EleutherAI ha una forte presenza su GitHub, con un'ampia gamma di repository pubblici dedicati principalmente a linguaggi come Python, Jupyter Notebook, C++ e JavaScript. Tra i progetti più noti ci sono lm-evaluation-harness e gpt-neox, che si concentrano sull'evaluazione dei modelli linguistici e sull'implementazione di trasformatori autoregressivi, rispettivamente.
A framework for few-shot evaluation of language models.
An implementation of model parallel autoregressive transformers on GPUs, based on the Megatron and DeepSpeed libraries
The hub for EleutherAI's work on interpretability and learning dynamics
Nessuna descrizione fornita per questo repository.
Deep learning for dummies. All the practical details and useful utilities that go into working with real models.
Sparsify transformers with SAEs and transcoders
Polyglot: Large Language Models of Well-balanced Competence in Multi-languages
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Erasing concepts from neural representations with provable guarantees
Keeping language models honest by directly eliciting knowledge encoded in their activations.
The simplest, fastest repository for training/finetuning medium-sized GPTs.
OSLO: Open Source for Large-scale Optimization
Official repository for the paper: Scaling Self-Supervised Representation Learning for Symbolic Piano Performance (ISMIR 2025)
Data processing system for polyglot
Experiments for efforts to train a new and improved t5
Minetest is an open source voxel game engine with easy modding and game creation
Efficient and robust implementation of seq-to-seq automatic piano transcription.
Mapping out the "memory" of neural nets with data attribution
One stop shop for all things carp
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Understanding how features learned by neural networks evolve throughout training
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Engineering the state of RNN language models (Mamba, RWKV, etc.)
Investigating the generalization behavior of LM probes trained to predict truth labels: (1) from one annotator to another, and (2) from easy questions to hard
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Efficiently computing & storing token n-grams from large corpora
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data related codebase for polyglot project
Pile Deduplication Code
Latent video diffusion
NeMo: a toolkit for conversational AI
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Closed-form polynomial approximations to neural networks
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Experiments in transformer knowledge and reasoning
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Linear probes with attention weighting
Equinox implementation of llama3 and llama3.1
A collection of modular datasets generated by GPT-4, General-Instruct - Roleplay-Instruct - Code-Instruct - and Toolformer
Baseline agents for Minetest tasks.
MIDI tokenizers and pre-processing utils.
A library for mechanistic anomaly detection
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A repo for distributed training of language models with Reinforcement Learning via Human Feedback (RLHF)
Jupyter notebook for the interpretablity section of the minetester blog post
Data collection pipeline for CodeCARP. Includes PyCharm plugins.
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Adds GaLore style projection wrappers to optax optimizers
Repository to host architecture experiments and development using Paxml and Praxis
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The code used in "Balancing Label Quantity and Quality for Scalable Elicitation"
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Robust recipes for to align language models with human and AI preferences
Project Repo for Unpaired Image Generation project
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Acompanying code for our research on SAE feature overlap when trained on different seeds.
Studying the variance in neural net predictions across training time
A replication of "EvilModel 2.0: Bringing Neural Network Models into Malware Attacks"
Library of interesting prompt generations
Can interpretability methods confer an advantage in competitive games?
Analogue of fMRI on artificial neural networks
https://github.com/xiexi51/RTopK PyTorch wrapper
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Tools for understanding how transformer predictions are built layer-by-layer
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A large instruct dataset for open-source models (WIP).
Mosaicml example benchmarks + LLM scripts
Minetest Game - The default game for the Minetest engine [https://github.com/minetest/minetest/]
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Investigating goal instability in RL
Democratizing Reinforcement Learning for LLMs
Exactly what it says on the tin
RWKV is an RNN with transformer-level LLM performance. It can be directly trained like a GPT (parallelizable). So it's combining the best of RNN and transformer - great performance, fast inference, saves VRAM, fast training, "infinite" ctx_len, and free sentence embedding.
Applying LEACE to models during training
GGML implementation of https://github.com/EleutherAI/aria
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A simple, performant and scalable Jax LLM!
Minetest's fork of Irrlicht
App for generating html table from LM evaluation JSONs
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Simplified library for mapping out the "memory" of neural nets with data attribution
A high-throughput and memory-efficient inference and serving engine for LLMs
Verifiers for LLM Reinforcement Learning
WMDP is a LLM proxy benchmark for hazardous knowledge in bio, cyber, and chemical security. We also release code for RMU, an unlearning method which reduces LLM performance on WMDP while retaining general capabilities.
Ongoing research training transformer models at scale
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A library for accelerating Transformer models on NVIDIA GPUs, including using 8-bit floating point (FP8) precision on Hopper and Ada GPUs, to provide better performance with lower memory utilization in both training and inference.
implmentation of Plenoxels radiance fields without neural networks, with free nerf strategy
OSLO: Open Source for Large-scale Optimization
Reproduce results and replicate training fo T0 (Multitask Prompted Training Enables Zero-Shot Task Generalization)
[WIP] a version of CLU with WandB logging added.
Compute FID scores with PyTorch.
EleutherAI sviluppa progetti focalizzati sull'intelligenza artificiale e l'apprendimento automatico, con repository come lm-evaluation-harness e gpt-neox, che servono per valutare i modelli di linguaggio e implementare trasformatori su GPU.
EleutherAI utilizza principalmente Python e Jupyter Notebook, ma anche C++, JavaScript, Rust e Cuda per i suoi progetti. Questa varietà consente di affrontare diversi aspetti dell'apprendimento automatico e dell'IA.
Sì, tutti i repository di EleutherAI sono pubblici. Questo consente a sviluppatori e ricercatori di accedere ai loro progetti e contribuire allo sviluppo di tecnologie avanzate nel campo dell'intelligenza artificiale.
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