From f231e0e6f9fd146f5bd326aa35604916ee7a7861 Mon Sep 17 00:00:00 2001 From: Wonderplex <50866817+Jasonqi146@users.noreply.github.com> Date: Mon, 6 Nov 2023 20:23:55 -0500 Subject: [PATCH] added llama-factory under llm_rl (#87) --- .gitignore | 162 +++- llm_rl/LICENSE | 201 +++++ llm_rl/README.md | 501 ++++++++++++ llm_rl/README_zh.md | 500 ++++++++++++ llm_rl/assets/wechat.jpg | Bin 0 -> 143444 bytes llm_rl/data/README.md | 107 +++ llm_rl/data/README_zh.md | 107 +++ .../data/belle_multiturn/belle_multiturn.py | 79 ++ .../data/example_dataset/example_dataset.py | 46 ++ llm_rl/data/hh_rlhf_en/hh_rlhf_en.py | 97 +++ llm_rl/data/ultra_chat/ultra_chat.py | 76 ++ llm_rl/data/wiki_demo.txt | 50 ++ llm_rl/evaluation/ceval/ceval.py | 166 ++++ llm_rl/evaluation/ceval/ceval.zip | Bin 0 -> 1548171 bytes llm_rl/evaluation/cmmlu/cmmlu.py | 167 ++++ llm_rl/evaluation/cmmlu/cmmlu.zip | Bin 0 -> 1078352 bytes llm_rl/evaluation/mmlu/mmlu.py | 167 ++++ llm_rl/evaluation/mmlu/mmlu.zip | Bin 0 -> 2336375 bytes llm_rl/pyproject.toml | 3 + llm_rl/requirements.txt | 20 + llm_rl/reward_model.sh | 21 + llm_rl/setup.py | 55 ++ llm_rl/src/api_demo.py | 14 + llm_rl/src/cli_demo.py | 39 + llm_rl/src/evaluate.py | 190 +++++ llm_rl/src/export_model.py | 9 + llm_rl/src/llmtuner/__init__.py | 9 + llm_rl/src/llmtuner/api/__init__.py | 1 + llm_rl/src/llmtuner/api/app.py | 146 ++++ llm_rl/src/llmtuner/api/protocol.py | 83 ++ llm_rl/src/llmtuner/chat/__init__.py | 1 + llm_rl/src/llmtuner/chat/stream_chat.py | 109 +++ llm_rl/src/llmtuner/dsets/__init__.py | 3 + llm_rl/src/llmtuner/dsets/loader.py | 145 ++++ llm_rl/src/llmtuner/dsets/preprocess.py | 268 +++++++ llm_rl/src/llmtuner/dsets/utils.py | 59 ++ llm_rl/src/llmtuner/extras/__init__.py | 0 llm_rl/src/llmtuner/extras/callbacks.py | 155 ++++ llm_rl/src/llmtuner/extras/constants.py | 92 +++ llm_rl/src/llmtuner/extras/logging.py | 43 ++ llm_rl/src/llmtuner/extras/misc.py | 118 +++ .../src/llmtuner/extras/patches/__init__.py | 0 .../llmtuner/extras/patches/llama_patch.py | 218 ++++++ llm_rl/src/llmtuner/extras/ploting.py | 52 ++ llm_rl/src/llmtuner/extras/save_and_load.py | 21 + llm_rl/src/llmtuner/extras/template.py | 713 ++++++++++++++++++ llm_rl/src/llmtuner/hparams/__init__.py | 4 + llm_rl/src/llmtuner/hparams/data_args.py | 169 +++++ .../src/llmtuner/hparams/finetuning_args.py | 107 +++ llm_rl/src/llmtuner/hparams/general_args.py | 13 + .../src/llmtuner/hparams/generating_args.py | 53 ++ llm_rl/src/llmtuner/hparams/model_args.py | 93 +++ llm_rl/src/llmtuner/tuner/__init__.py | 1 + llm_rl/src/llmtuner/tuner/core/__init__.py | 2 + llm_rl/src/llmtuner/tuner/core/adapter.py | 101 +++ llm_rl/src/llmtuner/tuner/core/loader.py | 244 ++++++ llm_rl/src/llmtuner/tuner/core/parser.py | 226 ++++++ llm_rl/src/llmtuner/tuner/core/utils.py | 94 +++ llm_rl/src/llmtuner/tuner/dpo/__init__.py | 1 + llm_rl/src/llmtuner/tuner/dpo/collator.py | 51 ++ llm_rl/src/llmtuner/tuner/dpo/trainer.py | 104 +++ llm_rl/src/llmtuner/tuner/dpo/workflow.py | 66 ++ llm_rl/src/llmtuner/tuner/ppo/__init__.py | 1 + llm_rl/src/llmtuner/tuner/ppo/trainer.py | 310 ++++++++ llm_rl/src/llmtuner/tuner/ppo/utils.py | 35 + llm_rl/src/llmtuner/tuner/ppo/workflow.py | 92 +++ llm_rl/src/llmtuner/tuner/pt/__init__.py | 1 + llm_rl/src/llmtuner/tuner/pt/workflow.py | 58 ++ llm_rl/src/llmtuner/tuner/rm/__init__.py | 1 + llm_rl/src/llmtuner/tuner/rm/collator.py | 27 + llm_rl/src/llmtuner/tuner/rm/metric.py | 7 + llm_rl/src/llmtuner/tuner/rm/trainer.py | 105 +++ llm_rl/src/llmtuner/tuner/rm/workflow.py | 68 ++ llm_rl/src/llmtuner/tuner/sft/__init__.py | 1 + llm_rl/src/llmtuner/tuner/sft/metric.py | 53 ++ llm_rl/src/llmtuner/tuner/sft/trainer.py | 92 +++ llm_rl/src/llmtuner/tuner/sft/workflow.py | 90 +++ llm_rl/src/llmtuner/tuner/tune.py | 51 ++ llm_rl/src/llmtuner/webui/__init__.py | 1 + llm_rl/src/llmtuner/webui/chatter.py | 101 +++ llm_rl/src/llmtuner/webui/common.py | 103 +++ .../src/llmtuner/webui/components/__init__.py | 6 + .../src/llmtuner/webui/components/chatbot.py | 49 ++ llm_rl/src/llmtuner/webui/components/data.py | 103 +++ llm_rl/src/llmtuner/webui/components/eval.py | 70 ++ .../src/llmtuner/webui/components/export.py | 79 ++ llm_rl/src/llmtuner/webui/components/infer.py | 39 + llm_rl/src/llmtuner/webui/components/top.py | 74 ++ llm_rl/src/llmtuner/webui/components/train.py | 154 ++++ llm_rl/src/llmtuner/webui/css.py | 20 + llm_rl/src/llmtuner/webui/engine.py | 57 ++ llm_rl/src/llmtuner/webui/interface.py | 66 ++ llm_rl/src/llmtuner/webui/locales.py | 698 +++++++++++++++++ llm_rl/src/llmtuner/webui/manager.py | 35 + llm_rl/src/llmtuner/webui/runner.py | 254 +++++++ llm_rl/src/llmtuner/webui/utils.py | 85 +++ llm_rl/src/train_bash.py | 14 + llm_rl/src/train_web.py | 11 + llm_rl/src/web_demo.py | 11 + llm_rl/tests/cal_flops.py | 44 ++ llm_rl/tests/llamafy_baichuan2.py | 86 +++ llm_rl/tests/llamafy_qwen.py | 135 ++++ llm_rl/tests/quantize.py | 50 ++ 103 files changed, 9678 insertions(+), 1 deletion(-) create mode 100644 llm_rl/LICENSE create mode 100644 llm_rl/README.md create mode 100644 llm_rl/README_zh.md create mode 100644 llm_rl/assets/wechat.jpg create mode 100644 llm_rl/data/README.md create mode 100644 llm_rl/data/README_zh.md create mode 100644 llm_rl/data/belle_multiturn/belle_multiturn.py create mode 100644 llm_rl/data/example_dataset/example_dataset.py create mode 100644 llm_rl/data/hh_rlhf_en/hh_rlhf_en.py create mode 100644 llm_rl/data/ultra_chat/ultra_chat.py create mode 100644 llm_rl/data/wiki_demo.txt create mode 100644 llm_rl/evaluation/ceval/ceval.py create mode 100644 llm_rl/evaluation/ceval/ceval.zip create mode 100644 llm_rl/evaluation/cmmlu/cmmlu.py create mode 100644 llm_rl/evaluation/cmmlu/cmmlu.zip create mode 100644 llm_rl/evaluation/mmlu/mmlu.py create mode 100644 llm_rl/evaluation/mmlu/mmlu.zip create mode 100644 llm_rl/pyproject.toml create mode 100644 llm_rl/requirements.txt create mode 100644 llm_rl/reward_model.sh create mode 100644 llm_rl/setup.py create mode 100644 llm_rl/src/api_demo.py create mode 100644 llm_rl/src/cli_demo.py create mode 100644 llm_rl/src/evaluate.py create mode 100644 llm_rl/src/export_model.py create mode 100644 llm_rl/src/llmtuner/__init__.py create mode 100644 llm_rl/src/llmtuner/api/__init__.py create mode 100644 llm_rl/src/llmtuner/api/app.py create mode 100644 llm_rl/src/llmtuner/api/protocol.py create mode 100644 llm_rl/src/llmtuner/chat/__init__.py create mode 100644 llm_rl/src/llmtuner/chat/stream_chat.py create mode 100644 llm_rl/src/llmtuner/dsets/__init__.py create mode 100644 llm_rl/src/llmtuner/dsets/loader.py create mode 100644 llm_rl/src/llmtuner/dsets/preprocess.py create mode 100644 llm_rl/src/llmtuner/dsets/utils.py create mode 100644 llm_rl/src/llmtuner/extras/__init__.py create mode 100644 llm_rl/src/llmtuner/extras/callbacks.py create mode 100644 llm_rl/src/llmtuner/extras/constants.py create mode 100644 llm_rl/src/llmtuner/extras/logging.py create mode 100644 llm_rl/src/llmtuner/extras/misc.py create mode 100644 llm_rl/src/llmtuner/extras/patches/__init__.py create mode 100644 llm_rl/src/llmtuner/extras/patches/llama_patch.py create mode 100644 llm_rl/src/llmtuner/extras/ploting.py create mode 100644 llm_rl/src/llmtuner/extras/save_and_load.py create mode 100644 llm_rl/src/llmtuner/extras/template.py create mode 100644 llm_rl/src/llmtuner/hparams/__init__.py create mode 100644 llm_rl/src/llmtuner/hparams/data_args.py create mode 100644 llm_rl/src/llmtuner/hparams/finetuning_args.py create mode 100644 llm_rl/src/llmtuner/hparams/general_args.py create mode 100644 llm_rl/src/llmtuner/hparams/generating_args.py create mode 100644 llm_rl/src/llmtuner/hparams/model_args.py create mode 100644 llm_rl/src/llmtuner/tuner/__init__.py create mode 100644 llm_rl/src/llmtuner/tuner/core/__init__.py create mode 100644 llm_rl/src/llmtuner/tuner/core/adapter.py create mode 100644 llm_rl/src/llmtuner/tuner/core/loader.py create mode 100644 llm_rl/src/llmtuner/tuner/core/parser.py create mode 100644 llm_rl/src/llmtuner/tuner/core/utils.py create mode 100644 llm_rl/src/llmtuner/tuner/dpo/__init__.py create mode 100644 llm_rl/src/llmtuner/tuner/dpo/collator.py create mode 100644 llm_rl/src/llmtuner/tuner/dpo/trainer.py create mode 100644 llm_rl/src/llmtuner/tuner/dpo/workflow.py create mode 100644 llm_rl/src/llmtuner/tuner/ppo/__init__.py create mode 100644 llm_rl/src/llmtuner/tuner/ppo/trainer.py create mode 100644 llm_rl/src/llmtuner/tuner/ppo/utils.py create mode 100644 llm_rl/src/llmtuner/tuner/ppo/workflow.py create mode 100644 llm_rl/src/llmtuner/tuner/pt/__init__.py create mode 100644 llm_rl/src/llmtuner/tuner/pt/workflow.py create mode 100644 llm_rl/src/llmtuner/tuner/rm/__init__.py create mode 100644 llm_rl/src/llmtuner/tuner/rm/collator.py create mode 100644 llm_rl/src/llmtuner/tuner/rm/metric.py create mode 100644 llm_rl/src/llmtuner/tuner/rm/trainer.py create mode 100644 llm_rl/src/llmtuner/tuner/rm/workflow.py create mode 100644 llm_rl/src/llmtuner/tuner/sft/__init__.py create mode 100644 llm_rl/src/llmtuner/tuner/sft/metric.py create mode 100644 llm_rl/src/llmtuner/tuner/sft/trainer.py create mode 100644 llm_rl/src/llmtuner/tuner/sft/workflow.py create mode 100644 llm_rl/src/llmtuner/tuner/tune.py create mode 100644 llm_rl/src/llmtuner/webui/__init__.py create mode 100644 llm_rl/src/llmtuner/webui/chatter.py create mode 100644 llm_rl/src/llmtuner/webui/common.py create mode 100644 llm_rl/src/llmtuner/webui/components/__init__.py create mode 100644 llm_rl/src/llmtuner/webui/components/chatbot.py create mode 100644 llm_rl/src/llmtuner/webui/components/data.py create mode 100644 llm_rl/src/llmtuner/webui/components/eval.py create mode 100644 llm_rl/src/llmtuner/webui/components/export.py create mode 100644 llm_rl/src/llmtuner/webui/components/infer.py create mode 100644 llm_rl/src/llmtuner/webui/components/top.py create mode 100644 llm_rl/src/llmtuner/webui/components/train.py create mode 100644 llm_rl/src/llmtuner/webui/css.py create mode 100644 llm_rl/src/llmtuner/webui/engine.py create mode 100644 llm_rl/src/llmtuner/webui/interface.py create mode 100644 llm_rl/src/llmtuner/webui/locales.py create mode 100644 llm_rl/src/llmtuner/webui/manager.py create mode 100644 llm_rl/src/llmtuner/webui/runner.py create mode 100644 llm_rl/src/llmtuner/webui/utils.py create mode 100644 llm_rl/src/train_bash.py create mode 100644 llm_rl/src/train_web.py create mode 100644 llm_rl/src/web_demo.py create mode 100644 llm_rl/tests/cal_flops.py create mode 100644 llm_rl/tests/llamafy_baichuan2.py create mode 100644 llm_rl/tests/llamafy_qwen.py create mode 100644 llm_rl/tests/quantize.py diff --git a/.gitignore b/.gitignore index 3e563d1d..83339037 100644 --- a/.gitignore +++ b/.gitignore @@ -5,6 +5,13 @@ __pycache__ dist .venv +# Byte-compiled / optimized / DLL files +*.py[cod] +*$py.class + +# C extensions +*.so + # Log *.log *.log.