Jinfeng Li's repositories
LLaMA-Factory
A WebUI for Efficient Fine-Tuning of 100+ LLMs (ACL 2024)
S-Eval
S-Eval: Automatic and Adaptive Test Generation for Benchmarking Safety Evaluation of Large Language Models
jailbreak_llms
A dataset consists of 6,387 ChatGPT prompts from Reddit, Discord, websites, and open-source datasets (including 666 jailbreak prompts).
LLMDataHub
A quick guide (especially) for trending instruction finetuning datasets
transformers_tasks
⭐️ NLP Algorithms with transformers lib. Supporting Text-Classification, Text-Generation, Information-Extraction, Text-Matching, RLHF, SFT etc.
xtuner
XTuner is a toolkit for efficiently fine-tuning LLM
zero_nlp
中文nlp解决方案(大模型、数据、模型、训练、推理)
llm-attacks
Universal and Transferable Attacks on Aligned Language Models
ChatGLM-Finetuning
基于ChatGLM-6B、ChatGLM2-6B模型,进行下游具体任务微调,涉及Freeze、Lora、P-tuning、全参微调等
helm
Holistic Evaluation of Language Models (HELM), a framework to increase the transparency of language models (https://arxiv.org/abs/2211.09110).
T2Ranking
T2Ranking: A large-scale Chinese benchmark for passage ranking.
ChatGLM-Tuning
一种平价的chatgpt实现方案, 基于ChatGLM-6B + LoRA
GPT-4-LLM
Instruction Tuning with GPT-4
langchain-ChatGLM
langchain-ChatGLM, local knowledge based ChatGLM with langchain | 基于本地知识库的 ChatGLM 问答
ChatGLM-6B
ChatGLM-6B:开源双语对话语言模型 | An Open Bilingual Dialogue Language Model
LLMZoo
⚡LLM Zoo is a project that provides data, models, and evaluation benchmark for large language models.⚡
Adan
Adan: Adaptive Nesterov Momentum Algorithm for Faster Optimizing Deep Models
imbalanced-dataset-sampler
A (PyTorch) imbalanced dataset sampler for oversampling low frequent classes and undersampling high frequent ones.
ChineseNLPCorpus
中文自然语言处理数据集,平时做做实验的材料。欢迎补充提交合并。
mt-dnn
Multi-Task Deep Neural Networks for Natural Language Understanding
transformers-tutorials
Github repo with tutorials to fine tune transformers for diff NLP tasks
pCLUE
pCLUE: 1000000+多任务提示学习数据集
EconML
ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.
EasyRec
A framework for large scale recommendation algorithms.
DeepMatch
A deep matching model library for recommendations & advertising. It's easy to train models and to export representation vectors which can be used for ANN search.
recmetrics
A library of metrics for evaluating recommender systems
ganbert
Enhancing the BERT training with Semi-supervised Generative Adversarial Networks
shap
A game theoretic approach to explain the output of any machine learning model.