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What is Yi?

Introduction

• 🤖 The Yi series models are the next generation of open-source large language models trained from scratch by 01.AI.

• 🙌 Targeted as a bilingual language model and trained on 3T multilingual corpus, the Yi series models become one of the strongest LLM worldwide, showing promise in language understanding, commonsense reasoning, reading comprehension, and more. For example,

  • Yi-34B-Chat model landed in second place (following GPT-4 Turbo), outperforming other LLMs (such as GPT-4, Mixtral, Claude) on the AlpacaEval Leaderboard (based on data available up to January 2024).

  • Yi-34B model ranked first among all existing open-source models (such as Falcon-180B, Llama-70B, Claude) in both English and Chinese on various benchmarks, including Hugging Face Open LLM Leaderboard (pre-trained) and C-Eval (based on data available up to November 2023).

  • 🙏 (Credits to Llama) Thanks to the Transformer and Llama open-source communities, as they reduce the efforts required to build from scratch and enable the utilization of the same tools within the AI ecosystem.

  If you're interested in Yi's adoption of Llama architecture and license usage policy, see Yi's relation with Llama. ⬇️

  
  

  💡 TL;DR

  The Yi series models adopt the same model architecture as Llama but are NOT derivatives of Llama.

  • Both Yi and Llama are based on the Transformer structure, which has been the standard architecture for large language models since 2018.

  • Grounded in the Transformer architecture, Llama has become a new cornerstone for the majority of state-of-the-art open-source models due to its excellent stability, reliable convergence, and robust compatibility. This positions Llama as the recognized foundational framework for models including Yi.

  • Thanks to the Transformer and Llama architectures, other models can leverage their power, reducing the effort required to build from scratch and enabling the utilization of the same tools within their ecosystems.

  • However, the Yi series models are NOT derivatives of Llama, as they do not use Llama's weights.

    • As Llama's structure is employed by the majority of open-source models, the key factors of determining model performance are training datasets, training pipelines, and training infrastructure.

    • Developing in a unique and proprietary way, Yi has independently created its own high-quality training datasets, efficient training pipelines, and robust training infrastructure entirely from the ground up. This effort has led to excellent performance with Yi series models ranking just behind GPT4 and surpassing Llama on the Alpaca Leaderboard in Dec 2023.

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News

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Models

Yi models come in multiple sizes and cater to different use cases. You can also fine-tune Yi models to meet your specific requirements.

If you want to deploy Yi models, make sure you meet the software and hardware requirements.

Chat models

<sub><sup>- 4-bit series models are quantized by AWQ.
- 8-bit series models are quantized by GPTQ
- All quantized models have a low barrier to use since they can be deployed on consumer-grade GPUs (e.g., 3090, 4090).</sup></sub>

Base models

<sub><sup>- 200k is roughly equivalent to 400,000 Chinese characters.
- If you want to use the previous version of the Yi-34B-200K (released on Nov 5, 2023), run git checkout 069cd341d60f4ce4b07ec394e82b79e94f656cf to download the weight.</sup></sub>

Model info

• For chat and base models

• For chat models

  For chat model limitations, see the explanations below. ⬇️

  
  The released chat model has undergone exclusive training using Supervised Fine-Tuning (SFT). Compared to other standard chat models, our model produces more diverse responses, making it suitable for various downstream tasks, such as creative scenarios. Furthermore, this diversity is expected to enhance the likelihood of generating higher quality responses, which will be advantageous for subsequent Reinforcement Learning (RL) training.

  
  However, this higher diversity might amplify certain existing issues, including:

  • Hallucination: This refers to the model generating factually incorrect or nonsensical information. With the model's responses being more varied, there's a higher chance of hallucination that are not based on accurate data or logical reasoning.
  • Non-determinism in re-generation: When attempting to regenerate or sample responses, inconsistencies in the outcomes may occur. The increased diversity can lead to varying results even under similar input conditions.
  • Cumulative Error: This occurs when errors in the model's responses compound over time. As the model generates more diverse responses, the likelihood of small inaccuracies building up into larger errors increases, especially in complex tasks like extended reasoning, mathematical problem-solving, etc.
  • To achieve more coherent and consistent responses, it is advisable to adjust generation configuration parameters such as temperature, top_p, or top_k. These adjustments can help in the balance between creativity and coherence in the model's outputs.

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How to use Yi?

