stanford-crfm / helmLinks
Holistic Evaluation of Language Models (HELM) is an open source Python framework created by the Center for Research on Foundation Models (CRFM) at Stanford for holistic, reproducible and transparent evaluation of foundation models, including large language models (LLMs) and multimodal models.
☆2,575Updated this week
Alternatives and similar repositories for helm
Users that are interested in helm are comparing it to the libraries listed below
Sorting:
- Measuring Massive Multitask Language Understanding | ICLR 2021☆1,527Updated 2 years ago
- An automatic evaluator for instruction-following language models. Human-validated, high-quality, cheap, and fast.☆1,920Updated 4 months ago
- The hub for EleutherAI's work on interpretability and learning dynamics☆2,689Updated 3 weeks ago
- General technology for enabling AI capabilities w/ LLMs and MLLMs☆4,217Updated last week
- Beyond the Imitation Game collaborative benchmark for measuring and extrapolating the capabilities of language models☆3,168Updated last year
- Doing simple retrieval from LLM models at various context lengths to measure accuracy☆2,097Updated last year
- Lighteval is your all-in-one toolkit for evaluating LLMs across multiple backends☆2,185Updated this week
- ☆1,555Updated last week
- Human preference data for "Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback"☆1,803Updated 5 months ago
- Toolkit for creating, sharing and using natural language prompts.☆2,978Updated 2 years ago
- Benchmarking large language models' complex reasoning ability with chain-of-thought prompting☆2,761Updated last year
- AllenAI's post-training codebase☆3,417Updated this week
- A framework for few-shot evaluation of language models.☆10,920Updated this week
- The official GitHub page for the survey paper "A Survey on Evaluation of Large Language Models".☆1,584Updated 6 months ago
- Data and tools for generating and inspecting OLMo pre-training data.☆1,359Updated last month
- YaRN: Efficient Context Window Extension of Large Language Models☆1,644Updated last year
- Freeing data processing from scripting madness by providing a set of platform-agnostic customizable pipeline processing blocks.☆2,768Updated this week
- A family of open-sourced Mixture-of-Experts (MoE) Large Language Models☆1,641Updated last year
- Robust recipes to align language models with human and AI preferences☆5,447Updated 3 months ago
- TruthfulQA: Measuring How Models Imitate Human Falsehoods☆855Updated 10 months ago
- MTEB: Massive Text Embedding Benchmark☆3,021Updated this week
- 800,000 step-level correctness labels on LLM solutions to MATH problems☆2,076Updated 2 years ago
- A library with extensible implementations of DPO, KTO, PPO, ORPO, and other human-aware loss functions (HALOs).☆894Updated 2 months ago
- Code for our EMNLP 2023 Paper: "LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models"☆1,217Updated last year
- Distilabel is a framework for synthetic data and AI feedback for engineers who need fast, reliable and scalable pipelines based on verifi…☆2,966Updated this week
- S-LoRA: Serving Thousands of Concurrent LoRA Adapters☆1,874Updated last year
- Minimalistic large language model 3D-parallelism training☆2,362Updated 3 weeks ago
- A modular RL library to fine-tune language models to human preferences☆2,374Updated last year
- 🤗 Evaluate: A library for easily evaluating machine learning models and datasets.☆2,380Updated last month
- The papers are organized according to our survey: Evaluating Large Language Models: A Comprehensive Survey.☆788Updated last year