Alignment dataset tracked in the /datasets sub-catalog.
How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources
Original dataset page from ehartford.
348,020 entries. Sourced from open-instruct-uncensored.jsonl. Uses only these dataset subsets;
Flan V2
CoT
Dolly
OASST1
GPT4-Alpaca
Code-Alpaca
ShareGPT
Count of each Dataset:
code_alpaca: 19991
oasst1: 49433
flan_v2: 97519
sharegpt: 46733
dolly: 14624
cot: 73946
gpt4_alpaca: 45774
language:
How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources
Original dataset page from ehartford.
348,020 entries. Sourced from open-instruct-uncensored.jsonl. Uses only these dataset subsets;
Count of each Dataset:
code_alpaca: 19991
oasst1: 49433
flan_v2: 97519
sharegpt: 46733
dolly: 14624
cot: 73946
gpt4_alpaca: 45774
Every night we snapshot the README of every dataset in the catalog. When the SHA changes we archive the new version and diff it against the last. This is the evolving thought record of the alignment-data field: what the author decided to say about the corpus, and how that framing shifted over time.
No abliterated models in our catalog have declared this dataset in their YAML metadata yet. This can mean the dataset is used but not declared, is used outside abliteration workflows, or is new. The link will populate automatically as models are indexed.
Copy any of these snippets into your notebook or terminal. All three fetch directly from Hugging Face using your own credentials.
from datasets import load_dataset
ds = load_dataset("xzuyn/tulu-uncensored")from huggingface_hub import snapshot_download
snapshot_download(repo_id="xzuyn/tulu-uncensored", repo_type="dataset")git clone https://huggingface.co/datasets/xzuyn/tulu-uncensoredEvery model page has an "Open in Abliteration" button that hands the model directly to the first-party desktop client, at the quantization your rig can actually run. No API keys, no subscription, no prompt leakage.