• Quick start
  • Choose your path
  • pip
  • docker
  • conda-lock
  • llama.cpp
  • Web demo
• Fine-tuning
• Quantization
• Deployment
• FAQ
• Learning hub

Quick start

💡 Tip: If you want to get started with the Yi model and explore different methods for inference, check out the Yi Cookbook.

Choose your path

Select one of the following paths to begin your journey with Yi!

🎯 Deploy Yi locally

If you prefer to deploy Yi models locally,

• 🙋‍♀️ and you have sufficient resources (for example, NVIDIA A800 80GB), you can choose one of the following methods:

  • pip
  • Docker
  • conda-lock

• 🙋‍♀️ and you have limited resources (for example, a MacBook Pro), you can use llama.cpp.

🎯 Not to deploy Yi locally

If you prefer not to deploy Yi models locally, you can explore Yi's capabilities using any of the following options.

🙋‍♀️ Run Yi with APIs

If you want to explore more features of Yi, you can adopt one of these methods:

• Yi APIs (Yi official)

  • Early access has been granted to some applicants. Stay tuned for the next round of access!

• Yi APIs (Replicate)

🙋‍♀️ Run Yi in playground

If you want to chat with Yi with more customizable options (e.g., system prompt, temperature, repetition penalty, etc.), you can try one of the following options:

• Yi-34B-Chat-Playground (Yi official)
  • Access is available through a whitelist. Welcome to apply (fill out a form in English or Chinese).
• Yi-34B-Chat-Playground (Replicate)

🙋‍♀️ Chat with Yi

If you want to chat with Yi, you can use one of these online services, which offer a similar user experience:

• Yi-34B-Chat (Yi official on Hugging Face)

  • No registration is required.

• Yi-34B-Chat (Yi official beta)

  • Access is available through a whitelist. Welcome to apply (fill out a form in English or Chinese).

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Quick start - pip

This tutorial guides you through every step of running Yi-34B-Chat locally on an A800 (80G) and then performing inference.

Step 0: Prerequisites

• Make sure Python 3.10 or a later version is installed.

• If you want to run other Yi models, see software and hardware requirements.

Step 1: Prepare your environment

To set up the environment and install the required packages, execute the following command.

git clone https://github.com/01-ai/Yi.git
cd yi
pip install -r requirements.txt

Step 2: Download the Yi model

You can download the weights and tokenizer of Yi models from the following sources:

• Hugging Face
• ModelScope
• WiseModel

Step 3: Perform inference

You can perform inference with Yi chat or base models as below.

Perform inference with Yi chat model

1. Create a file named quick_start.py and copy the following content to it.

   from transformers import AutoModelForCausalLM, AutoTokenizer
   
   model_path = '<your-model-path>'
   
   tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False)
   
   # Since transformers 4.35.0, the GPT-Q/AWQ model can be loaded using AutoModelForCausalLM.
   model = AutoModelForCausalLM.from_pretrained(
       model_path,
       device_map="auto",
       torch_dtype='auto'
   ).eval()
   
   # Prompt content: "hi"
   messages = [
       {"role": "user", "content": "hi"}
   ]
   
   input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
   output_ids = model.generate(input_ids.to('cuda'))
   response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
   
   # Model response: "Hello! How can I assist you today?"
   print(response)
   

2. Run quick_start.py.

   python quick_start.py
   

   Then you can see an output similar to the one below. 🥳

   Hello! How can I assist you today?
   

Perform inference with Yi base model

• Yi-34B

  The steps are similar to pip - Perform inference with Yi chat model.

  You can use the existing file text_generation.py.

  python demo/text_generation.py  --model <your-model-path>
  

  Then you can see an output similar to the one below. 🥳

  Output. ⬇️

  
  

  Prompt: Let me tell you an interesting story about cat Tom and mouse Jerry,

  Generation: Let me tell you an interesting story about cat Tom and mouse Jerry, which happened in my childhood. My father had a big house with two cats living inside it to kill mice. One day when I was playing at home alone, I found one of the tomcats lying on his back near our kitchen door, looking very much like he wanted something from us but couldn’t get up because there were too many people around him! He kept trying for several minutes before finally giving up...

