A large-scale 7B pretraining language model developed by BaiChuan-Inc.
A series of large language models developed by Baichuan Intelligent Technology
A 13B large language model developed by Baichuan Intelligent Technology
A Contamination-free Multi-task Language Understanding Benchmark [Official, ACL 2025]
[NeurIPS 2023 Spotlight] In-Context Impersonation Reveals Large Language Models' Strengths and Biases
AGI-Elo: How Far Are We From Mastering A Task?
CLI tool to evaluate LLM factuality on MMLU benchmark.
Code and data accompanying the article "The impact of quantising a small open source LLM". This repository explores how quantisation affects performance, VRAM usage, and inference speed in Qwen3 1.7B.
An easy-to-use and standardised framework for evaluating Large Language Models (LLMs) on the Massive Multitask Language Understanding (MMLU) dataset. Currently supported: Hugging Face transformer models and Bedrock models.
LLMs' performance analysis on CPU, GPU, Execution Time and Energy Usage
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