, correct,
task_logical_index, example_key, and forward-inference FLOP estimates split into
flops_prefill, flops_decode, and flops_total.
Dataset repo: atrost/nanochat-d24-sft-chat-eval-v1… See the full description on the dataset page: https://huggingface.co/datasets/atrost/nanochat-d24-sft-chat-eval-v1.details_alexredna__Tukan-1.1B-Chat-reasoning-sft-COLA
Dataset Card for Evaluation run of alexredna/Tukan-1.1B-Chat-reasoning-sft-COLA
Dataset automatically created during the evaluation run of model alexredna/Tukan-1.1B-Chat-reasoning-sft-COLA on the Open LLM Leaderboard.
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The… See the full description on the dataset page: https://huggingface.co/datasets/open-llm-leaderboard-old/details_alexredna__Tukan-1.1B-Chat-reasoning-sft-COLA.LogicMind-Chat-Reasoning-SFT-300K
Nemotron-Post-Training-Dataset-v2-chat Dataset Card
Overview 📌
This dataset contains 296,168 chat-style instruction/response samples generated by qwen-3-32b. Each record provides a user prompt, an explicit reasoning trace, and a final answer, plus precomputed length fields. The data is packaged as JSONL (one JSON object per line).
Highlights
Scale: 296,168 samples
Category: chat (100%)
Generator: qwen-3-32b (100%)
Structure: problem → qwen3-reasoning → qwen3-solution… See the full description on the dataset page: https://huggingface.co/datasets/Jackrong/LogicMind-Chat-Reasoning-SFT-300K.fairleap-driver-chat-sft-43k
Fairleap Driver Chat SFT 43k
📘 Dataset Overview
42,743 synthetic Indonesian conversations between a Gojek/GOTO driver and an assistant,
built for the Fairleap AI project — a platform addressing
income uncertainty and wellbeing for ride-hailing drivers in Indonesia. Each record is one
complete chat: a stuffed system prompt, the driver's questions, and the assistant's replies,
sometimes with a tool call or an injected domain-knowledge block in between.
It exists because… See the full description on the dataset page: https://huggingface.co/datasets/fairleap-ai/fairleap-driver-chat-sft-43k.ling-lang-chat-sft
Ling-Lang Chat SFT Dataset
Instruction-tuning data (chat/messages JSONL) for fine-tuning LLMs to help with the
Ling programming language — a multilingual systems
programming language with keyword aliases in 15 human languages, a Cranelift
JIT/AOT + tree-walking interpreter + bytecode VM, and a ling-* crate ecosystem.
Used to fine-tune zai-org/GLM-4-32B-0414
via QLoRA for chat.ling-lang.org.
Categories
File
Topic
01_syntax.jsonl
Core ling-lang syntax… See the full description on the dataset page: https://huggingface.co/datasets/taellinglin/ling-lang-chat-sft.details_abhinand__TinyLlama-1.1B-OpenHermes-2.5-Chat-v0.1-sft
Dataset Card for Evaluation run of abhinand/TinyLlama-1.1B-OpenHermes-2.5-Chat-v0.1-sft
Dataset automatically created during the evaluation run of model abhinand/TinyLlama-1.1B-OpenHermes-2.5-Chat-v0.1-sft on the Open LLM Leaderboard.
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of… See the full description on the dataset page: https://huggingface.co/datasets/open-llm-leaderboard-old/details_abhinand__TinyLlama-1.1B-OpenHermes-2.5-Chat-v0.1-sft.Nemotron-SFT-Instruction-Following-Chat-v3-prompt-only
Nemotron-SFT-Instruction-Following-Chat-v3-prompt-only
Prompt-only extraction from nvidia/Nemotron-SFT-Instruction-Following-Chat-v3.
Files:
prompts.csv: one prompt extraction record per source row. Records include
prompt, separated system_prompt, and structured tools when the source row
defines available tools. Nested values are JSON-encoded inside CSV cells.
summary.md: source row counts, extracted row counts, count deltas, and failed prompt counts.
null_or_empty_rows.md: row… See the full description on the dataset page: https://huggingface.co/datasets/jamesdborin/Nemotron-SFT-Instruction-Following-Chat-v3-prompt-only.bharat-v3-chat-sft-v3llama3_additional_rr40k_non_delete_sft_chat_formatbharat-v3-chat-sft-v2Nemotron-SFT-Instruction-Following-Chat-v2
Dataset Description:
The Nemotron-Instruction-Following-Chat-v2 dataset is designed to broadly strengthen the model’s interactive capabilities, including open-ended chat and precise instruction following.The dataset is a refreshed version of Nemotron-Instruction-Following-Chat-v1 with synthetic dialogues generated from Kimi-K2-Thinking, GLM-4.6, Qwen3-235B-A22B-Thinking-2507, GPT-OSS-120b, Kimi-K2-Instruct-0905, and Qwen3-235B-A22B-Instruct-2507.
This dataset is ready for… See the full description on the dataset page: https://huggingface.co/datasets/OperatorSDG/Nemotron-SFT-Instruction-Following-Chat-v2.JF_nvidia_Nemotron-SFT-Instruction-Following-Chat-v2_reasoning_offbharat-v3-chat-sftopc-sft-stage2-chatlemonseed-chat-sft
lemonseed-chat-sft
LemonSeed — chat SFT (multi-turn conversations).
Contents
chat_sft.jsonl (1502 rows)
Format
JSON Lines (.jsonl), one example per line.
Provenance
Synthetic, generated programmatically for the LemonSeed 1.5B project (by Geramy L. Loveless). Data authored by Michael Anthony Falabella.
details_bartowski__internlm2-chat-7b-sft-llama
Dataset Card for Evaluation run of bartowski/internlm2-chat-7b-sft-llama
Dataset automatically created during the evaluation run of model bartowski/internlm2-chat-7b-sft-llama on the Open LLM Leaderboard.
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is… See the full description on the dataset page: https://huggingface.co/datasets/open-llm-leaderboard-old/details_bartowski__internlm2-chat-7b-sft-llama.science_chat_safety-sftlegal-chat-sft-dataset
Thai Legal Chat SFT Dataset (CoT & Hybrid RAG)
ชุดข้อมูลสำหรับการทำ Instruction Fine-Tuning (SFT) เพื่อสร้าง AI ผู้ช่วยนักกฎหมายไทยที่มีความสามารถในการคิดวิเคราะห์แบบเป็นขั้นตอน (Chain-of-Thought) และมีความรู้กฎหมายที่ทันสมัยจากการใช้ Hybrid RAG (Retrieval-Augmented Generation)
Dataset Summary
ชุดข้อมูลนี้ถูกสร้างขึ้นแบบสังเคราะห์ (Synthetic Data) โดยใช้โมเดลภาษาขนาดใหญ่ (LLM) ตระกูล Qwen (27B+) บนขุมพลัง AMD MI300X ผ่านระบบ vLLM Monster Engine… See the full description on the dataset page: https://huggingface.co/datasets/Phonsiri/legal-chat-sft-dataset.lemonseed-chat-sft-mix
lemonseed-chat-sft-mix
LemonSeed — chat SFT mixed with prose anchor.
Contents
chat_sft_mix.jsonl (2002 rows)
Format
JSON Lines (.jsonl), one example per line.
Provenance
Synthetic, generated programmatically for the LemonSeed 1.5B project (by Geramy L. Loveless). Data authored by Michael Anthony Falabella.