• Yi-9B

  Input

  from transformers import AutoModelForCausalLM, AutoTokenizer
  
  MODEL_DIR = "01-ai/Yi-9B"
  model = AutoModelForCausalLM.from_pretrained(MODEL_DIR, torch_dtype="auto")
  tokenizer = AutoTokenizer.from_pretrained(MODEL_DIR, use_fast=False)
  
  input_text = "# write the quick sort algorithm"
  inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
  outputs = model.generate(**inputs, max_length=256)
  print(tokenizer.decode(outputs[0], skip_special_tokens=True))
  

  Output

  # write the quick sort algorithm
  def quick_sort(arr):
      if len(arr) <= 1:
          return arr
      pivot = arr[len(arr) // 2]
      left = [x for x in arr if x < pivot]
      middle = [x for x in arr if x == pivot]
      right = [x for x in arr if x > pivot]
      return quick_sort(left) + middle + quick_sort(right)
  
  # test the quick sort algorithm
  print(quick_sort([3, 6, 8, 10, 1, 2, 1]))
  

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Quick start - Docker

docker run -it --gpus all \
-v <your-model-path>: /models
ghcr.io/01-ai/yi:latest

Quick start - conda-lock

Quick start - llama.cpp

The following tutorial will guide you through every step of running a quantized model (Yi-chat-6B-2bits) locally and then performing inference.

git clone git@github.com:ggerganov/llama.cpp.git

GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/XeIaso/yi-chat-6B-GGUF

git-lfs pull --include yi-chat-6b.Q2_K.gguf

make -j4 && ./main -m /Users/yu/yi-chat-6B-GGUF/yi-chat-6b.Q2_K.gguf -p "How do you feed your pet fox? Please answer this question in 6 simple steps:\nStep 1:" -n 384 -e

...

How do you feed your pet fox? Please answer this question in 6 simple steps:

Step 1: Select the appropriate food for your pet fox. You should choose high-quality, balanced prey items that are suitable for their unique dietary needs. These could include live or frozen mice, rats, pigeons, or other small mammals, as well as fresh fruits and vegetables.

Step 2: Feed your pet fox once or twice a day, depending on the species and its individual preferences. Always ensure that they have access to fresh water throughout the day.

Step 3: Provide an appropriate environment for your pet fox. Ensure it has a comfortable place to rest, plenty of space to move around, and opportunities to play and exercise.

Step 4: Socialize your pet with other animals if possible. Interactions with other creatures can help them develop social skills and prevent boredom or stress.

Step 5: Regularly check for signs of illness or discomfort in your fox. Be prepared to provide veterinary care as needed, especially for common issues such as parasites, dental health problems, or infections.

Step 6: Educate yourself about the needs of your pet fox and be aware of any potential risks or concerns that could affect their well-being. Regularly consult with a veterinarian to ensure you are providing the best care.

...

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Web demo

You can build a web UI demo for Yi chat models (note that Yi base models are not supported in this senario).

Step 1: Prepare your environment.

Step 2: Download the Yi model.

Step 3. To start a web service locally, run the following command.

python demo/web_demo.py -c <your-model-path>

You can access the web UI by entering the address provided in the console into your browser.

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Fine-tuning

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

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Quantization

GPT-Q

python quantization/gptq/quant_autogptq.py \
  --model /base_model                      \
  --output_dir /quantized_model            \
  --trust_remote_code

Once finished, you can then evaluate the resulting model as follows:

python quantization/gptq/eval_quantized_model.py \
  --model /quantized_model                       \
  --trust_remote_code

AWQ

python quantization/awq/quant_autoawq.py \
  --model /base_model                      \
  --output_dir /quantized_model            \
  --trust_remote_code

Once finished, you can then evaluate the resulting model as follows:

python quantization/awq/eval_quantized_model.py \
  --model /quantized_model                       \
  --trust_remote_code

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Deployment

If you want to deploy Yi models, make sure you meet the software and hardware requirements.

Software requirements

Before using Yi quantized models, make sure you've installed the correct software listed below.

Hardware requirements

Before deploying Yi in your environment, make sure your hardware meets the following requirements.

Chat models

Below are detailed minimum VRAM requirements under different batch use cases.

Base models

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FAQ

Learning hub

Why Yi?

• Ecosystem
  • Upstream
  • Downstream
    • Serving
    • Quantization
    • Fine-tuning
    • API
• Benchmarks
  • Chat model performance
  • Base model performance
    • Yi-34B and Yi-34B-200K
    • Yi-9B

Ecosystem

Yi has a comprehensive ecosystem, offering a range of tools, services, and models to enrich your experiences and maximize productivity.

