openai/automated-interpretability openai/automated-interpretability

  • Stars
    star
    896
  • Rank 50,968 (Top 2 %)
  • Language
    Python
  • Created over 1 year ago
  • Updated 9 months ago
Twitter logo Share LinkedIn logo Share Facebook logo Share
openai/automated-interpretability
github

openai

Reviews

There are no reviews yet. Be the first to send feedback to the community and the maintainers!

Repository Details

Automated interpretability

Code and tools

This repository contains code and tools associated with the Language models can explain neurons in language models paper, specifically:

  • Code for automatically generating, simulating, and scoring explanations of neuron behavior using the methodology described in the paper. See the neuron-explainer README for more information.

Note: if you run into errors of the form "Error: Could not find any credentials that grant access to storage account: 'openaipublic' and container: 'neuron-explainer'"." you might be able to fix this by signing up for an azure account and specifying the credentials as described in the error message.

  • A tool for viewing neuron activations and explanations, accessible here. See the neuron-viewer README for more information.

Public datasets

Together with this code, we're also releasing public datasets of GPT-2 XL neurons and explanations. Here's an overview of those datasets.

  • Neuron activations: az://openaipublic/neuron-explainer/data/collated-activations/{layer_index}/{neuron_index}.json
    • Tokenized text sequences and their activations for the neuron. We provide multiple sets of tokens and activations: top-activating ones, random samples from several quantiles; and a completely random sample. We also provide some basic statistics for the activations.
    • Each file contains a JSON-formatted NeuronRecord dataclass.
  • Neuron explanations: az://openaipublic/neuron-explainer/data/explanations/{layer_index}/{neuron_index}.jsonl
    • Scored model-generated explanations of the behavior of the neuron, including simulation results.
    • Each file contains a JSON-formatted NeuronSimulationResults dataclass.
  • Related neurons: az://openaipublic/neuron-explainer/data/related-neurons/weight-based/{layer_index}/{neuron_index}.json
    • Lists of the upstream and downstream neurons with the most positive and negative connections (see below for definition).
    • Each file contains a JSON-formatted dataclass whose definition is not included in this repo.
  • Tokens with high average activations: az://openaipublic/neuron-explainer/data/related-tokens/activation-based/{layer_index}/{neuron_index}.json
    • Lists of tokens with the highest average activations for individual neurons, and their average activations.
    • Each file contains a JSON-formatted TokenLookupTableSummaryOfNeuron dataclass.
  • Tokens with large inbound and outbound weights: az://openaipublic/neuron-explainer/data/related-tokens/weight-based/{layer_index}/{neuron_index}.json
    • List of the most-positive and most-negative input and output tokens for individual neurons, as well as the associated weight (see below for definition).
    • Each file contains a JSON-formatted WeightBasedSummaryOfNeuron dataclass.

Update (July 5, 2023) We also released a set of explanations for GPT-2 Small. The methodology is slightly different from the methodology used for GPT-2 XL so the results aren't directly comparable.

  • Neuron activations: az://openaipublic/neuron-explainer/gpt2_small_data/collated-activations/{layer_index}/{neuron_index}.json
  • Neuron explanations: az://openaipublic/neuron-explainer/gpt2_small_data/explanations/{layer_index}/{neuron_index}.jsonl

Definition of connection weights

Refer to GPT-2 model code for understanding of model weight conventions.

Neuron-neuron: For two neurons (l1, n1) and (l2, n2) with l1 < l2, the connection strength is defined as h{l1}.mlp.c_proj.w[:, n1, :] @ diag(h{l2}.ln_2.g) @ h{l2}.mlp.c_fc.w[:, :, n2].

Neuron-token: For token t and neuron (l, n), the input weight is computed as wte[t, :] @ diag(h{l}.ln_2.g) @ h{l}.mlp.c_fc.w[:, :, n] and the output weight is computed as h{l}.mlp.c_proj.w[:, n, :] @ diag(ln_f.g) @ wte[t, :].

Misc Lists of Interesting Neurons

Lists of neurons we thought were interesting according to different criteria, with some preliminary descriptions.

More Repositories

1

whisper

Robust Speech Recognition via Large-Scale Weak Supervision
Python
62,693
star
2

openai-cookbook

Examples and guides for using the OpenAI API
MDX
58,610
star
3

gym

A toolkit for developing and comparing reinforcement learning algorithms.
Python
34,442
star
4

