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Dismiss alert {{ message }} 01-ai / Yi Public * Notifications * Fork 73 * Star 1.6k A series of large language models trained from scratch by developers @01-ai 01.ai License Apache-2.0 license 1.6k stars 73 forks Activity Star Notifications * Code * Issues 17 * Pull requests 0 * Discussions * Actions * Projects 0 * Security * Insights More * Code * Issues * Pull requests * Discussions * Actions * Projects * Security * Insights 01-ai/Yi This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. main Switch branches/tags [ ] Branches Tags Could not load branches Nothing to show {{ refName }} default View all branches Could not load tags Nothing to show {{ refName }} default View all tags Name already in use A tag already exists with the provided branch name. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Are you sure you want to create this branch? Cancel Create 1 branch 0 tags Code * Local * Codespaces * Clone HTTPS GitHub CLI [https://github.com/0] Use Git or checkout with SVN using the web URL. [gh repo clone 01-ai/] Work fast with our official CLI. Learn more about the CLI. * Open with GitHub Desktop * Download ZIP Sign In Required Please sign in to use Codespaces. Launching GitHub Desktop If nothing happens, download GitHub Desktop and try again. Launching GitHub Desktop If nothing happens, download GitHub Desktop and try again. Launching Xcode If nothing happens, download Xcode and try again. Launching Visual Studio Code Your codespace will open once ready. There was a problem preparing your codespace, please try again. Latest commit @ZhaoFancy ZhaoFancy Merge pull request #28 from 01-ai/fancy/add_200k ... 8e33c86 Nov 6, 2023 Merge pull request #28 from 01-ai/fancy/add_200k Add Yi-6B-200K and Yi-34B-200K 8e33c86 Git stats * 14 commits Files Permalink Failed to load latest commit information. Type Name Latest commit message Commit time .github/workflows disable internal github action for this time November 4, 2023 11:32 assets/img init November 4, 2023 00:13 demo init November 4, 2023 00:13 finetune fixed typo: separate November 6, 2023 14:07 .dockerignore init November 4, 2023 00:13 .gitignore fixed typo: separate November 6, 2023 14:07 .pre-commit-config.yaml init November 4, 2023 00:13 Dockerfile init November 4, 2023 00:13 LICENSE init November 4, 2023 00:13 MODEL_LICENSE_AGREEMENT.txt bump model license agreement version November 4, 2023 11:06 README.md add more links November 6, 2023 22:25 requirements.txt init November 4, 2023 00:13 View code [ ] Introduction News Model Performance Usage 1. Prepare development environment 2. Download the model (optional) 3. Examples 3.1 Use the base model 3.2 Finetuning from the base model: Disclaimer License README.md [Yi] [badge] [6874747073] [6874747073] [6874747073] [6874747073] [6874747073] Introduction The Yi series models are large language models trained from scratch by developers at 01.AI. The first public release contains two bilingual (English/Chinese) base models with the parameter sizes of 6B and 34B. Both of them are trained with 4K sequence length and can be extended to 32K during inference time. News * 2023/11/05: The base model of Yi-6B-200K and Yi-34B-200K with 200K context length. * 2023/11/02: The base model of Yi-6B and Yi-34B. Model Performance Common-sense Reading Math Model MMLU CMMLU C-Eval GAOKAO BBH Reasoning Comprehension & Code 5-shot 5-shot 5-shot 0-shot 3-shot@1 - - - LLaMA2-34B 62.6 - - - 44.1 69.9 68.0 26.0 LLaMA2-70B 68.9 53.3 - 49.8 51.2 71.9 69.4 36.8 Baichuan2-13B 59.2 62.0 58.1 54.3 48.8 64.3 62.4 23.0 Qwen-14B 66.3 71.0 72.1 62.5 53.4 73.3 72.5 39.8 Skywork-13B 62.1 61.8 60.6 68.1 41.7 72.4 61.4 24.9 InternLM-20B 62.1 59.0 58.8 45.5 52.5 78.3 - 30.4 Aquila-34B 67.8 71.4 63.1 - - - - - Falcon-180B 70.4 58.0 57.8 59.0 54.0 77.3 68.8 34.0 Yi-6B 63.2 75.5 72.0 72.2 42.8 72.3 68.7 19.8 Yi-6B-200K 64.0 75.3 73.5 73.9 42.0 72.0 69.1 19.0 Yi-34B 76.3 83.7 81.4 82.8 54.3 80.1 76.4 37.1 Yi-34B-200K 76.1 83.6 81.9 83.4 52.7 79.7 76.6 36.3 While benchmarking open-source models, we have observed a disparity between the results generated by our pipeline and those reported in public sources (e.g. OpenCompass). Upon conducting a more in-depth investigation of this difference, we have discovered that various models may employ different prompts, post-processing strategies, and sampling techniques, potentially resulting in significant variations in the outcomes. Our prompt and post-processing strategy remains consistent with the original benchmark, and greedy decoding is employed during evaluation without any post-processing for the generated content. For scores that were not reported by the original authors (including scores reported with different settings), we try to get results with our pipeline. To evaluate the model's capability extensively, we adopted the methodology outlined in Llama2. Specifically, we included PIQA, SIQA, HellaSwag, WinoGrande, ARC, OBQA, and CSQA to assess common sense reasoning. SquAD, QuAC, and BoolQ were incorporated to evaluate reading comprehension. CSQA was exclusively tested using a 7-shot setup, while all other tests were conducted with a 0-shot configuration. Additionally, we introduced GSM8K (8-shot@1), MATH (4-shot@1), HumanEval (0-shot@1), and MBPP (3-shot@1) under the category "Math & Code". Due to technical constraints, we did not test Falcon-180 on QuAC and OBQA; the score is derived by averaging the scores on the remaining tasks. Since the scores for these two tasks are generally lower than the average, we believe that Falcon-180B's performance was not underestimated. Usage Feel free to create an issue if you encounter any problem when using the Yi series models. 