* @@ -33,4 +40,157 @@ tests/state_of_the_union.txt # Build build -!dummy_file \ No newline at end of file +!dummy_file + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# poetry +# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control +#poetry.lock + +# pdm 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/llm_rl/README.md b/llm_rl/README.md new file mode 100644 index 00000000..54da92da --- /dev/null +++ b/llm_rl/README.md @@ -0,0 +1,501 @@ +# LLaMA Factory: Training and Evaluating Large Language Models with Minimal Effort + +[![GitHub Repo stars](https://img.shields.io/github/stars/hiyouga/LLaMA-Factory?style=social)](https://github.com/hiyouga/LLaMA-Factory/stargazers) +[![GitHub Code License](https://img.shields.io/github/license/hiyouga/LLaMA-Factory)](LICENSE) +[![GitHub last commit](https://img.shields.io/github/last-commit/hiyouga/LLaMA-Factory)](https://github.com/hiyouga/LLaMA-Factory/commits/main) +[![PyPI](https://img.shields.io/pypi/v/llmtuner)](https://pypi.org/project/llmtuner/) +[![Downloads](https://static.pepy.tech/badge/llmtuner)](https://pypi.org/project/llmtuner/) +[![GitHub pull request](https://img.shields.io/badge/PRs-welcome-blue)](https://github.com/hiyouga/LLaMA-Factory/pulls) +[![Discord](https://dcbadge.vercel.app/api/server/e73gccsSd?compact=true&style=flat)](https://discord.gg/e73gccsSd) + +👋 Join our [WeChat](assets/wechat.jpg). + +\[ English | [中文](README_zh.md) \] + +## LLaMA Board: A One-stop Web UI for Getting Started with LLaMA Factory + +Launch **LLaMA Board** via `CUDA_VISIBLE_DEVICES=0 python src/train_web.py`. (multiple GPUs are not supported yet) + +Here is an example of altering the self-cognition of an instruction-tuned language model within 10 minutes on a single GPU. + +https://github.com/hiyouga/LLaMA-Factory/assets/16256802/6ba60acc-e2e2-4bec-b846-2d88920d5ba1 + +## Changelog + +[23/10/21] We supported **[NEFTune](https://arxiv.org/abs/2310.05914)** trick for fine-tuning. Try `--neft_alpha` argument to activate NEFTune, e.g., `--neft_alpha 5`. + +[23/09/27] We supported **$S^2$-Attn** proposed by [LongLoRA](https://github.com/dvlab-research/LongLoRA) for the LLaMA models. Try `--shift_attn` argument to enable shift short attention. + +[23/09/23] We integrated MMLU, C-Eval and CMMLU benchmarks in this repo. See [this example](#evaluation) to evaluate your models. + +[23/09/10] We supported using **[FlashAttention-2](https://github.com/Dao-AILab/flash-attention)** for the LLaMA models. Try `--flash_attn` argument to enable FlashAttention-2 if you are using RTX4090, A100 or H100 GPUs. + +[23/08/12] We supported **RoPE scaling** to extend the context length of the LLaMA models. Try `--rope_scaling linear` argument in training and `--rope_scaling dynamic` argument at inference to extrapolate the position embeddings. + +[23/08/11] We supported **[DPO training](https://arxiv.org/abs/2305.18290)** for instruction-tuned models. See [this example](#dpo-training) to train your models. + +[23/07/31] We supported **dataset streaming**. Try `--streaming` and `--max_steps 10000` arguments to load your dataset in streaming mode. + +[23/07/29] We released two instruction-tuned 13B models at Hugging Face. See these Hugging Face Repos ([LLaMA-2](https://huggingface.co/hiyouga/Llama-2-Chinese-13b-chat) / [Baichuan](https://huggingface.co/hiyouga/Baichuan-13B-sft)) for details. + +[23/07/18] We developed an **all-in-one Web UI** for training, evaluation and inference. Try `train_web.py` to fine-tune models in your Web browser. Thank [@KanadeSiina](https://github.com/KanadeSiina) and [@codemayq](https://github.com/codemayq) for their efforts in the development. + +[23/07/09] We released **[FastEdit](https://github.com/hiyouga/FastEdit)** ⚡🩹, an easy-to-use package for editing the factual knowledge of large language models efficiently. Please follow [FastEdit](https://github.com/hiyouga/FastEdit) if you are interested. + +[23/06/29] We provided a **reproducible example** of training a chat model using instruction-following datasets, see [Baichuan-7B-sft](https://huggingface.co/hiyouga/Baichuan-7B-sft) for details. + +[23/06/22] We aligned the [demo API](src/api_demo.py) with the [OpenAI's](https://platform.openai.com/docs/api-reference/chat) format where you can insert the fine-tuned model in **arbitrary ChatGPT-based applications**. + +[23/06/03] We supported quantized training and inference (aka **[QLoRA](https://github.com/artidoro/qlora)**). Try `--quantization_bit 4/8` argument to work with quantized models. + +## Supported Models + +| Model | Model size | Default module | Template | +| -------------------------------------------------------- | --------------------------- | ----------------- | --------- | +| [Baichuan](https://github.com/baichuan-inc/Baichuan-13B) | 7B/13B | W_pack | baichuan | +| [Baichuan2](https://github.com/baichuan-inc/Baichuan2) | 7B/13B | W_pack | baichuan2 | +| [BLOOM](https://huggingface.co/bigscience/bloom) | 560M/1.1B/1.7B/3B/7.1B/176B | query_key_value | - | +| [BLOOMZ](https://huggingface.co/bigscience/bloomz) | 560M/1.1B/1.7B/3B/7.1B/176B | query_key_value | - | +| [ChatGLM3](https://github.com/THUDM/ChatGLM3) | 6B | query_key_value | chatglm3 | +| [Falcon](https://huggingface.co/tiiuae/falcon-7b) | 7B/40B/180B | query_key_value | - | +| [InternLM](https://github.com/InternLM/InternLM) | 7B/20B | q_proj,v_proj | intern | +| [LLaMA](https://github.com/facebookresearch/llama) | 7B/13B/33B/65B | q_proj,v_proj | - | +| [LLaMA-2](https://huggingface.co/meta-llama) | 7B/13B/70B | q_proj,v_proj | llama2 | +| [Mistral](https://huggingface.co/mistralai) | 7B | q_proj,v_proj | mistral | +| [Phi-1.5](https://huggingface.co/microsoft/phi-1_5) | 1.3B | Wqkv | - | +| [Qwen](https://github.com/QwenLM/Qwen-7B) | 7B/14B | c_attn | qwen | +| [XVERSE](https://github.com/xverse-ai) | 7B/13B/65B | q_proj,v_proj | xverse | + +> [!NOTE] +> **Default module** is used for the `--lora_target` argument, you can use `--lora_target all` to specify all the available modules. +> +> For the "base" models, the `--template` argument can be chosen from `default`, `alpaca`, `vicuna` etc. But make sure to use the **corresponding template** for the "chat" models. + +Please refer to [template.py](src/llmtuner/extras/template.py) for a full list of models we supported. + +## Supported Training Approaches + +| Approach | Full-parameter | Partial-parameter | LoRA | QLoRA | +| ---------------------- | ------------------ | ------------------ | ------------------ | ------------------ | +| Pre-Training | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: | +| Supervised Fine-Tuning | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: | +| Reward Modeling | | | :white_check_mark: | :white_check_mark: | +| PPO Training | | | :white_check_mark: | :white_check_mark: | +| DPO Training | :white_check_mark: | | :white_check_mark: | :white_check_mark: | + +> [!NOTE] +> Use `--quantization_bit 4/8` argument to enable QLoRA. + +## Provided Datasets + +
Pre-training datasets + +- [Wiki Demo (en)](data/wiki_demo.txt) +- [RefinedWeb (en)](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) +- [RedPajama V2 (en)](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-V2) +- [Wikipedia (en)](https://huggingface.co/datasets/olm/olm-wikipedia-20221220) +- [Wikipedia (zh)](https://huggingface.co/datasets/pleisto/wikipedia-cn-20230720-filtered) +- [Pile (en)](https://huggingface.co/datasets/EleutherAI/pile) +- [SkyPile (zh)](https://huggingface.co/datasets/Skywork/SkyPile-150B) +- [The Stack (en)](https://huggingface.co/datasets/bigcode/the-stack) +- [StarCoder (en)](https://huggingface.co/datasets/bigcode/starcoderdata) + +
+ +
Supervised fine-tuning datasets + +- [Stanford Alpaca (en)](https://github.com/tatsu-lab/stanford_alpaca) +- [Stanford Alpaca (zh)](https://github.com/ymcui/Chinese-LLaMA-Alpaca) +- [GPT-4 Generated Data (en&zh)](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM) +- [Self-cognition (zh)](data/self_cognition.json) +- [Open Assistant (multilingual)](https://huggingface.co/datasets/OpenAssistant/oasst1) +- [ShareGPT (zh)](https://huggingface.co/datasets/QingyiSi/Alpaca-CoT/tree/main/Chinese-instruction-collection) +- [Guanaco Dataset (multilingual)](https://huggingface.co/datasets/JosephusCheung/GuanacoDataset) +- [BELLE 2M (zh)](https://huggingface.co/datasets/BelleGroup/train_2M_CN) +- [BELLE 1M (zh)](https://huggingface.co/datasets/BelleGroup/train_1M_CN) +- [BELLE 0.5M (zh)](https://huggingface.co/datasets/BelleGroup/train_0.5M_CN) +- [BELLE Dialogue 0.4M (zh)](https://huggingface.co/datasets/BelleGroup/generated_chat_0.4M) +- [BELLE School Math 0.25M (zh)](https://huggingface.co/datasets/BelleGroup/school_math_0.25M) +- [BELLE Multiturn Chat 0.8M (zh)](https://huggingface.co/datasets/BelleGroup/multiturn_chat_0.8M) +- [UltraChat (en)](https://github.com/thunlp/UltraChat) +- [LIMA (en)](https://huggingface.co/datasets/GAIR/lima) +- [OpenPlatypus (en)](https://huggingface.co/datasets/garage-bAInd/Open-Platypus) +- [CodeAlpaca 20k (en)](https://huggingface.co/datasets/sahil2801/CodeAlpaca-20k) +- [Alpaca CoT (multilingual)](https://huggingface.co/datasets/QingyiSi/Alpaca-CoT) +- [MathInstruct (en)](https://huggingface.co/datasets/TIGER-Lab/MathInstruct) +- [Firefly 1.1M (zh)](https://huggingface.co/datasets/YeungNLP/firefly-train-1.1M) +- [Web QA (zh)](https://huggingface.co/datasets/suolyer/webqa) +- [WebNovel (zh)](https://huggingface.co/datasets/zxbsmk/webnovel_cn) +- [Ad Gen (zh)](https://huggingface.co/datasets/HasturOfficial/adgen) +- [ShareGPT Hyperfiltered (en)](https://huggingface.co/datasets/totally-not-an-llm/sharegpt-hyperfiltered-3k) +- [ShareGPT4 (en&zh)](https://huggingface.co/datasets/shibing624/sharegpt_gpt4) +- [UltraChat 200k (en)](https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k) +- [AgentInstruct (en)](https://huggingface.co/datasets/THUDM/AgentInstruct) +- [LMSYS Chat 1M (en)](https://huggingface.co/datasets/lmsys/lmsys-chat-1m) +- [Evol Instruct V2 (en)](https://huggingface.co/datasets/WizardLM/WizardLM_evol_instruct_V2_196k) + +
+ +
Preference datasets + +- [HH-RLHF (en)](https://huggingface.co/datasets/Anthropic/hh-rlhf) +- [Open Assistant (multilingual)](https://huggingface.co/datasets/OpenAssistant/oasst1) +- [GPT-4 Generated Data (en&zh)](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM) + +
+ +Please refer to [data/README.md](data/README.md) for details. + +Some datasets require confirmation before using them, so we recommend logging in with your Hugging Face account using these commands. + +```bash +pip install --upgrade huggingface_hub +huggingface-cli login +``` + +## Requirement + +- Python 3.8+ and PyTorch 1.13.1+ +- 🤗Transformers, Datasets, Accelerate, PEFT and TRL +- sentencepiece, protobuf and tiktoken +- fire, jieba, rouge-chinese and nltk (used at evaluation and predict) +- gradio and matplotlib (used in web UI) +- uvicorn, fastapi and sse-starlette (used in API) + +And **powerful GPUs**! + +## Getting Started + +### Data Preparation (optional) + +Please refer to [data/README.md](data/README.md) for checking the details about the format of dataset files. You can either use a single `.json` file or a [dataset loading script](https://huggingface.co/docs/datasets/dataset_script) with multiple files to create a custom dataset. + +> [!NOTE] +> Please update `data/dataset_info.json` to use your custom dataset. About the format of this file, please refer to `data/README.md`. + +### Dependence Installation (optional) + +```bash +git clone https://github.com/hiyouga/LLaMA-Factory.git +conda create -n llama_factory python=3.10 +conda activate llama_factory +cd LLaMA-Factory +pip install -r requirements.txt +``` + +If you want to enable the quantized LoRA (QLoRA) on the Windows platform, you will be required to install a pre-built version of `bitsandbytes` library, which supports CUDA 11.1 to 12.1. + +```bash +pip install https://github.com/jllllll/bitsandbytes-windows-webui/releases/download/wheels/bitsandbytes-0.39.1-py3-none-win_amd64.whl +``` + +### Train on a single GPU + +> [!IMPORTANT] +> If you want to train models on multiple GPUs, please refer to [Distributed Training](#distributed-training). + +#### Pre-Training + +```bash +CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \ + --stage pt \ + --model_name_or_path path_to_llama_model \ + --do_train \ + --dataset wiki_demo \ + --finetuning_type lora \ + --lora_target q_proj,v_proj \ + --output_dir path_to_pt_checkpoint \ + --overwrite_cache \ + --per_device_train_batch_size 4 \ + --gradient_accumulation_steps 4 \ + --lr_scheduler_type cosine \ + --logging_steps 10 \ + --save_steps 1000 \ + --learning_rate 5e-5 \ + --num_train_epochs 3.0 \ + --plot_loss \ + --fp16 +``` + +#### Supervised Fine-Tuning + +```bash +CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \ + --stage sft \ + --model_name_or_path path_to_llama_model \ + --do_train \ + --dataset alpaca_gpt4_en \ + --template default \ + --finetuning_type lora \ + --lora_target q_proj,v_proj \ + --output_dir path_to_sft_checkpoint \ + --overwrite_cache \ + --per_device_train_batch_size 4 \ + --gradient_accumulation_steps 4 \ + --lr_scheduler_type cosine \ + --logging_steps 10 \ + --save_steps 1000 \ + --learning_rate 5e-5 \ + --num_train_epochs 3.0 \ + --plot_loss \ + --fp16 +``` + +#### Reward Modeling + +```bash +CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \ + --stage rm \ + --model_name_or_path path_to_llama_model \ + --do_train \ + --dataset comparison_gpt4_en \ + --template default \ + --finetuning_type lora \ + --lora_target q_proj,v_proj \ + --resume_lora_training False \ + --checkpoint_dir path_to_sft_checkpoint \ + --output_dir path_to_rm_checkpoint \ + --per_device_train_batch_size 2 \ + --gradient_accumulation_steps 4 \ + --lr_scheduler_type cosine \ + --logging_steps 10 \ + --save_steps 1000 \ + --learning_rate 1e-6 \ + --num_train_epochs 1.0 \ + --plot_loss \ + --fp16 +``` + +#### PPO Training + +```bash +CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \ + --stage ppo \ + --model_name_or_path path_to_llama_model \ + --do_train \ + --dataset alpaca_gpt4_en \ + --template default \ + --finetuning_type lora \ + --lora_target q_proj,v_proj \ + --resume_lora_training False \ + --checkpoint_dir path_to_sft_checkpoint \ + --reward_model path_to_rm_checkpoint \ + --output_dir path_to_ppo_checkpoint \ + --per_device_train_batch_size 2 \ + --gradient_accumulation_steps 4 \ + --lr_scheduler_type cosine \ + --logging_steps 10 \ + --save_steps 1000 \ + --learning_rate 1e-5 \ + --num_train_epochs 1.0 \ + --plot_loss \ + --fp16 +``` + +#### DPO Training + +```bash +CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \ + --stage dpo \ + --model_name_or_path path_to_llama_model \ + --do_train \ + --dataset comparison_gpt4_en \ + --template default \ + --finetuning_type lora \ + --lora_target q_proj,v_proj \ + --resume_lora_training False \ + --checkpoint_dir path_to_sft_checkpoint \ + --output_dir path_to_dpo_checkpoint \ + --per_device_train_batch_size 2 \ + --gradient_accumulation_steps 4 \ + --lr_scheduler_type cosine \ + --logging_steps 10 \ + --save_steps 1000 \ + --learning_rate 1e-5 \ + --num_train_epochs 1.0 \ + --plot_loss \ + --fp16 +``` + +### Distributed Training + +#### Use Huggingface Accelerate + +```bash +accelerate config # configure the environment +accelerate launch src/train_bash.py # arguments (same as above) +``` + +
Example config for LoRA training + +```yaml +compute_environment: LOCAL_MACHINE +distributed_type: MULTI_GPU +downcast_bf16: 'no' +gpu_ids: all +machine_rank: 0 +main_training_function: main +mixed_precision: fp16 +num_machines: 1 +num_processes: 4 +rdzv_backend: static +same_network: true +tpu_env: [] +tpu_use_cluster: false +tpu_use_sudo: false +use_cpu: false +``` + +
+ +#### Use DeepSpeed + +```bash +deepspeed --num_gpus 8 --master_port=9901 src/train_bash.py \ + --deepspeed ds_config.json \ + ... # arguments (same as above) +``` + +
Example config for full-parameter training with DeepSpeed ZeRO-2 + +```json +{ + "train_batch_size": "auto", + "train_micro_batch_size_per_gpu": "auto", + "gradient_accumulation_steps": "auto", + "gradient_clipping": "auto", + "zero_allow_untested_optimizer": true, + "fp16": { + "enabled": "auto", + "loss_scale": 0, + "initial_scale_power": 16, + "loss_scale_window": 1000, + "hysteresis": 2, + "min_loss_scale": 1 + }, + "zero_optimization": { + "stage": 2, + "allgather_partitions": true, + "allgather_bucket_size": 5e8, + "reduce_scatter": true, + "reduce_bucket_size": 5e8, + "overlap_comm": false, + "contiguous_gradients": true + } +} +``` + +
+ +### Export model + +```bash +python src/export_model.py \ + --model_name_or_path path_to_llama_model \ + --template default \ + --finetuning_type lora \ + --checkpoint_dir path_to_checkpoint \ + --export_dir path_to_export +``` + +### API Demo + +```bash +python src/api_demo.py \ + --model_name_or_path path_to_llama_model \ + --template default \ + --finetuning_type lora \ + --checkpoint_dir path_to_checkpoint +``` + +> [!NOTE] +> Visit `http://localhost:8000/docs` for API documentation. + +### CLI Demo + +```bash +python src/cli_demo.py \ + --model_name_or_path path_to_llama_model \ + --template default \ + --finetuning_type lora \ + --checkpoint_dir path_to_checkpoint +``` + +### Web Demo + +```bash +python src/web_demo.py \ + --model_name_or_path path_to_llama_model \ + --template default \ + --finetuning_type lora \ + --checkpoint_dir path_to_checkpoint +``` + +### Evaluation + +```bash +CUDA_VISIBLE_DEVICES=0 python src/evaluate.py \ + --model_name_or_path path_to_llama_model \ + --finetuning_type lora \ + --checkpoint_dir path_to_checkpoint \ + --template vanilla \ + --task mmlu \ + --split test \ + --lang en \ + --n_shot 5 \ + --batch_size 4 +``` + +### Predict + +```bash +CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \ + --stage sft \ + --model_name_or_path path_to_llama_model \ + --do_predict \ + --dataset alpaca_gpt4_en \ + --template default \ + --finetuning_type lora \ + --checkpoint_dir path_to_checkpoint \ + --output_dir path_to_predict_result \ + --per_device_eval_batch_size 8 \ + --max_samples 100 \ + --predict_with_generate +``` + +> [!NOTE] +> We recommend using `--per_device_eval_batch_size=1` and `--max_target_length 128` at 4/8-bit predict. + +## Projects using LLaMA Factory + +- **[StarWhisper](https://github.com/Yu-Yang-Li/StarWhisper)**: A large language model for Astronomy, based on ChatGLM2-6B and Qwen-14B. +- **[DISC-LawLLM](https://github.com/FudanDISC/DISC-LawLLM)**: A large language model specialized in Chinese legal domain, based on Baichuan-13B, is capable of retrieving and reasoning on legal knowledge. +- **[Sunsimiao](https://github.com/thomas-yanxin/Sunsimiao)**: A large language model specialized in Chinese medical domain, based on Baichuan-7B and ChatGLM-6B. +- **[CareGPT](https://github.com/WangRongsheng/CareGPT)**: A series of large language models for Chinese medical domain, based on LLaMA2-7B and Baichuan-13B. + +## License + +This repository is licensed under the [Apache-2.0 License](LICENSE). + +Please follow the model licenses to use the corresponding model weights: [Baichuan](https://huggingface.co/baichuan-inc/Baichuan-13B-Base/resolve/main/Community%20License%20for%20Baichuan-13B%20Model.pdf) / [Baichuan2](https://huggingface.co/baichuan-inc/Baichuan2-13B-Chat/resolve/main/Community%20License%20for%20Baichuan2%20Model.pdf) / [BLOOM](https://huggingface.co/spaces/bigscience/license) / [ChatGLM3](https://github.com/THUDM/ChatGLM3/blob/main/MODEL_LICENSE) / [Falcon](https://huggingface.co/tiiuae/falcon-180B/blob/main/LICENSE.txt) / [InternLM](https://github.com/InternLM/InternLM#license) / [LLaMA](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) / [LLaMA-2](https://ai.meta.com/llama/license/) / [Mistral](LICENSE) / [Phi-1.5](https://huggingface.co/microsoft/phi-1_5/resolve/main/Research%20License.docx) / [Qwen](https://huggingface.co/Qwen/Qwen-7B-Chat/blob/main/LICENSE) / [XVERSE](https://github.com/xverse-ai/XVERSE-13B/blob/main/MODEL_LICENSE.pdf) + +## Citation + +If this work is helpful, please kindly cite as: + +```bibtex +@Misc{llama-factory, + title = {LLaMA Factory}, + author = {hiyouga}, + howpublished = {\url{https://github.com/hiyouga/LLaMA-Factory}}, + year = {2023} +} +``` + +## Acknowledgement + +This repo benefits from [PEFT](https://github.com/huggingface/peft), [QLoRA](https://github.com/artidoro/qlora) and [FastChat](https://github.com/lm-sys/FastChat). Thanks for their wonderful works. + +## Star History + +![Star History Chart](https://api.star-history.com/svg?repos=hiyouga/LLaMA-Factory&type=Date) diff --git a/llm_rl/README_zh.md b/llm_rl/README_zh.md new file mode 100644 index 00000000..c69e3983 --- /dev/null +++ b/llm_rl/README_zh.md @@ -0,0 +1,500 @@ +# LLaMA Factory: 轻松的大模型训练与评估 + +[![GitHub Repo stars](https://img.shields.io/github/stars/hiyouga/LLaMA-Factory?style=social)](https://github.com/hiyouga/LLaMA-Factory/stargazers) +[![GitHub Code License](https://img.shields.io/github/license/hiyouga/LLaMA-Factory)](LICENSE) +[![GitHub last commit](https://img.shields.io/github/last-commit/hiyouga/LLaMA-Factory)](https://github.com/hiyouga/LLaMA-Factory/commits/main) +[![PyPI](https://img.shields.io/pypi/v/llmtuner)](https://pypi.org/project/llmtuner/) +[![Downloads](https://static.pepy.tech/badge/llmtuner)](https://pypi.org/project/llmtuner/) +[![GitHub pull request](https://img.shields.io/badge/PRs-welcome-blue)](https://github.com/hiyouga/LLaMA-Factory/pulls) +[![Discord](https://dcbadge.vercel.app/api/server/e73gccsSd?compact=true&style=flat)](https://discord.gg/e73gccsSd) + +👋 加入我们的[微信群](assets/wechat.jpg)。 + +\[ [English](README.md) | 中文 \] + +## LLaMA Board: 通过一站式网页界面快速上手 LLaMA Factory + +使用 `CUDA_VISIBLE_DEVICES=0 python src/train_web.py` 启动 **LLaMA Board**。(该界面目前仅支持单卡训练) + +下面是使用单张 GPU 在 10 分钟内更改对话式大型语言模型自我认知的示例。 + +https://github.com/hiyouga/LLaMA-Factory/assets/16256802/6ba60acc-e2e2-4bec-b846-2d88920d5ba1 + +## 更新日志 + +[23/10/21] 我们支持了 **[NEFTune](https://arxiv.org/abs/2310.05914)** 训练技巧。请使用 `--neft_alpha` 参数启用 NEFTune,例如 `--neft_alpha 5`。 + +[23/09/27] 我们针对 LLaMA 模型支持了 [LongLoRA](https://github.com/dvlab-research/LongLoRA) 提出的 **$S^2$-Attn**。请使用 `--shift_attn` 参数以启用该功能。 + +[23/09/23] 我们在项目中集成了 MMLU、C-Eval 和 CMMLU 评估集。使用方法请参阅[此示例](#模型评估)。 + +[23/09/10] 我们针对 LLaMA 模型支持了 **[FlashAttention-2](https://github.com/Dao-AILab/flash-attention)**。如果您使用的是 RTX4090、A100 或 H100 GPU,请使用 `--flash_attn` 参数以启用 FlashAttention-2。 + +[23/08/12] 我们支持了 **RoPE 插值**来扩展 LLaMA 模型的上下文长度。请使用 `--rope_scaling linear` 参数训练模型或使用 `--rope_scaling dynamic` 参数评估模型。 + +[23/08/11] 我们支持了指令模型的 **[DPO 训练](https://arxiv.org/abs/2305.18290)**。使用方法请参阅[此示例](#dpo-训练)。 + +[23/07/31] 我们支持了**数据流式加载**。请尝试使用 `--streaming` 和 `--max_steps 10000` 参数来流式加载数据集。 + +[23/07/29] 我们在 Hugging Face 发布了两个 13B 指令微调模型。详细内容请查阅我们的 Hugging Face 项目([LLaMA-2](https://huggingface.co/hiyouga/Llama-2-Chinese-13b-chat) / [Baichuan](https://huggingface.co/hiyouga/Baichuan-13B-sft))。 + +[23/07/18] 我们开发了支持训练和测试的**浏览器一体化界面**。请使用 `train_web.py` 在您的浏览器中微调模型。感谢 [@KanadeSiina](https://github.com/KanadeSiina) 和 [@codemayq](https://github.com/codemayq) 在该功能开发中付出的努力。 + +[23/07/09] 我们开源了 **[FastEdit](https://github.com/hiyouga/FastEdit)** ⚡🩹,一个简单易用的、能迅速编辑大模型事实记忆的工具包。如果您感兴趣请关注我们的 [FastEdit](https://github.com/hiyouga/FastEdit) 项目。 + +[23/06/29] 我们提供了一个**可复现的**指令模型微调示例,详细内容请查阅 [Baichuan-7B-sft](https://huggingface.co/hiyouga/Baichuan-7B-sft)。 + +[23/06/22] 我们对齐了[示例 API](src/api_demo.py) 与 [OpenAI API](https://platform.openai.com/docs/api-reference/chat) 的格式,您可以将微调模型接入**任意基于 ChatGPT 的应用**中。 + +[23/06/03] 我们实现了 4 比特的 LoRA 训练(也称 **[QLoRA](https://github.com/artidoro/qlora)**)。请使用 `--quantization_bit 4` 参数进行 4 比特量化微调。 + +## 模型 + +| 模型名 | 模型大小 | 默认模块 | Template | +| -------------------------------------------------------- | --------------------------- | ----------------- | --------- | +| [Baichuan](https://github.com/baichuan-inc/Baichuan-13B) | 7B/13B | W_pack | baichuan | +| [Baichuan2](https://github.com/baichuan-inc/Baichuan2) | 7B/13B | W_pack | baichuan2 | +| [BLOOM](https://huggingface.co/bigscience/bloom) | 560M/1.1B/1.7B/3B/7.1B/176B | query_key_value | - | +| [BLOOMZ](https://huggingface.co/bigscience/bloomz) | 560M/1.1B/1.7B/3B/7.1B/176B | query_key_value | - | +| [ChatGLM3](https://github.com/THUDM/ChatGLM3) | 6B | query_key_value | chatglm3 | +| [Falcon](https://huggingface.co/tiiuae/falcon-7b) | 7B/40B/180B | query_key_value | - | +| [InternLM](https://github.com/InternLM/InternLM) | 7B/20B | q_proj,v_proj | intern | +| [LLaMA](https://github.com/facebookresearch/llama) | 7B/13B/33B/65B | q_proj,v_proj | - | +| [LLaMA-2](https://huggingface.co/meta-llama) | 7B/13B/70B | q_proj,v_proj | llama2 | +| [Mistral](https://huggingface.co/mistralai) | 7B | q_proj,v_proj | mistral | +| [Phi-1.5](https://huggingface.co/microsoft/phi-1_5) | 1.3B | Wqkv | - | +| [Qwen](https://github.com/QwenLM/Qwen-7B) | 7B/14B | c_attn | qwen | +| [XVERSE](https://github.com/xverse-ai) | 7B/13B/65B | q_proj,v_proj | xverse | + +> [!NOTE] +> **默认模块**应作为 `--lora_target` 参数的默认值,可使用 `--lora_target all` 参数指定全部模块。 +> +> 对于所有“基座”(Base)模型,`--template` 参数可以是 `default`, `alpaca`, `vicuna` 等任意值。但“对话”(Chat)模型请务必使用**对应的模板**。 + +项目所支持模型的完整列表请参阅 [template.py](src/llmtuner/extras/template.py)。 + +## 训练方法 + +| 方法 | 全参数训练 | 部分参数训练 | LoRA | QLoRA | +| ---------------------- | ------------------ | ------------------ | ------------------ | ------------------ | +| 预训练 | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: | +| 指令监督微调 | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: | +| 奖励模型训练 | | | :white_check_mark: | :white_check_mark: | +| PPO 训练 | | | :white_check_mark: | :white_check_mark: | +| DPO 训练 | :white_check_mark: | | :white_check_mark: | :white_check_mark: | + +> [!NOTE] +> 请使用 `--quantization_bit 4/8` 参数来启用 QLoRA 训练。 + +## 数据集 + +