CLIP

CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image
Jupyter Notebook
22,966
star
5

openai-python

The official Python library for the OpenAI API
Python
22,561
star
6

gpt-2

Code for the paper "Language Models are Unsupervised Multitask Learners"
Python
21,450
star
7

chatgpt-retrieval-plugin

The ChatGPT Retrieval Plugin lets you easily find personal or work documents by asking questions in natural language.
Python
21,032
star
8

baselines

OpenAI Baselines: high-quality implementations of reinforcement learning algorithms
Python
15,622
star
9

gpt-3

GPT-3: Language Models are Few-Shot Learners
15,573
star
10

swarm

Educational framework exploring ergonomic, lightweight multi-agent orchestration. Managed by OpenAI Solution team.
Python
14,944
star
11

evals

Evals is a framework for evaluating LLMs and LLM systems, and an open-source registry of benchmarks.
Python
14,607
star
12

tiktoken

tiktoken is a fast BPE tokeniser for use with OpenAI's models.
Python
11,374
star
13

triton

Development repository for the Triton language and compiler
C++
11,077
star
14

DALL-E

PyTorch package for the discrete VAE used for DALL·E.
Python
10,760
star
15

shap-e

Generate 3D objects conditioned on text or images
Python
10,285
star
16

spinningup

An educational resource to help anyone learn deep reinforcement learning.
Python
8,587
star
17

openai-node

The official Node.js / Typescript library for the OpenAI API
TypeScript
7,703
star
18

universe

Universe: a software platform for measuring and training an AI's general intelligence across the world's supply of games, websites and other applications.
Python
7,385
star
19

jukebox

Code for the paper "Jukebox: A Generative Model for Music"
Python
7,326
star
20

point-e

Point cloud diffusion for 3D model synthesis
Python
5,777
star
21

consistency_models

Official repo for consistency models.
Python
5,725
star
22

guided-diffusion

Python
5,000
star
23

plugins-quickstart

Get a ChatGPT plugin up and running in under 5 minutes!
Python
4,133
star
24

transformer-debugger

Python
4,003
star
25

retro

Retro Games in Gym
C
3,361
star
26

glide-text2im

GLIDE: a diffusion-based text-conditional image synthesis model
Python
3,277
star
27

glow

Code for reproducing results in "Glow: Generative Flow with Invertible 1x1 Convolutions"
Python
3,016
star
28

mujoco-py

MuJoCo is a physics engine for detailed, efficient rigid body simulations with contacts. mujoco-py allows using MuJoCo from Python 3.
Cython
2,586
star
29

openai-quickstart-node

Node.js example app from the OpenAI API quickstart tutorial
JavaScript
2,534
star
30

weak-to-strong

Python
2,445
star
31

improved-gan

Code for the paper "Improved Techniques for Training GANs"
Python
2,218
star
32

human-eval

Code for the paper "Evaluating Large Language Models Trained on Code"
Python
2,204
star
33

improved-diffusion

Release for Improved Denoising Diffusion Probabilistic Models
Python
2,102
star
34

roboschool

DEPRECATED: Open-source software for robot simulation, integrated with OpenAI Gym.
Python
2,064
star
35

image-gpt

Python
2,025
star
36

consistencydecoder

Consistency Distilled Diff VAE
Python
1,933
star
37

finetune-transformer-lm

Code and model for the paper "Improving Language Understanding by Generative Pre-Training"
Python
1,929
star
38

gpt-2-output-dataset

Dataset of GPT-2 outputs for research in detection, biases, and more
Python
1,908
star
39

multiagent-particle-envs

Code for a multi-agent particle environment used in the paper "Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments"
Python
1,871
star
40

pixel-cnn

Code for the paper "PixelCNN++: A PixelCNN Implementation with Discretized Logistic Mixture Likelihood and Other Modifications"
Python
1,856
star
41

openai-quickstart-python

Python example app from the OpenAI API quickstart tutorial
1,685
star
42

requests-for-research

A living collection of deep learning problems
HTML
1,625
star
43

multi-agent-emergence-environments

Environment generation code for the paper "Emergent Tool Use From Multi-Agent Autocurricula"
Python
1,590
star
44

gpt-discord-bot

Example Discord bot written in Python that uses the completions API to have conversations with the `text-davinci-003` model, and the moderations API to filter the messages.
Python
1,569
star
45

evolution-strategies-starter

Code for the paper "Evolution Strategies as a Scalable Alternative to Reinforcement Learning"
Python
1,504
star
46

generating-reviews-discovering-sentiment

Code for "Learning to Generate Reviews and Discovering Sentiment"
Python
1,491
star
47

neural-mmo

Code for the paper "Neural MMO: A Massively Multiagent Game Environment for Training and Evaluating Intelligent Agents"
Python
1,463
star
48

prm800k

800,000 step-level correctness labels on LLM solutions to MATH problems
Python
1,371
star
49

openai-dotnet

The official .NET library for the OpenAI API
C#
1,352
star
50

openai-assistants-quickstart

OpenAI Assistants API quickstart with Next.js.
TypeScript
1,350
star
51

sparse_attention

Examples of using sparse attention, as in "Generating Long Sequences with Sparse Transformers"
Python
1,347
star
52

maddpg

Code for the MADDPG algorithm from the paper "Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments"
Python
1,284
star
53

