mcleish7 / gemstone-scaling-laws
☆23Updated 2 months ago
Alternatives and similar repositories for gemstone-scaling-laws:
Users that are interested in gemstone-scaling-laws are comparing it to the libraries listed below
- ☆18Updated 9 months ago
- [NeurIPS 2024] Goldfish Loss: Mitigating Memorization in Generative LLMs☆84Updated 5 months ago
- The repository contains code for Adaptive Data Optimization☆23Updated 4 months ago
- Is In-Context Learning Sufficient for Instruction Following in LLMs? [ICLR 2025]☆29Updated 3 months ago
- ☆31Updated 3 months ago
- Stanford NLP Python library for benchmarking the utility of LLM interpretability methods☆70Updated 3 weeks ago
- PaCE: Parsimonious Concept Engineering for Large Language Models (NeurIPS 2024)☆35Updated 5 months ago
- ☆13Updated last year
- Providing the answer to "How to do patching on all available SAEs on GPT-2?". It is an official repository of the implementation of the p…☆11Updated 2 months ago
- Official repository of "LiNeS: Post-training Layer Scaling Prevents Forgetting and Enhances Model Merging"☆25Updated 5 months ago
- The official repository for SkyLadder: Better and Faster Pretraining via Context Window Scheduling☆29Updated last month
- ☆47Updated last year
- ☆33Updated 4 months ago
- ☆42Updated last year
- Exploration of automated dataset selection approaches at large scales.☆39Updated last month
- [NeurIPS 2024 Spotlight] Code and data for the paper "Finding Transformer Circuits with Edge Pruning".☆52Updated last month
- Simple and scalable tools for data-driven pretraining data selection.☆22Updated 2 months ago
- Efficient Scaling laws and collaborative pretraining.☆16Updated 2 months ago
- Repository for NPHardEval, a quantified-dynamic benchmark of LLMs☆53Updated last year
- Intriguing Properties of Data Attribution on Diffusion Models (ICLR 2024)☆28Updated last year
- ☆54Updated 2 years ago
- An official implementation of "Catastrophic Failure of LLM Unlearning via Quantization" (ICLR 2025)☆26Updated 2 months ago
- ☆14Updated last year
- [ICLR 2025] Cheating Automatic LLM Benchmarks: Null Models Achieve High Win Rates (Oral)☆77Updated 6 months ago
- Codebase for Instruction Following without Instruction Tuning☆34Updated 7 months ago
- Official Repository for Dataset Inference for LLMs☆33Updated 9 months ago
- Codebase for Context-aware Meta-learned Loss Scaling (CaMeLS). https://arxiv.org/abs/2305.15076.☆25Updated last year
- Code for NeurIPS 2024 Spotlight: "Scaling Laws and Compute-Optimal Training Beyond Fixed Training Durations"☆71Updated 5 months ago
- ☆27Updated 9 months ago
- ☆18Updated last month