预训练数据集 + +- [Wiki Demo (en)](data/wiki_demo.txt) +- [RefinedWeb (en)](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) +- [RedPajama V2 (en)](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-V2) +- [Wikipedia (en)](https://huggingface.co/datasets/olm/olm-wikipedia-20221220) +- [Wikipedia (zh)](https://huggingface.co/datasets/pleisto/wikipedia-cn-20230720-filtered) +- [Pile (en)](https://huggingface.co/datasets/EleutherAI/pile) +- [SkyPile (zh)](https://huggingface.co/datasets/Skywork/SkyPile-150B) +- [The Stack (en)](https://huggingface.co/datasets/bigcode/the-stack) +- [StarCoder (en)](https://huggingface.co/datasets/bigcode/starcoderdata) + +
+ +
指令微调数据集 + +- [Stanford Alpaca (en)](https://github.com/tatsu-lab/stanford_alpaca) +- [Stanford Alpaca (zh)](https://github.com/ymcui/Chinese-LLaMA-Alpaca) +- [GPT-4 Generated Data (en&zh)](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM) +- [Self-cognition (zh)](data/self_cognition.json) +- [Open Assistant (multilingual)](https://huggingface.co/datasets/OpenAssistant/oasst1) +- [ShareGPT (zh)](https://huggingface.co/datasets/QingyiSi/Alpaca-CoT/tree/main/Chinese-instruction-collection) +- [Guanaco Dataset (multilingual)](https://huggingface.co/datasets/JosephusCheung/GuanacoDataset) +- [BELLE 2M (zh)](https://huggingface.co/datasets/BelleGroup/train_2M_CN) +- [BELLE 1M (zh)](https://huggingface.co/datasets/BelleGroup/train_1M_CN) +- [BELLE 0.5M (zh)](https://huggingface.co/datasets/BelleGroup/train_0.5M_CN) +- [BELLE Dialogue 0.4M (zh)](https://huggingface.co/datasets/BelleGroup/generated_chat_0.4M) +- [BELLE School Math 0.25M (zh)](https://huggingface.co/datasets/BelleGroup/school_math_0.25M) +- [BELLE Multiturn Chat 0.8M (zh)](https://huggingface.co/datasets/BelleGroup/multiturn_chat_0.8M) +- [UltraChat (en)](https://github.com/thunlp/UltraChat) +- [LIMA (en)](https://huggingface.co/datasets/GAIR/lima) +- [OpenPlatypus (en)](https://huggingface.co/datasets/garage-bAInd/Open-Platypus) +- [CodeAlpaca 20k (en)](https://huggingface.co/datasets/sahil2801/CodeAlpaca-20k) +- [Alpaca CoT (multilingual)](https://huggingface.co/datasets/QingyiSi/Alpaca-CoT) +- [MathInstruct (en)](https://huggingface.co/datasets/TIGER-Lab/MathInstruct) +- [Firefly 1.1M (zh)](https://huggingface.co/datasets/YeungNLP/firefly-train-1.1M) +- [Web QA (zh)](https://huggingface.co/datasets/suolyer/webqa) +- [WebNovel (zh)](https://huggingface.co/datasets/zxbsmk/webnovel_cn) +- [Ad Gen (zh)](https://huggingface.co/datasets/HasturOfficial/adgen) +- [ShareGPT Hyperfiltered (en)](https://huggingface.co/datasets/totally-not-an-llm/sharegpt-hyperfiltered-3k) +- [ShareGPT4 (en&zh)](https://huggingface.co/datasets/shibing624/sharegpt_gpt4) +- [UltraChat 200k (en)](https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k) +- [AgentInstruct (en)](https://huggingface.co/datasets/THUDM/AgentInstruct) +- [LMSYS Chat 1M (en)](https://huggingface.co/datasets/lmsys/lmsys-chat-1m) +- [Evol Instruct V2 (en)](https://huggingface.co/datasets/WizardLM/WizardLM_evol_instruct_V2_196k) + +
+ +
偏好数据集 + +- [HH-RLHF (en)](https://huggingface.co/datasets/Anthropic/hh-rlhf) +- [Open Assistant (multilingual)](https://huggingface.co/datasets/OpenAssistant/oasst1) +- [GPT-4 Generated Data (en&zh)](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM) + +
+ +使用方法请参考 [data/README_zh.md](data/README_zh.md) 文件。 + +部分数据集的使用需要确认,我们推荐使用下述命令登录您的 Hugging Face 账户。 + +```bash +pip install --upgrade huggingface_hub +huggingface-cli login +``` + +## 软件依赖 + +- Python 3.8+ 和 PyTorch 1.13.1+ +- 🤗Transformers, Datasets, Accelerate, PEFT 和 TRL +- sentencepiece, protobuf 和 tiktoken +- fire, jieba, rouge-chinese 和 nltk (用于评估及预测) +- gradio 和 matplotlib (用于网页端交互) +- uvicorn, fastapi 和 sse-starlette (用于 API) + +以及 **强而有力的 GPU**! + +## 如何使用 + +### 数据准备(可跳过) + +关于数据集文件的格式,请参考 [data/README_zh.md](data/README_zh.md) 的内容。构建自定义数据集时,既可以使用单个 `.json` 文件,也可以使用一个[数据加载脚本](https://huggingface.co/docs/datasets/dataset_script)和多个文件。 + +> [!NOTE] +> 使用自定义数据集时,请更新 `data/dataset_info.json` 文件,该文件的格式请参考 `data/README_zh.md`。 + +### 环境搭建(可跳过) + +```bash +git clone https://github.com/hiyouga/LLaMA-Factory.git +conda create -n llama_factory python=3.10 +conda activate llama_factory +cd LLaMA-Factory +pip install -r requirements.txt +``` + +如果要在 Windows 平台上开启量化 LoRA(QLoRA),需要安装预编译的 `bitsandbytes` 库, 支持 CUDA 11.1 到 12.1. + +```bash +pip install https://github.com/jllllll/bitsandbytes-windows-webui/releases/download/wheels/bitsandbytes-0.39.1-py3-none-win_amd64.whl +``` + +### 单 GPU 训练 + +> [!IMPORTANT] +> 如果您使用多张 GPU 训练模型,请移步[多 GPU 分布式训练](#多-gpu-分布式训练)部分。 + +#### 预训练 + +```bash +CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \ + --stage pt \ + --model_name_or_path path_to_llama_model \ + --do_train \ + --dataset wiki_demo \ + --finetuning_type lora \ + --lora_target q_proj,v_proj \ + --output_dir path_to_pt_checkpoint \ + --overwrite_cache \ + --per_device_train_batch_size 4 \ + --gradient_accumulation_steps 4 \ + --lr_scheduler_type cosine \ + --logging_steps 10 \ + --save_steps 1000 \ + --learning_rate 5e-5 \ + --num_train_epochs 3.0 \ + --plot_loss \ + --fp16 +``` + +#### 指令监督微调 + +```bash +CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \ + --stage sft \ + --model_name_or_path path_to_llama_model \ + --do_train \ + --dataset alpaca_gpt4_zh \ + --template default \ + --finetuning_type lora \ + --lora_target q_proj,v_proj \ + --output_dir path_to_sft_checkpoint \ + --overwrite_cache \ + --per_device_train_batch_size 4 \ + --gradient_accumulation_steps 4 \ + --lr_scheduler_type cosine \ + --logging_steps 10 \ + --save_steps 1000 \ + --learning_rate 5e-5 \ + --num_train_epochs 3.0 \ + --plot_loss \ + --fp16 +``` + +#### 奖励模型训练 + +```bash +CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \ + --stage rm \ + --model_name_or_path path_to_llama_model \ + --do_train \ + --dataset comparison_gpt4_zh \ + --template default \ + --finetuning_type lora \ + --lora_target q_proj,v_proj \ + --resume_lora_training False \ + --checkpoint_dir path_to_sft_checkpoint \ + --output_dir path_to_rm_checkpoint \ + --per_device_train_batch_size 2 \ + --gradient_accumulation_steps 4 \ + --lr_scheduler_type cosine \ + --logging_steps 10 \ + --save_steps 1000 \ + --learning_rate 1e-6 \ + --num_train_epochs 1.0 \ + --plot_loss \ + --fp16 +``` + +#### PPO 训练 + +```bash +CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \ + --stage ppo \ + --model_name_or_path path_to_llama_model \ + --do_train \ + --dataset alpaca_gpt4_zh \ + --template default \ + --finetuning_type lora \ + --lora_target q_proj,v_proj \ + --resume_lora_training False \ + --checkpoint_dir path_to_sft_checkpoint \ + --reward_model path_to_rm_checkpoint \ + --output_dir path_to_ppo_checkpoint \ + --per_device_train_batch_size 2 \ + --gradient_accumulation_steps 4 \ + --lr_scheduler_type cosine \ + --logging_steps 10 \ + --save_steps 1000 \ + --learning_rate 1e-5 \ + --num_train_epochs 1.0 \ + --plot_loss +``` + +#### DPO 训练 + +```bash +CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \ + --stage dpo \ + --model_name_or_path path_to_llama_model \ + --do_train \ + --dataset comparison_gpt4_zh \ + --template default \ + --finetuning_type lora \ + --lora_target q_proj,v_proj \ + --resume_lora_training False \ + --checkpoint_dir path_to_sft_checkpoint \ + --output_dir path_to_dpo_checkpoint \ + --per_device_train_batch_size 2 \ + --gradient_accumulation_steps 4 \ + --lr_scheduler_type cosine \ + --logging_steps 10 \ + --save_steps 1000 \ + --learning_rate 1e-5 \ + --num_train_epochs 1.0 \ + --plot_loss \ + --fp16 +``` + +### 多 GPU 分布式训练 + +#### 使用 Huggingface Accelerate + +```bash +accelerate config # 首先配置分布式环境 +accelerate launch src/train_bash.py # 参数同上 +``` + +
LoRA 训练的 Accelerate 配置示例 + +```yaml +compute_environment: LOCAL_MACHINE +distributed_type: MULTI_GPU +downcast_bf16: 'no' +gpu_ids: all +machine_rank: 0 +main_training_function: main +mixed_precision: fp16 +num_machines: 1 +num_processes: 4 +rdzv_backend: static +same_network: true +tpu_env: [] +tpu_use_cluster: false +tpu_use_sudo: false +use_cpu: false +``` + +
+ +#### 使用 DeepSpeed + +```bash +deepspeed --num_gpus 8 --master_port=9901 src/train_bash.py \ + --deepspeed ds_config.json \ + ... # 参数同上 +``` + +
使用 DeepSpeed ZeRO-2 进行全参数训练的 DeepSpeed 配置示例 + +```json +{ + "train_batch_size": "auto", + "train_micro_batch_size_per_gpu": "auto", + "gradient_accumulation_steps": "auto", + "gradient_clipping": "auto", + "zero_allow_untested_optimizer": true, + "fp16": { + "enabled": "auto", + "loss_scale": 0, + "initial_scale_power": 16, + "loss_scale_window": 1000, + "hysteresis": 2, + "min_loss_scale": 1 + }, + "zero_optimization": { + "stage": 2, + "allgather_partitions": true, + "allgather_bucket_size": 5e8, + "reduce_scatter": true, + "reduce_bucket_size": 5e8, + "overlap_comm": false, + "contiguous_gradients": true + } +} +``` + +
+ +### 导出微调后的完整模型 + +```bash +python src/export_model.py \ + --model_name_or_path path_to_llama_model \ + --template default \ + --finetuning_type lora \ + --checkpoint_dir path_to_checkpoint \ + --export_dir path_to_export +``` + +### API 服务 + +```bash +python src/api_demo.py \ + --model_name_or_path path_to_llama_model \ + --template default \ + --finetuning_type lora \ + --checkpoint_dir path_to_checkpoint +``` + +> [!NOTE] +> 关于 API 文档请见 `http://localhost:8000/docs`。 + +### 命令行测试 + +```bash +python src/cli_demo.py \ + --model_name_or_path path_to_llama_model \ + --template default \ + --finetuning_type lora \ + --checkpoint_dir path_to_checkpoint +``` + +### 浏览器测试 + +```bash +python src/web_demo.py \ + --model_name_or_path path_to_llama_model \ + --template default \ + --finetuning_type lora \ + --checkpoint_dir path_to_checkpoint +``` + +### 模型评估 + +```bash +CUDA_VISIBLE_DEVICES=0 python src/evaluate.py \ + --model_name_or_path path_to_llama_model \ + --finetuning_type lora \ + --checkpoint_dir path_to_checkpoint \ + --template vanilla \ + --task ceval \ + --split validation \ + --lang zh \ + --n_shot 5 \ + --batch_size 4 +``` + +### 模型预测 + +```bash +CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \ + --stage sft \ + --model_name_or_path path_to_llama_model \ + --do_predict \ + --dataset alpaca_gpt4_zh \ + --template default \ + --finetuning_type lora \ + --checkpoint_dir path_to_checkpoint \ + --output_dir path_to_predict_result \ + --per_device_eval_batch_size 8 \ + --max_samples 100 \ + --predict_with_generate +``` + +> [!NOTE] +> 我们建议在量化模型的预测中使用 `--per_device_eval_batch_size=1` 和 `--max_target_length 128`。 + +## 使用了 LLaMA Factory 的项目 + +- **[StarWhisper](https://github.com/Yu-Yang-Li/StarWhisper)**: 天文大模型 StarWhisper,基于 ChatGLM2-6B 和 Qwen-14B 在天文数据上微调而得。 +- **[DISC-LawLLM](https://github.com/FudanDISC/DISC-LawLLM)**: 中文法律领域大模型 DISC-LawLLM,基于 Baichuan-13B 微调而得,具有法律推理和知识检索能力。 +- **[Sunsimiao](https://github.com/thomas-yanxin/Sunsimiao)**: 孙思邈中文医疗大模型 Sumsimiao,基于 Baichuan-7B 和 ChatGLM-6B 在中文医疗数据上微调而得。 +- **[CareGPT](https://github.com/WangRongsheng/CareGPT)**: 医疗大模型项目 CareGPT,基于 LLaMA2-7B 和 Baichuan-13B 在中文医疗数据上微调而得。 + +## 协议 + +本仓库的代码依照 [Apache-2.0](LICENSE) 协议开源。 + +使用模型权重时,请遵循对应的模型协议:[Baichuan](https://huggingface.co/baichuan-inc/Baichuan-13B-Base/resolve/main/Community%20License%20for%20Baichuan-13B%20Model.pdf) / [Baichuan2](https://huggingface.co/baichuan-inc/Baichuan2-13B-Chat/resolve/main/Community%20License%20for%20Baichuan2%20Model.pdf) / [BLOOM](https://huggingface.co/spaces/bigscience/license) / [ChatGLM3](https://github.com/THUDM/ChatGLM3/blob/main/MODEL_LICENSE) / [Falcon](https://huggingface.co/tiiuae/falcon-180B/blob/main/LICENSE.txt) / [InternLM](https://github.com/InternLM/InternLM#license) / [LLaMA](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) / [LLaMA-2](https://ai.meta.com/llama/license/) / [Mistral](LICENSE) / [Phi-1.5](https://huggingface.co/microsoft/phi-1_5/resolve/main/Research%20License.docx) / [Qwen](https://huggingface.co/Qwen/Qwen-7B-Chat/blob/main/LICENSE) / [XVERSE](https://github.com/xverse-ai/XVERSE-13B/blob/main/MODEL_LICENSE.pdf) + +## 引用 + +如果您觉得此项目有帮助,请考虑以下列格式引用 + +```bibtex +@Misc{llama-factory, + title = {LLaMA Factory}, + author = {hiyouga}, + howpublished = {\url{https://github.com/hiyouga/LLaMA-Factory}}, + year = {2023} +} +``` + +## 致谢 + +本项目受益于 [PEFT](https://github.com/huggingface/peft)、[QLoRA](https://github.com/artidoro/qlora) 和 [FastChat](https://github.com/lm-sys/FastChat),感谢以上诸位作者的付出。 + +## Star History + +![Star History Chart](https://api.star-history.com/svg?repos=hiyouga/LLaMA-Factory&type=Date) diff --git a/llm_rl/assets/wechat.jpg b/llm_rl/assets/wechat.jpg new file mode 100644 index 0000000000000000000000000000000000000000..8df68f9cbab8eda4a380b4f866adb0a569505aec GIT binary patch literal 143444 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z4ZS4|?%i}7bJ?`(G7|c;y}!tUqT7r6sWgVhE?dgI*r9Ych`~J@i~ELO5I#<3&I~&+ zsFp~|jkKOExObgvciXnjesh>=({fBUw{_s{0PPXrbtk!I{v(cPL;L1&cKLSUD#Di! z0XuB*9y}KV|FHI77ykQq{_wr`|6F8ICZ`MsVPS0bH`002w#ijg=#1{i4|timkZyh> z;RN|;KEVGPuYwIXGZ;uf4)!zqqAS~_B;JEFe?6$y+`ne?$1z7yYZ}-@EjQh)L&8k| z9=r>5+GAitPyz2${MmX-I>4BOKLKJGetn5G?cSeyBO<^D+5K!ox3P#8uCmkza-3g*|XJKME7Tw^kcr%4C|;hmx&jS{IZj09vsVmi=3D3yAsg z7AnZ!-K8+6oF)~29J;7zrYC~cs>smnemDK(?UT;l*vre}J9jkk^Q7|kd0UeAccax- z$wJ?jHA>^S3JvwgE@&m_yi_(&`%pRSltV8DXM9Q#6#c91_^MLD(yHX>_d1*u0jd}J za5_27v5HJvE-OD->r0zZIC93fa&~(_(Cq)I2!%GR$cmJIuBUOF*mvE#3c+`thnR9^ zrj_2<2pQM-kav6VQdv!>+UdNuT7Wl>fPL(AnTr*Y$(AIv>R~!ieIR2d9$j7Eyu>Vr z3~r^~OC}Kw_}TG8wMS6q9aW*?pNNZGjNpv%^m+vWg8>uN!BN69zBTmWTe6F*A0gjF z(i+Vz15U8>f5pE!kjD6`$N*R&AB**xjY$gs7S74cbFFH^HC?%2ExZBQ643t=m51-E z`%V@Mvf+&|{Z*3|JbS~ZW7U0#>$U^pB_( datasets.DatasetInfo: + features = datasets.Features({ + "instruction": datasets.Value("string"), + "output": datasets.Value("string"), + "history": datasets.Sequence(datasets.Sequence(datasets.Value("string"))) + }) + return datasets.DatasetInfo( + description=_DESCRIPTION, + features=features, + homepage=_HOMEPAGE, + license=_LICENSE, + citation=_CITATION + ) + + def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]: + file_path = dl_manager.download(_URL) + return [ + datasets.SplitGenerator( + name=datasets.Split.TRAIN, + gen_kwargs={ + "filepath": file_path + } + ) + ] + + def _generate_examples(self, filepath: str) -> Dict[int, Dict[str, Any]]: # generate multi-turn chat with history + with open(filepath, "r", encoding="utf-8") as f: + for key, row in enumerate(f): + data = json.loads(row) + prompt = data["instruction"].strip() + response = data["output"].strip() + + assist_idx = prompt.rfind("Assistant:") + human_idx = prompt.rfind("Human:") + query = prompt[human_idx+6:assist_idx].strip() + prompt = prompt[:human_idx].strip() + history = [] + + while prompt.rfind("Assistant:") != -1: + assist_idx = prompt.rfind("Assistant:") + human_idx = prompt.rfind("Human:") + if human_idx != -1: + old_query = prompt[human_idx+6:assist_idx].strip() + old_resp = prompt[assist_idx+10:].strip() + history.insert(0, (old_query, old_resp)) + else: + break + prompt = prompt[:human_idx].strip() + + yield key, { + "instruction": query, + "output": response, + "history": history + } diff --git a/llm_rl/data/example_dataset/example_dataset.py b/llm_rl/data/example_dataset/example_dataset.py new file mode 100644 index 00000000..db3e9ffb --- /dev/null +++ b/llm_rl/data/example_dataset/example_dataset.py @@ -0,0 +1,46 @@ +import json +import datasets +from typing import Any, Dict, List + + +_DESCRIPTION = "An example of dataset for LLaMA." +_CITATION = "" +_HOMEPAGE = "" +_LICENSE = "" +_URL = "examples.json" + + +class ExampleDataset(datasets.GeneratorBasedBuilder): + + VERSION = datasets.Version("0.0.0") + + def _info(self) -> datasets.DatasetInfo: + features = datasets.Features({ + "instruction": datasets.Value("string"), + "input": datasets.Value("string"), + "output": datasets.Value("string"), + "history": datasets.Sequence(datasets.Sequence(datasets.Value("string"))) + }) + return datasets.DatasetInfo( + description=_DESCRIPTION, + features=features, + homepage=_HOMEPAGE, + license=_LICENSE, + citation=_CITATION + ) + + def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]: + file_path = dl_manager.download(_URL) + return [ + datasets.SplitGenerator( + name=datasets.Split.TRAIN, + gen_kwargs={ + "filepath": file_path + } + ) + ] + + def _generate_examples(self, filepath: str) -> Dict[int, Dict[str, Any]]: + example_dataset = json.load(open(filepath, "r", encoding="utf-8")) + for key, example in enumerate(example_dataset): + yield key, example diff --git a/llm_rl/data/hh_rlhf_en/hh_rlhf_en.py b/llm_rl/data/hh_rlhf_en/hh_rlhf_en.py new file mode 100644 index 00000000..8d51e4c4 --- /dev/null +++ b/llm_rl/data/hh_rlhf_en/hh_rlhf_en.py @@ -0,0 +1,97 @@ +import json +import datasets +from typing import Any, Dict, List + + +_DESCRIPTION = "Human preference data about helpfulness and harmlessness for ChatGLM." +_CITATION = "" +_HOMEPAGE = "https://huggingface.co/datasets/Anthropic/hh-rlhf" +_LICENSE = "mit" +_URL = "https://huggingface.co/datasets/Anthropic/hh-rlhf/resolve/main/" +_URLS = { + "train": [ + _URL + "harmless-base/train.jsonl.gz", + _URL + "helpful-base/train.jsonl.gz", + _URL + "helpful-online/train.jsonl.gz", + _URL + "helpful-rejection-sampled/train.jsonl.gz" + ], + "test": [ + _URL + "harmless-base/test.jsonl.gz", + _URL + "helpful-base/test.jsonl.gz", + _URL + "helpful-online/test.jsonl.gz", + _URL + "helpful-rejection-sampled/test.jsonl.gz" + ] +} + + +class HhRlhfEn(datasets.GeneratorBasedBuilder): + + VERSION = datasets.Version("0.0.0") + + def _info(self) -> datasets.DatasetInfo: + features = datasets.Features({ + "instruction": datasets.Value("string"), + "output": datasets.Sequence(datasets.Value("string")), + "history": datasets.Sequence(datasets.Sequence(datasets.Value("string"))) + }) + return datasets.DatasetInfo( + description=_DESCRIPTION, + features=features, + homepage=_HOMEPAGE, + license=_LICENSE, + citation=_CITATION + ) + + def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]: + file_path = dl_manager.download_and_extract(_URLS) + return [ + datasets.SplitGenerator( + name=datasets.Split.TRAIN, + gen_kwargs={ + "filepaths": file_path["train"] + } + ), + datasets.SplitGenerator( + name=datasets.Split.TEST, + gen_kwargs={ + "filepaths": file_path["test"] + } + ) + ] + + def _generate_examples(self, filepaths: List[str]) -> Dict[int, Dict[str, Any]]: # generate multi-turn chat for ChatGLM + key = 0 + for filepath in filepaths: + with open(filepath, "r", encoding="utf-8") as f: + for row in f: + data = json.loads(row) + chosen = data["chosen"] + rejected = data["rejected"] + + assist_idx = rejected.rfind("\n\nAssistant: ") + r_reject = rejected[assist_idx+13:].strip() + assist_idx = chosen.rfind("\n\nAssistant: ") + r_accept = chosen[assist_idx+13:].strip() + + human_idx = chosen.rfind("\n\nHuman: ") + query = chosen[human_idx+9:assist_idx].strip() + prompt = chosen[:human_idx] + history = [] + + while prompt.rfind("\n\nAssistant: ") != -1: + assist_idx = prompt.rfind("\n\nAssistant: ") + human_idx = prompt.rfind("\n\nHuman: ") + if human_idx != -1: + old_query = prompt[human_idx+9:assist_idx].strip() + old_resp = prompt[assist_idx+13:].strip() + history.insert(0, (old_query, old_resp)) + else: + break + prompt = prompt[:human_idx] + + yield key, { + "instruction": query, + "output": [r_accept, r_reject], + "history": history + } + key += 1 diff --git a/llm_rl/data/ultra_chat/ultra_chat.py b/llm_rl/data/ultra_chat/ultra_chat.py new file mode 100644 index 00000000..dd29311c --- /dev/null +++ b/llm_rl/data/ultra_chat/ultra_chat.py @@ -0,0 +1,76 @@ +import json +import datasets +from typing import Any, Dict, List + + +_DESCRIPTION = "UltraChat: Large-scale, Informative, and Diverse Multi-round Dialogue Data." + +_CITATION = """\ +@misc{UltraChat, + author = {Ding, Ning and Chen, Yulin and Xu, Bokai and Hu, Shengding and Qin, Yujia and Liu, Zhiyuan and Sun, Maosong and Zhou, Bowen}, + title = {UltraChat: A Large-scale Auto-generated Multi-round Dialogue Data}, + year = {2023}, + publisher = {GitHub}, + journal = {GitHub repository}, + howpublished = {\\url{https://github.com/thunlp/ultrachat}}, +} +""" + +_HOMEPAGE = "https://huggingface.co/datasets/stingning/ultrachat" +_LICENSE = "cc-by-nc-4.0" +_BASE_DATA_URL = "https://huggingface.co/datasets/stingning/ultrachat/resolve/main/train_{idx}.jsonl" + + +class BelleMultiturn(datasets.GeneratorBasedBuilder): + + VERSION = datasets.Version("0.0.0") + + def _info(self) -> datasets.DatasetInfo: + features = datasets.Features({ + "instruction": datasets.Value("string"), + "output": datasets.Value("string"), + "history": datasets.Sequence(datasets.Sequence(datasets.Value("string"))) + }) + return datasets.DatasetInfo( + description=_DESCRIPTION, + features=features, + homepage=_HOMEPAGE, + license=_LICENSE, + citation=_CITATION + ) + + def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]: + file_paths = [dl_manager.download(_BASE_DATA_URL.format(idx=idx)) for idx in range(9)] # multiple shards + return [ + datasets.SplitGenerator( + name=datasets.Split.TRAIN, + gen_kwargs={ + "filepaths": file_paths + } + ) + ] + + def _generate_examples(self, filepaths: List[str]) -> Dict[int, Dict[str, Any]]: # generate multi-turn chat for ChatGLM + for filepath in filepaths: + with open(filepath, "r", encoding="utf-8") as f: + for row in f: + try: + data = json.loads(row) + except: + continue + key = data["id"] + content = data["data"] + if len(content) % 2 == 1: + content.pop(-1) + if len(content) < 2: + continue + + query = content[-2] + response = content[-1] + history = [[content[2*i], content[2*i+1]] for i in range(len(content) // 2 - 1)] + + yield key, { + "instruction": query, + "output": response, + "history": history + } diff --git a/llm_rl/data/wiki_demo.txt b/llm_rl/data/wiki_demo.txt new file mode 100644 index 00000000..4b6fd2b0 --- /dev/null +++ b/llm_rl/data/wiki_demo.txt @@ -0,0 +1,50 @@ +Machine learning (ML) is a field devoted to understanding and building methods that let machines "learn" – that is, methods that leverage data to improve computer performance on some set of tasks. +Machine learning algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as in medicine, email filtering, speech recognition, agriculture, and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks. +A subset of machine learning is closely related to computational statistics, which focuses on making predictions using computers, but not all machine learning is statistical learning. The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning. Data mining is a related field of study, focusing on exploratory data analysis through unsupervised learning. +Some implementations of machine learning use data and neural networks in a way that mimics the working of a biological brain. +In its application across business problems, machine learning is also referred to as predictive analytics. +Learning algorithms work on the basis that strategies, algorithms, and inferences that worked well in the past are likely to continue working well in the future. These inferences can sometimes be obvious, such as "since the sun