1. Prepare development environment The best approach to try the Yi series models is through Docker with GPUs. We provide the following docker images to help you get started. Note that the latest tag always points to the latest code in the main branch. To test a stable version, please replace it with a specific tag. If you prefer trying out with your local development environment. First, create a virtual environment and clone this repo. Then install the dependencies with pip install -r requirements.txt. For the best performance, we recommend you also install the latest version (>= 2.3.3) of flash-attention. 2. Download the model (optional) By default the model weights and tokenizer will be downloaded from HuggingFace automatically in the next step. You can also download them manually from the following places: * ModelScope * WiseModel (Search for Yi) * Mirror site (remember to extract the content with tar) + Yi-6B.tar + Yi-6B-200K.tar + Yi-34B.tar + Yi-34B-200K.tar 3. Examples 3.1 Use the base model python demo/text_generation.py To reuse the downloaded models in the previous step, you can provide the extra --model argument: python demo/text_generation.py --model /path/to/model Or if you'd like to get your hands dirty: from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("01-ai/Yi-34B", device_map="auto", torch_dtype="auto", trust_remote_code=True) tokenizer = AutoTokenizer.from_pretrained("01-ai/Yi-34B", trust_remote_code=True) inputs = tokenizer("There's a place where time stands still. A place of breath taking wonder, but also", return_tensors="pt") outputs = model.generate(inputs.input_ids.cuda(), max_new_tokens=256) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) Output Prompt: There's a place where time stands still. A place of breath taking wonder, but also Generation: There's a place where time stands still. A place of breath taking wonder, but also of great danger. A place where the very air you breathe could kill you. A place where the only way to survive is to be prepared. The place is called the Arctic. The Arctic is a vast, frozen wilderness. It is a place of extremes. The temperatures can drop to -40 degrees Celsius. The winds can reach speeds of 100 kilometers per hour. The sun can shine for 24 hours a day, or not at all for weeks on end. The Arctic is also a place of great beauty. The ice and snow are a pristine white. The sky is a deep blue. The sunsets are spectacular. But the Arctic is also a place of great danger. The ice can be treacherous. The winds can be deadly. The sun can be blinding. The Arctic is a place where the only way to survive is to be prepared. The Arctic is a place of extremes. The temperatures can drop to -40 degrees Celsius. The winds can reach speeds of 100 kilometers per hour. The sun can shine for 24 hours a day, or not at all for weeks on end. The Arctic is a place of great beauty. The ice and snow are a For more advanced usage, please refer the doc. 3.2 Finetuning from the base model: bash finetune/scripts/run_sft_Yi_6b.sh Once finished, you can compare the finetuned model and the base model with the following command: bash finetune/scripts/run_eval.sh For more advanced usage like fine-tuning based on your custom data, please refer the doc. Disclaimer We use data compliance checking algorithms during the training process, to ensure the compliance of the trained model to the best of our ability. Due to complex data and the diversity of language model usage scenarios, we cannot guarantee that the model will generate correct, and reasonable output in all scenarios. Please be aware that there is still a risk of the model producing problematic outputs. We will not be responsible for any risks and issues resulting from misuse, misguidance, illegal usage, and related misinformation, as well as any associated data security concerns. License The source code in this repo is licensed under the Apache 2.0 license . The Yi series models are fully open for academic research and free commercial usage with permission via applications. All usage must adhere to the Model License Agreement 2.0. To apply for the official commercial license, please contact us (yi@01.ai). About A series of large language models trained from scratch by developers @01-ai 01.ai Topics large-language-models Resources Readme License Apache-2.0 license Activity Stars 1.6k stars Watchers 38 watching Forks 73 forks Report repository Releases No releases published Contributors 2 * * Languages * Python 96.3% * Shell 3.0% * Dockerfile 0.7% Footer (c) 2023 GitHub, Inc. Footer navigation * Terms * Privacy * Security * Status * Docs * Contact GitHub * Pricing * API * Training * Blog * About You can't perform that action at this time.