datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
alpaca-cleaned
Dataset Card for Alpaca-Cleaned
Forked from https://huggingface.co/datasets/yahma/alpaca-cleaned
Repository: https://github.com/gururise/AlpacaDataCleaned
Dataset Description
This is a cleaned version of the original Alpaca Dataset released by Stanford. The following issues have been identified in the original release and fixed in this dataset:
Hallucinations: Many instructions in the original dataset had instructions referencing data on the internet, which just caused… See the full description on the dataset page: https://huggingface.co/datasets/unsloth/alpaca-cleaned.receipted-unsloth
Receipted Unsloth
How SZL Holdings actually trains. Silhouette from Unsloth QLoRA. Cut is original SZL. We do not republish Unsloth Studio, Desktop, copy, code, or someone else's tensors.
Collection: Receipted Unsloth — LIVE
The house loop
Disclose the Apache base (Qwen/Qwen2.5-* or Qwen/Qwen3.5-0.8B).
Train with Unsloth FastLanguageModel QLoRA on owner metal or HF Jobs (uv run + HF_TOKEN).
Bind dataset SHA-256, LoRA knobs, seed, and loss into a training receipt.… See the full description on the dataset page: https://huggingface.co/datasets/SZLHOLDINGS/receipted-unsloth.Hermes-OmniForge-Qwen36-27B-full-v0.3.0-unsloth
Hermes OmniForge Qwen3.6-27B Dataset v0.3.0
This package contains the Hermes OmniForge Qwen3.6-27B v0.3.0 synthetic SFT dataset and Unsloth-ready exports.
data/final/train.jsonl
data/final/validation.jsonl
data/final/test.jsonl
data/final/*_unsloth_text.jsonl
data/final/*_unsloth_vision.jsonl
scripts/export_unsloth.py
scripts/validate_dataset.py
scripts/train_unsloth_text_example.py
scripts/train_unsloth_vision_example.py
reports/dataset_report.json
Dataset Shape… See the full description on the dataset page: https://huggingface.co/datasets/r0b0tlab/Hermes-OmniForge-Qwen36-27B-full-v0.3.0-unsloth.ogiri-bokete-unsloth-vlm
Japanese Bokete Ogiri — Unsloth VLM format
YANS-official/ogiri-bokete を、UnslothのVision SFTで扱える会話形式に変換した非公開用データセットです。
各JSONLレコードは「1画像 + 1回答」です。
{
"messages": [
{"role": "user", "content": [
{"type": "image", "image": "images/124469.jpg"},
{"type": "text", "text": "この画像のお題に対して、面白い一言を1つ返してください。"}
]},
{"role": "assistant", "content": [
{"type": "text", "text": "..."}
]}
]
}
Files
train.jsonl: 1,678 records / 630 prompts… See the full description on the dataset page: https://huggingface.co/datasets/beezza/ogiri-bokete-unsloth-vlm.dockerNLcommands-sft-unsloth
Docker NL Commands (Unsloth-ready)
Converted from dockerNLcommands-sft-jsonl for Unsloth Studio.
Configurations
Config
Format
Columns
Train rows
Test rows
alpaca (default)
Alpaca
instruction, input, output
2294
121
chatml
ChatML
messages (with role + content)
2294
121
Usage
Unsloth Studio
Dataset → Hugging Face → lakhera2023/dockerNLcommands-sft-unsloth
Format: alpaca (default config)
Train split: train, eval split: test… See the full description on the dataset page: https://huggingface.co/datasets/lakhera2023/dockerNLcommands-sft-unsloth.