rose every morning for the last 10,000 days, it will probably rise tomorrow morning as well". Other times, they can be more nuanced, such as "X% of families have geographically separate species with color variants, so there is a Y% chance that undiscovered black swans exist". +Machine learning programs can perform tasks without being explicitly programmed to do so. It involves computers learning from data provided so that they carry out certain tasks. For simple tasks assigned to computers, it is possible to program algorithms telling the machine how to execute all steps required to solve the problem at hand; on the computer's part, no learning is needed. For more advanced tasks, it can be challenging for a human to manually create the needed algorithms. In practice, it can turn out to be more effective to help the machine develop its own algorithm, rather than having human programmers specify every needed step. +The discipline of machine learning employs various approaches to teach computers to accomplish tasks where no fully satisfactory algorithm is available. In cases where vast numbers of potential answers exist, one approach is to label some of the correct answers as valid. This can then be used as training data for the computer to improve the algorithm(s) it uses to determine correct answers. For example, to train a system for the task of digital character recognition, the MNIST dataset of handwritten digits has often been used. +The term machine learning was coined in 1959 by Arthur Samuel, an IBM employee and pioneer in the field of computer gaming and artificial intelligence. The synonym self-teaching computers was also used in this time period. +By the early 1960s an experimental "learning machine" with punched tape memory, called Cybertron, had been developed by Raytheon Company to analyze sonar signals, electrocardiograms, and speech patterns using rudimentary reinforcement learning. It was repetitively "trained" by a human operator/teacher to recognize patterns and equipped with a "goof" button to cause it to re-evaluate incorrect decisions. A representative book on research into machine learning during the 1960s was Nilsson's book on Learning Machines, dealing mostly with machine learning for pattern classification. Interest related to pattern recognition continued into the 1970s, as described by Duda and Hart in 1973. In 1981 a report was given on using teaching strategies so that a neural network learns to recognize 40 characters (26 letters, 10 digits, and 4 special symbols) from a computer terminal. +Tom M. Mitchell provided a widely quoted, more formal definition of the algorithms studied in the machine learning field: "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E." This definition of the tasks in which machine learning is concerned offers a fundamentally operational definition rather than defining the field in cognitive terms. This follows Alan Turing's proposal in his paper "Computing Machinery and Intelligence", in which the question "Can machines think?" is replaced with the question "Can machines do what we (as thinking entities) can do?". +Modern-day machine learning has two objectives, one is to classify data based on models which have been developed, the other purpose is to make predictions for future outcomes based on these models. A hypothetical algorithm specific to classifying data may use computer vision of moles coupled with supervised learning in order to train it to classify the cancerous moles. A machine learning algorithm for stock trading may inform the trader of future potential predictions. +As a scientific endeavor, machine learning grew out of the quest for artificial intelligence (AI). In the early days of AI as an academic discipline, some researchers were interested in having machines learn from data. They attempted to approach the problem with various symbolic methods, as well as what were then termed "neural networks"; these were mostly perceptrons and other models that were later found to be reinventions of the generalized linear models of statistics. Probabilistic reasoning was also employed, especially in automated medical diagnosis.: 488  +However, an increasing emphasis on the logical, knowledge-based approach caused a rift between AI and machine learning. Probabilistic systems were plagued by theoretical and practical problems of data acquisition and representation.: 488  By 1980, expert systems had come to dominate AI, and statistics was out of favor. Work on symbolic/knowledge-based learning did continue within AI, leading to inductive logic programming, but the more statistical line of research was now outside the field of AI proper, in pattern recognition and information retrieval.: 708–710, 755  Neural networks research had been abandoned by AI and computer science around the same time. This line, too, was continued outside the AI/CS field, as "connectionism", by researchers from other disciplines including Hopfield, Rumelhart, and Hinton. Their main success came in the mid-1980s with the reinvention of backpropagation.: 25  +Machine learning (ML), reorganized and recognized as its own field, started to flourish in the 1990s. The field changed its goal from achieving artificial intelligence to tackling solvable problems of a practical nature. It shifted focus away from the symbolic approaches it had inherited from AI, and toward methods and models borrowed from statistics, fuzzy logic, and probability theory. +Machine learning and data mining often employ the same methods and overlap significantly, but while machine learning focuses on prediction, based on known properties learned from the training data, data mining focuses on the discovery of (previously) unknown properties in the data (this is the analysis step of knowledge discovery in databases). Data mining uses many machine learning methods, but with different goals; on the other hand, machine learning also employs data mining methods as "unsupervised learning" or as a preprocessing step to improve learner accuracy. Much of the confusion between these two research communities (which do often have separate conferences and separate journals, ECML PKDD being a major exception) comes from the basic assumptions they work with: in machine learning, performance is usually evaluated with respect to the ability to reproduce known knowledge, while in knowledge discovery and data mining (KDD) the key task is the discovery of previously unknown knowledge. Evaluated with respect to known knowledge, an uninformed (unsupervised) method will easily be outperformed by other supervised methods, while in a typical KDD task, supervised methods cannot be used due to the unavailability of training data. +Machine learning also has intimate ties to optimization: many learning problems are formulated as minimization of some loss function on a training set of examples. Loss functions express the discrepancy between the predictions of the model being trained and the actual problem instances (for example, in classification, one wants to assign a label to instances, and models are trained to correctly predict the pre-assigned labels of a set of examples). +The difference between optimization and machine learning arises from the goal of generalization: while optimization algorithms can minimize the loss on a training set, machine learning is concerned with minimizing the loss on unseen samples. Characterizing the generalization of various learning algorithms is an active topic of current research, especially for deep learning algorithms. +Machine learning and statistics are closely related fields in terms of methods, but distinct in their principal goal: statistics draws population inferences from a sample, while machine learning finds generalizable predictive patterns. According to Michael I. Jordan, the ideas of machine learning, from methodological principles to theoretical tools, have had a long pre-history in statistics. He also suggested the term data science as a placeholder to call the overall field. +Leo Breiman distinguished two statistical modeling paradigms: data model and algorithmic model, wherein "algorithmic model" means more or less the machine learning algorithms like Random Forest. +Some statisticians have adopted methods from machine learning, leading to a combined field that they call statistical learning. +Analytical and computational techniques derived from deep-rooted physics of disordered systems can be extended to large-scale problems, including machine learning, e.g., to analyze the weight space of deep neural networks. Statistical physics is thus finding applications in the area of medical diagnostics. +A core objective of a learner is to generalize from its experience. Generalization in this context is the ability of a learning machine to perform accurately on new, unseen examples/tasks after having experienced a learning data set. The training examples come from some generally unknown probability distribution (considered representative of the space of occurrences) and the learner has to build a general model about this space that enables it to produce sufficiently accurate predictions in new cases. +The computational analysis of machine learning algorithms and their performance is a branch of theoretical computer science known as computational learning theory via the Probably Approximately Correct Learning (PAC) model. Because training sets are finite and the future is uncertain, learning theory usually does not yield guarantees of the performance of algorithms. Instead, probabilistic bounds on the performance are quite common. The bias–variance decomposition is one way to quantify generalization error. +For the best performance in the context of generalization, the complexity of the hypothesis should match the complexity of the function underlying the data. If the hypothesis is less complex than the function, then the model has under fitted the data. If the complexity of the model is increased in response, then the training error decreases. But if the hypothesis is too complex, then the model is subject to overfitting and generalization will be poorer. +In addition to performance bounds, learning theorists study the time complexity and feasibility of learning. In computational learning theory, a computation is considered feasible if it can be done in polynomial time. There are two kinds of time complexity results: Positive results show that a certain class of functions can be learned in polynomial time. Negative results show that certain classes cannot be learned in polynomial time. +Machine learning approaches are traditionally divided into three broad categories, which correspond to learning paradigms, depending on the nature of the "signal" or "feedback" available to the learning system: +Supervised learning: The computer is presented with example inputs and their desired outputs, given by a "teacher", and the goal is to learn a general rule that maps inputs to outputs. +Unsupervised learning: No labels are given to the learning algorithm, leaving it on its own to find structure in its input. Unsupervised learning can be a goal in itself (discovering hidden patterns in data) or a means towards an end (feature learning). +Reinforcement learning: A computer program interacts with a dynamic environment in which it must perform a certain goal (such as driving a vehicle or playing a game against an opponent). As it navigates its problem space, the program is provided feedback that's analogous to rewards, which it tries to maximize. Although each algorithm has advantages and limitations, no single algorithm works for all problems. +Supervised learning algorithms build a mathematical model of a set of data that contains both the inputs and the desired outputs. The data is known as training data, and consists of a set of training examples. Each training example has one or more inputs and the desired output, also known as a supervisory