Get the app
extract 0 0 0

lmorlok/hh-rlhf-llama-3-8b-embedding

published by @lmorlok

Alignment dataset tracked in the /datasets sub-catalog.

Lifetime downloads
611
Last 30 days
139
Likes
-
Size
8.1 GB
Created on HF
2024-11-06
Age
1 years ago

Description

Description

This dataset contains the last-layer-embeddings of the hh-rlhf dataset. The model that we used for embedding is the llama-3-8b pretrained LLM, therefore, the dimension of the embeddings is 4096.

Tags

size_categories:100K<n<1Mformat:parquetlibrary:datasetslibrary:dasklibrary:mlcroissantlibrary:polarsregion:us

README current version from Hugging Face

dataset_info:
features:

  • name: chosen
    sequence: float64
  • name: rejected
    sequence: float64
    splits:
  • name: train
    num_bytes: 10539475200
    num_examples: 160800
  • name: test
    num_bytes: 560532288
    num_examples: 8552
    download_size: 8645907100
    dataset_size: 11100007488
    configs:
  • config_name: default
    data_files:
    • split: train
      path: data/train-*
    • split: test
      path: data/test-*

Description

This dataset contains the last-layer-embeddings of the hh-rlhf dataset. The model that we used for embedding is the llama-3-8b pretrained LLM, therefore, the dimension of the embeddings is 4096.

README history 5 revisions

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.

  1. 2024-11-08Update README.md818f882645 B
    Loading...
  2. 2024-11-08Update README.mdf54fcc9593 B
    Loading...
  3. 2024-11-07Upload data69ee369435 B
    Loading...
  4. 2024-11-06Upload data13a5d8c410 B
    Loading...
  5. 2024-11-06initial commite4f81340 B
    Loading...

Used by abliterated models none yet

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.

Community discussions 1 thread · 1 open · 1 comments

Threads posted by the community on Hugging Face: bug reports, questions about usage, pull requests on the README, license clarifications. Click any thread to expand and read the full conversation inline. Newest first.

  1. 2024-11-07[bot] Conversion to Parquetopen1 💬#1
    Loading...

Continue on Hugging Face →

Related datasets same stage · extract

Anthropic/hh-rlhf
2.1M dl 2442 likes 90.4 MB
Dataset Card for HH-RLHF Dataset Summary This repository provides access to two different kinds of data: Human ...
djain95/sae-jailbreaks-cache
468.6K dl 1 likes 30.6 GB
mosaicml/dolly_hhrlhf
309.5K dl 113 likes 23.7 MB
Dataset Card for "dolly_hhrlhf" This dataset is a combination of Databrick's dolly-15k dataset and a filtered subset of Anthrop...
Dahoas/full-hh-rlhf
266.3K dl 104 likes 129.8 MB
Dataset Card for "full-hh-rlhf" Anthropic's HH dataset reformatted into prompt, chosen, rejected samples.
mlabonne/harmful_behaviors
198.2K dl 169 likes 23 KB
mlabonne/harmless_alpaca
190.2K dl 51 likes 1.2 MB
walledai/AdvBench
176.3K dl 123 likes 38 KB
Dataset Card for AdvBench Paper: Universal and Transferable Adversarial Attacks on Aligned Language Models Data: AdvBench Datas...
JailbreakV-28K/JailBreakV-28k
116.4K dl 80 likes 288.8 MB
⛓‍💥 JailBreakV-28K: A Benchmark for Assessing the Robustness of MultiModal Large Language Models against Jailbreak Attacks 🌐 ...

Read further

Community best practices (extraction)
5 rules the field converged on for building the pair.
Building your own extraction pair
When the reference pair is not enough. Sizing, framing, and evaluation.
Datasets that shaped abliteration
The five corpora that did most of the work.
Non-English abliteration communities
How Chinese, Russian, Japanese, and Korean practitioners build extraction pairs.

Repository

DIR data 24 files FILE .gitattributes FILE README.md

File contents live on Hugging Face. Click a file or folder to open it there.

Download the dataset

0

Copy any of these snippets into your notebook or terminal. All three fetch directly from Hugging Face using your own credentials.

Recommended: datasets library (Python) 
from datasets import load_dataset
ds = load_dataset("lmorlok/hh-rlhf-llama-3-8b-embedding")
Raw snapshot (Python) 
from huggingface_hub import snapshot_download
snapshot_download(repo_id="lmorlok/hh-rlhf-llama-3-8b-embedding", repo_type="dataset")
git clone (requires git-lfs) 
git clone https://huggingface.co/datasets/lmorlok/hh-rlhf-llama-3-8b-embedding
Browse repository on Hugging Face ->
Catalog is the map. Apps are the tools.

Run models on your own machine, not in the cloud.

Every 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.

Get the app Browse the catalog
Open in Abliteration