Video-Pre-Training

Video PreTraining (VPT): Learning to Act by Watching Unlabeled Online Videos
Python
1,280
star
54

openai-openapi

OpenAPI specification for the OpenAI API
1,235
star
55

lm-human-preferences

Code for the paper Fine-Tuning Language Models from Human Preferences
Python
1,185
star
56

following-instructions-human-feedback

1,129
star
57

universe-starter-agent

A starter agent that can solve a number of universe environments.
Python
1,086
star
58

dalle-2-preview

1,044
star
59

InfoGAN

Code for reproducing key results in the paper "InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets"
Python
1,029
star
60

grade-school-math

Python
1,005
star
61

procgen

Procgen Benchmark: Procedurally-Generated Game-Like Gym-Environments
C++
1,005
star
62

supervised-reptile

Code for the paper "On First-Order Meta-Learning Algorithms"
JavaScript
955
star
63

blocksparse

Efficient GPU kernels for block-sparse matrix multiplication and convolution
Cuda
941
star
64

random-network-distillation

Code for the paper "Exploration by Random Network Distillation"
Python
861
star
65

kubernetes-ec2-autoscaler

A batch-optimized scaling manager for Kubernetes
Python
849
star
66

summarize-from-feedback

Code for "Learning to summarize from human feedback"
Python
833
star
67

large-scale-curiosity

Code for the paper "Large-Scale Study of Curiosity-Driven Learning"
Python
800
star
68

multiagent-competition

Code for the paper "Emergent Complexity via Multi-agent Competition"
Python
761
star
69

imitation

Code for the paper "Generative Adversarial Imitation Learning"
Python
643
star
70

deeptype

Code for the paper "DeepType: Multilingual Entity Linking by Neural Type System Evolution"
Python
633
star
71

mlsh

Code for the paper "Meta-Learning Shared Hierarchies"
Python
588
star
72

iaf

Code for reproducing key results in the paper "Improving Variational Inference with Inverse Autoregressive Flow"
Python
499
star
73

mujoco-worldgen

Automatic object XML generation for Mujoco
Python
489
star
74

safety-gym

Tools for accelerating safe exploration research.
Python
421
star
75

vdvae

Repository for the paper "Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images"
Python
407
star
76

coinrun

Code for the paper "Quantifying Transfer in Reinforcement Learning"
C++
390
star
77

robogym

Robotics Gym Environments
Python
389
star
78

weightnorm

Example code for Weight Normalization, from "Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks"
Python
357
star
79

atari-py

A packaged and slightly-modified version of https://github.com/bbitmaster/ale_python_interface
C++
354
star
80

openai-security-bots

Python
351
star
81

openai-gemm

Open single and half precision gemm implementations
C
335
star
82

vime

Code for the paper "Curiosity-driven Exploration in Deep Reinforcement Learning via Bayesian Neural Networks"
Python
331
star
83

safety-starter-agents

Basic constrained RL agents used in experiments for the "Benchmarking Safe Exploration in Deep Reinforcement Learning" paper.
Python
312
star
84

ebm_code_release

Code for Implicit Generation and Generalization with Energy Based Models
Python
311
star
85

CLIP-featurevis

code for reproducing some of the diagrams in the paper "Multimodal Neurons in Artificial Neural Networks"
Python
294
star
86

gym-http-api

API to access OpenAI Gym from other languages via HTTP
Python
292
star
87

gym-soccer

Python
289
star
88

sparse_autoencoder

Python
287
star
89

robosumo

Code for the paper "Continuous Adaptation via Meta-Learning in Nonstationary and Competitive Environments"
Python
283
star
90

web-crawl-q-and-a-example

Learn how to crawl your website and build a Q/A bot with the OpenAI API
Jupyter Notebook
268
star
91

phasic-policy-gradient

Code for the paper "Phasic Policy Gradient"
Python
245
star
92

EPG

Code for the paper "Evolved Policy Gradients"
Python
240
star
93

orrb

Code for the paper "OpenAI Remote Rendering Backend"
C#
235
star
94

miniF2F

Formal to Formal Mathematics Benchmark
Objective-C++
202
star
95

atari-reset

Code for the blog post "Learning Montezuma’s Revenge from a Single Demonstration"
Python
183
star
96

spinningup-workshop

For educational materials related to the spinning up workshops.
TeX
181
star
97

train-procgen

Code for the paper "Leveraging Procedural Generation to Benchmark Reinforcement Learning"
Python
170
star
98

human-eval-infilling

Code for the paper "Efficient Training of Language Models to Fill in the Middle"
Python
162
star
99

openai-go

The official Go library for the OpenAI API
Go
145
star
100

dallify-discord-bot

Example code for using OpenAI’s NodeJS SDK with discord.js SDK to create a Discord Bot that uses Slash Commands.
TypeScript
139
star