signal. In the mathematical model, each training example is represented by an array or vector, sometimes called a feature vector, and the training data is represented by a matrix. Through iterative optimization of an objective function, supervised learning algorithms learn a function that can be used to predict the output associated with new inputs. An optimal function will allow the algorithm to correctly determine the output for inputs that were not a part of the training data. An algorithm that improves the accuracy of its outputs or predictions over time is said to have learned to perform that task. +Types of supervised-learning algorithms include active learning, classification and regression. Classification algorithms are used when the outputs are restricted to a limited set of values, and regression algorithms are used when the outputs may have any numerical value within a range. As an example, for a classification algorithm that filters emails, the input would be an incoming email, and the output would be the name of the folder in which to file the email. +Similarity learning is an area of supervised machine learning closely related to regression and classification, but the goal is to learn from examples using a similarity function that measures how similar or related two objects are. It has applications in ranking, recommendation systems, visual identity tracking, face verification, and speaker verification. +Unsupervised learning algorithms take a set of data that contains only inputs, and find structure in the data, like grouping or clustering of data points. The algorithms, therefore, learn from test data that has not been labeled, classified or categorized. Instead of responding to feedback, unsupervised learning algorithms identify commonalities in the data and react based on the presence or absence of such commonalities in each new piece of data. A central application of unsupervised learning is in the field of density estimation in statistics, such as finding the probability density function. Though unsupervised learning encompasses other domains involving summarizing and explaining data features. Unsupervised learning algorithms streamlined the process of survey and graph large indel based haplotypes of a gene of interest from pan-genome. +Cluster analysis is the assignment of a set of observations into subsets (called clusters) so that observations within the same cluster are similar according to one or more predesignated criteria, while observations drawn from different clusters are dissimilar. Different clustering techniques make different assumptions on the structure of the data, often defined by some similarity metric and evaluated, for example, by internal compactness, or the similarity between members of the same cluster, and separation, the difference between clusters. Other methods are based on estimated density and graph connectivity. +Semi-supervised learning falls between unsupervised learning (without any labeled training data) and supervised learning (with completely labeled training data). Some of the training examples are missing training labels, yet many machine-learning researchers have found that unlabeled data, when used in conjunction with a small amount of labeled data, can produce a considerable improvement in learning accuracy. +In weakly supervised learning, the training labels are noisy, limited, or imprecise; however, these labels are often cheaper to obtain, resulting in larger effective training sets. +Reinforcement learning is an area of machine learning concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward. Due to its generality, the field is studied in many other disciplines, such as game theory, control theory, operations research, information theory, simulation-based optimization, multi-agent systems, swarm intelligence, statistics and genetic algorithms. In machine learning, the environment is typically represented as a Markov decision process (MDP). Many reinforcements learning algorithms use dynamic programming techniques. Reinforcement learning algorithms do not assume knowledge of an exact mathematical model of the MDP and are used when exact models are infeasible. Reinforcement learning algorithms are used in autonomous vehicles or in learning to play a game against a human opponent. +Dimensionality reduction is a process of reducing the number of random variables under consideration by obtaining a set of principal variables. In other words, it is a process of reducing the dimension of the feature set, also called the "number of features". Most of the dimensionality reduction techniques can be considered as either feature elimination or extraction. One of the popular methods of dimensionality reduction is principal component analysis (PCA). PCA involves changing higher-dimensional data (e.g., 3D) to a smaller space (e.g., 2D). This results in a smaller dimension of data (2D instead of 3D), while keeping all original variables in the model without changing the data. The manifold hypothesis proposes that high-dimensional data sets lie along low-dimensional manifolds, and many dimensionality reduction techniques make this assumption, leading to the area of manifold learning and manifold regularization. +Although machine learning has been transformative in some fields, machine-learning programs often fail to deliver expected results. Reasons for this are numerous: lack of (suitable) data, lack of access to the data, data bias, privacy problems, badly chosen tasks and algorithms, wrong tools and people, lack of resources, and evaluation problems. +In 2018, a self-driving car from Uber failed to detect a pedestrian, who was killed after a collision. Attempts to use machine learning in healthcare with the IBM Watson system failed to deliver even after years of time and billions of dollars invested. +Machine learning has been used as a strategy to update the evidence related to a systematic review and increased reviewer burden related to the growth of biomedical literature. While it has improved with training sets, it has not yet developed sufficiently to reduce the workload burden without limiting the necessary sensitivity for the findings research themselves. +Machine learning approaches in particular can suffer from different data biases. A machine learning system trained specifically on current customers may not be able to predict the needs of new customer groups that are not represented in the training data. When trained on human-made data, machine learning is likely to pick up the constitutional and unconscious biases already present in society. Language models learned from data have been shown to contain human-like biases. Machine learning systems used for criminal risk assessment have been found to be biased against black people. In 2015, Google photos would often tag black people as gorillas, and in 2018 this still was not well resolved, but Google reportedly was still using the workaround to remove all gorillas from the training data, and thus was not able to recognize real gorillas at all. Similar issues with recognizing non-white people have been found in many other systems. In 2016, Microsoft tested a chatbot that learned from Twitter, and it quickly picked up racist and sexist language. Because of such challenges, the effective use of machine learning may take longer to be adopted in other domains. Concern for fairness in machine learning, that is, reducing bias in machine learning and propelling its use for human good is increasingly expressed by artificial intelligence scientists, including Fei-Fei Li, who reminds engineers that "There's nothing artificial about AI...It's inspired by people, it's created by people, and—most importantly—it impacts people. It is a powerful tool we are only just beginning to understand, and that is a profound responsibility." +Learners can also disappoint by "learning the wrong lesson". A toy example is that an image classifier trained only on pictures of brown horses and black cats might conclude that all brown patches are likely to be horses. A real-world example is that, unlike humans, current image classifiers often do not primarily make judgments from the spatial relationship between components of the picture, and they learn relationships between pixels that humans are oblivious to, but that still correlate with images of certain types of real objects. Modifying these patterns on a legitimate image can result in "adversarial" images that the system misclassifies. +Adversarial vulnerabilities can also result in nonlinear systems, or from non-pattern perturbations. Some systems are so brittle that changing a single adversarial pixel predictably induces misclassification.[citation needed] Machine learning models are often vulnerable to manipulation and/or evasion via adversarial machine learning. +Researchers have demonstrated how backdoors can be placed undetectably into classifying (e.g., for categories "spam" and well-visible "not spam" of posts) machine learning models which are often developed and/or trained by third parties. Parties can change the classification of any input, including in cases for which a type of data/software transparency is provided, possibly including white-box access. +Machine learning poses a host of ethical questions. Systems that are trained on datasets collected with biases may exhibit these biases upon use (algorithmic bias), thus digitizing cultural prejudices. For example, in 1988, the UK's Commission for Racial Equality found that St. George's Medical School had been using a computer program trained from data of previous admissions staff and this program had denied nearly 60 candidates who were found to be either women or had non-European sounding names. Using job hiring data from a firm with racist hiring policies may lead to a machine learning system duplicating the bias by scoring job applicants by similarity to previous successful applicants. Responsible collection of data and documentation of algorithmic rules used by a system thus is a critical part of machine learning. +AI can be well-equipped to make decisions in technical fields, which rely heavily on data and historical information. These decisions rely on the objectivity and logical reasoning. Because human languages contain biases, machines trained on language corpora will necessarily also learn these biases. +Other forms of ethical challenges, not related to personal biases, are seen in health care. There are concerns among health care professionals that these systems might not be designed in the public's interest but as income-generating machines. This is especially true in the United States where there is a long-standing ethical dilemma of improving health care, but also increase profits. For example, the algorithms could be designed to provide patients with unnecessary tests or medication in which the algorithm's proprietary owners hold stakes. There is potential for machine learning in health care to provide professionals an additional tool to diagnose, medicate, and plan recovery paths for patients, but this requires these biases to be mitigated. +Since the 2010s, advances in both machine learning algorithms and computer hardware have led to more efficient methods for training deep neural networks (a particular narrow subdomain of machine learning) that contain many layers of non-linear hidden units. By 2019, graphic processing units (GPUs), often with AI-specific enhancements, had displaced CPUs as the dominant method of training large-scale commercial cloud AI. OpenAI estimated the hardware computing used in the largest deep learning projects from AlexNet (2012) to AlphaZero (2017), and found a 300,000-fold increase in the amount of compute required, with a doubling-time trendline of 3.4 months. diff --git a/llm_rl/evaluation/ceval/ceval.py b/llm_rl/evaluation/ceval/ceval.py new file mode 100644 index 00000000..33005de3 --- /dev/null +++ b/llm_rl/evaluation/ceval/ceval.py @@ -0,0 +1,166 @@ +# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import os + +import datasets +import pandas as pd + + +_CITATION = """\ +@article{huang2023ceval, + title={C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Models}, + author={Huang, Yuzhen and Bai, Yuzhuo and Zhu, Zhihao and Zhang, Junlei and Zhang, Jinghan and Su, Tangjun and Liu, Junteng and Lv, Chuancheng and Zhang, Yikai and Lei, Jiayi and Fu, Yao and Sun, Maosong and He, Junxian}, + journal={arXiv preprint arXiv:2305.08322}, + year={2023} +} +""" + +_DESCRIPTION = """\ +C-Eval is a comprehensive Chinese evaluation suite for foundation models. It consists of 13948 multi-choice questions spanning 52 diverse disciplines and four difficulty levels. +""" + +_HOMEPAGE = "https://cevalbenchmark.com" + +_LICENSE = "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License" + +_URL = "ceval.zip" + +task_list = [ + "computer_network", + "operating_system", + "computer_architecture", + "college_programming", + "college_physics", + "college_chemistry", + "advanced_mathematics", + "probability_and_statistics", + "discrete_mathematics", + "electrical_engineer", + "metrology_engineer", + "high_school_mathematics", + "high_school_physics", + "high_school_chemistry", + "high_school_biology", + "middle_school_mathematics", + "middle_school_biology", + "middle_school_physics", + "middle_school_chemistry", + "veterinary_medicine", + "college_economics", + "business_administration", + "marxism", + "mao_zedong_thought", + "education_science", + "teacher_qualification", + "high_school_politics", + "high_school_geography", + "middle_school_politics", + "middle_school_geography", + "modern_chinese_history", + "ideological_and_moral_cultivation", + "logic", + "law", + "chinese_language_and_literature", + "art_studies", + "professional_tour_guide", + "legal_professional", + "high_school_chinese", + "high_school_history", + "middle_school_history", + "civil_servant", + "sports_science", + "plant_protection", + "basic_medicine", + "clinical_medicine", + "urban_and_rural_planner", + "accountant", + "fire_engineer", + "environmental_impact_assessment_engineer", + "tax_accountant", + "physician", +] + + +class CevalConfig(datasets.BuilderConfig): + def __init__(self, **kwargs): + super().__init__(version=datasets.Version("1.0.0"), **kwargs) + + +class Ceval(datasets.GeneratorBasedBuilder): + BUILDER_CONFIGS = [ + CevalConfig( + name=task_name, + ) + for task_name in task_list + ] + + def _info(self): + features = datasets.Features( + { + "id": datasets.Value("int32"), + "question": datasets.Value("string"), + "A": datasets.Value("string"), + "B": datasets.Value("string"), + "C": datasets.Value("string"), + "D": datasets.Value("string"), + "answer": datasets.Value("string"), + "explanation": datasets.Value("string"), + } + ) + return datasets.DatasetInfo( + description=_DESCRIPTION, + features=features, + homepage=_HOMEPAGE, + license=_LICENSE, + citation=_CITATION, + ) + + def _split_generators(self, dl_manager): + data_dir = dl_manager.download_and_extract(_URL) + task_name = self.config.name + return [ + datasets.SplitGenerator( + name=datasets.Split.TEST, + gen_kwargs={ + "filepath": os.path.join( + data_dir, "test", f"{task_name}_test.csv" + ), + }, + ), + datasets.SplitGenerator( + name=datasets.Split.VALIDATION, + gen_kwargs={ + "filepath": os.path.join( + data_dir, "val", f"{task_name}_val.csv" + ), + }, + ), + datasets.SplitGenerator( + name=datasets.Split.TRAIN, + gen_kwargs={ + "filepath": os.path.join( + data_dir, "dev", f"{task_name}_dev.csv" + ), + }, + ), + ] + + def 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zB>&S&143fDEWjbiI5VE@!<%%H8Bc~_$bK7c_-cQM9?@GuyJ zklEh{!8mh&5o1y;Ucg5?;OPuA-)h0&=-9WWt`4*zfkzpuECia)F_q;1@Y)9kLC;WD z#1ioAi#M1t5-m%j6F_3bA0G3qyo++c0 literal 0 HcmV?d00001 diff --git a/llm_rl/evaluation/mmlu/mmlu.py b/llm_rl/evaluation/mmlu/mmlu.py new file mode 100644 index 00000000..9f1bd101 --- /dev/null +++ b/llm_rl/evaluation/mmlu/mmlu.py @@ -0,0 +1,167 @@ +# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import os + +import datasets +import pandas as pd + + +_CITATION = """\ +@article{hendryckstest2021, + title={Measuring Massive Multitask Language Understanding}, + author={Dan Hendrycks and Collin Burns and Steven Basart and Andy Zou and Mantas Mazeika and Dawn Song and Jacob Steinhardt}, + journal={Proceedings of the International Conference on Learning Representations (ICLR)}, + year={2021} +} +""" + +_DESCRIPTION = """\ +Measuring Massive Multitask Language Understanding by Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt (ICLR 2021). +""" + +_HOMEPAGE = "https://github.com/hendrycks/test" + +_LICENSE = "MIT" + +_URL = "mmlu.zip" + +task_list = [ + "high_school_european_history", + "business_ethics", + "clinical_knowledge", + "medical_genetics", + "high_school_us_history", + "high_school_physics", + "high_school_world_history", + "virology", + "high_school_microeconomics", + "econometrics", + "college_computer_science", + "high_school_biology", + "abstract_algebra", + "professional_accounting", + "philosophy", + "professional_medicine", + "nutrition", + "global_facts", + "machine_learning", + "security_studies", + "public_relations", + "professional_psychology", + "prehistory", + "anatomy", + "human_sexuality", + "college_medicine", + "high_school_government_and_politics", + "college_chemistry", + "logical_fallacies", + "high_school_geography", + "elementary_mathematics", + "human_aging", + "college_mathematics", + "high_school_psychology", + "formal_logic", + "high_school_statistics", + "international_law", + "high_school_mathematics", + "high_school_computer_science", + "conceptual_physics", + "miscellaneous", + "high_school_chemistry", + "marketing", + "professional_law", + "management", + "college_physics", + "jurisprudence", + "world_religions", + "sociology", + "us_foreign_policy", + "high_school_macroeconomics", + "computer_security", + "moral_scenarios", + "moral_disputes", + "electrical_engineering", + "astronomy", + "college_biology", +] + + +class MMLUConfig(datasets.BuilderConfig): + def __init__(self, **kwargs): + super().__init__(version=datasets.Version("1.0.0"), **kwargs) + + +class MMLU(datasets.GeneratorBasedBuilder): + BUILDER_CONFIGS = [ + MMLUConfig( + name=task_name, + ) + for task_name in task_list + ] + + def _info(self): + features = datasets.Features( + { + "question": datasets.Value("string"), + "A": datasets.Value("string"), + "B": datasets.Value("string"), + "C": datasets.Value("string"), + "D": datasets.Value("string"), + "answer": datasets.Value("string"), + } + ) + return datasets.DatasetInfo( + description=_DESCRIPTION, + features=features, + homepage=_HOMEPAGE, + license=_LICENSE, + citation=_CITATION, + ) + + def _split_generators(self, dl_manager): + data_dir = dl_manager.download_and_extract(_URL) + task_name = self.config.name + return [ + datasets.SplitGenerator( + name=datasets.Split.TEST, + gen_kwargs={ + "filepath": os.path.join( + data_dir, "data", "test", f"{task_name}_test.csv" + ), + }, + ), + datasets.SplitGenerator( + name=datasets.Split.VALIDATION, + gen_kwargs={ + "filepath": os.path.join( + data_dir, "data", "val", f"{task_name}_val.csv" + ), + }, + ), + datasets.SplitGenerator( + name=datasets.Split.TRAIN, + gen_kwargs={ + "filepath": os.path.join( + data_dir, "data", "dev", f"{task_name}_dev.csv" + ), + }, + ), + ] + + def _generate_examples(self, filepath): + df = pd.read_csv(filepath) + df.columns = ["question", "A", "B", "C", "D", "answer"] + + for i, instance in enumerate(df.to_dict(orient="records")): + yield i, instance diff --git a/llm_rl/evaluation/mmlu/mmlu.zip b/llm_rl/evaluation/mmlu/mmlu.zip new file mode 100644 index 0000000000000000000000000000000000000000..1aaee65f1c258cfc044ce9d0a7ef745c4defa937 GIT binary patch literal 2336375 zcmaHSV|XO$)@5vTIyNe{ZQHhO+eW8j+qT_t(y?uJY-e)knS1X!XTF*GQB_YpZ~dvg 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language governing permissions and +# limitations under the License. +import os + +import datasets +import pandas as pd + + +_CITATION = """\ +@article{li2023cmmlu, + title={CMMLU: Measuring massive multitask language understanding in Chinese}, + author={Haonan Li and Yixuan Zhang and Fajri Koto and Yifei Yang and Hai Zhao and Yeyun Gong and Nan Duan and Timothy Baldwin}, + journal={arXiv preprint arXiv:2306.09212}, + year={2023} +} +""" + +_DESCRIPTION = """\ +CMMLU is a comprehensive Chinese assessment suite specifically designed to evaluate the advanced knowledge and reasoning abilities of LLMs within the Chinese language and cultural context. +""" + +_HOMEPAGE = "https://github.com/haonan-li/CMMLU" + +_LICENSE = "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License" + +_URL = "cmmlu.zip" + +task_list = [ + 'agronomy', + 'anatomy', + 'ancient_chinese', + 'arts', + 'astronomy', + 'business_ethics', + 'chinese_civil_service_exam', + 'chinese_driving_rule', + 'chinese_food_culture', + 'chinese_foreign_policy', + 'chinese_history', + 'chinese_literature', + 'chinese_teacher_qualification', + 'clinical_knowledge', + 'college_actuarial_science', + 'college_education', + 'college_engineering_hydrology', + 'college_law', + 'college_mathematics', + 'college_medical_statistics', + 'college_medicine', + 'computer_science', + 'computer_security', + 'conceptual_physics', + 'construction_project_management', + 'economics', + 'education', + 'electrical_engineering', + 'elementary_chinese', + 'elementary_commonsense', + 'elementary_information_and_technology', + 'elementary_mathematics', + 'ethnology', + 'food_science', + 'genetics', + 'global_facts', + 'high_school_biology', + 'high_school_chemistry', + 'high_school_geography', + 'high_school_mathematics', + 'high_school_physics', + 'high_school_politics', + 'human_sexuality', + 'international_law', + 'journalism', + 'jurisprudence', + 'legal_and_moral_basis', + 'logical', + 'machine_learning', + 'management', + 'marketing', + 'marxist_theory', + 'modern_chinese', + 'nutrition', + 'philosophy', + 'professional_accounting', + 'professional_law', + 'professional_medicine', + 'professional_psychology', + 'public_relations', + 'security_study', + 'sociology', + 'sports_science', + 'traditional_chinese_medicine', + 'virology', + 'world_history', + 'world_religions', +] + + +class CMMLUConfig(datasets.BuilderConfig): + def __init__(self, **kwargs): + super().__init__(version=datasets.Version("1.0.1"), **kwargs) + + +class CMMLU(datasets.GeneratorBasedBuilder): + BUILDER_CONFIGS = [ + CMMLUConfig( + name=task_name, + ) + for task_name in task_list + ] + + def _info(self): + features = datasets.Features( + { + "question": datasets.Value("string"), + "A": datasets.Value("string"), + "B": datasets.Value("string"), + "C": datasets.Value("string"), + "D": datasets.Value("string"), + "answer": datasets.Value("string"), + } + ) + return datasets.DatasetInfo( + description=_DESCRIPTION, + features=features, + homepage=_HOMEPAGE, + license=_LICENSE, + citation=_CITATION, + ) + + def _split_generators(self, dl_manager): + data_dir = dl_manager.download_and_extract(_URL) + task_name = self.config.name + return [ + datasets.SplitGenerator( + name=datasets.Split.TEST, + gen_kwargs={ + "filepath": os.path.join(data_dir, f"test/{task_name}.csv"), + }, + ), + datasets.SplitGenerator( + name=datasets.Split.TRAIN, + gen_kwargs={ + "filepath": os.path.join(data_dir, f"dev/{task_name}.csv"), + }, + ), + ] + + def _generate_examples(self, filepath): + df = pd.read_csv(filepath, header=0, index_col=0, encoding="utf-8") + for i, instance in enumerate(df.to_dict(orient="records")): + question = instance.pop("Question", "") + answer = instance.pop("Answer", "") + instance["question"] = question + instance["answer"] = answer + yield i, instance diff --git a/llm_rl/evaluation/cmmlu/cmmlu.zip b/llm_rl/evaluation/cmmlu/cmmlu.zip new file mode 100644 index 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