• Upstream
• Downstream
  • Serving
  • Quantization
  • Fine-tuning
  • API

Upstream

The Yi series models follow the same model architecture as Llama. By choosing Yi, you can leverage existing tools, libraries, and resources within the Llama ecosystem, eliminating the need to create new tools and enhancing development efficiency.

For example, the Yi series models are saved in the format of the Llama model. You can directly use LlamaForCausalLM and LlamaTokenizer to load the model. For more information, see Use the chat model.

from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("01-ai/Yi-34b", use_fast=False)

model = AutoModelForCausalLM.from_pretrained("01-ai/Yi-34b", device_map="auto")

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Downstream

💡 Tip

• Feel free to create a PR and share the fantastic work you've built using the Yi series models.

• To help others quickly understand your work, it is recommended to use the format of <model-name>: <model-intro> + <model-highlights>.

Serving

If you want to get up with Yi in a few minutes, you can use the following services built upon Yi.

• Yi-34B-Chat: you can chat with Yi using one of the following platforms:

  • Yi-34B-Chat | Hugging Face
  • Yi-34B-Chat | Yi Platform: Note that currently it's available through a whitelist. Welcome to apply (fill out a form in English or Chinese) and experience it firsthand!

• Yi-6B-Chat (Replicate): you can use this model with more options by setting additional parameters and calling APIs.

• ScaleLLM: you can use this service to run Yi models locally with added flexibility and customization.

Quantization

If you have limited computational capabilities, you can use Yi's quantized models as follows.

These quantized models have reduced precision but offer increased efficiency, such as faster inference speed and smaller RAM usage.

• TheBloke/Yi-34B-GPTQ
• TheBloke/Yi-34B-GGUF
• TheBloke/Yi-34B-AWQ

Fine-tuning

If you're seeking to explore the diverse capabilities within Yi's thriving family, you can delve into Yi's fine-tuned models as below.

• TheBloke Models: this site hosts numerous fine-tuned models derived from various LLMs including Yi.

  This is not an exhaustive list for Yi, but to name a few sorted on downloads:

  • TheBloke/dolphin-2_2-yi-34b-AWQ
  • TheBloke/Yi-34B-Chat-AWQ
  • TheBloke/Yi-34B-Chat-GPTQ

• SUSTech/SUS-Chat-34B: this model ranked first among all models below 70B and outperformed the twice larger deepseek-llm-67b-chat. You can check the result on the Open LLM Leaderboard.

• OrionStarAI/OrionStar-Yi-34B-Chat-Llama: this model excelled beyond other models (such as GPT-4, Qwen-14B-Chat, Baichuan2-13B-Chat) in C-Eval and CMMLU evaluations on the OpenCompass LLM Leaderboard.

• NousResearch/Nous-Capybara-34B: this model is trained with 200K context length and 3 epochs on the Capybara dataset.

API

• amazing-openai-api: this tool converts Yi model APIs into the OpenAI API format out of the box.
• LlamaEdge: this tool builds an OpenAI-compatible API server for Yi-34B-Chat using a portable Wasm (WebAssembly) file, powered by Rust.

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Tech report

For detailed capabilities of the Yi series model, see Yi: Open Foundation Models by 01.AI.

Citation

@misc{ai2024yi,
    title={Yi: Open Foundation Models by 01.AI},
    author={01. AI and : and Alex Young and Bei Chen and Chao Li and Chengen Huang and Ge Zhang and Guanwei Zhang and Heng Li and Jiangcheng Zhu and Jianqun Chen and Jing Chang and Kaidong Yu and Peng Liu and Qiang Liu and Shawn Yue and Senbin Yang and Shiming Yang and Tao Yu and Wen Xie and Wenhao Huang and Xiaohui Hu and Xiaoyi Ren and Xinyao Niu and Pengcheng Nie and Yuchi Xu and Yudong Liu and Yue Wang and Yuxuan Cai and Zhenyu Gu and Zhiyuan Liu and Zonghong Dai},
    year={2024},
    eprint={2403.04652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

Benchmarks

• Chat model performance
• Base model performance

Chat model performance

Yi-34B-Chat model demonstrates exceptional performance, ranking first among all existing open-source models in the benchmarks including MMLU, CMMLU, BBH, GSM8k, and more.

Base model performance

Yi-34B and Yi-34B-200K

The Yi-34B and Yi-34B-200K models stand out as the top performers among open-source models, especially excelling in MMLU, CMMLU, common-sense reasoning, reading comprehension, and more.

Yi-9B

Yi-9B is almost the best among a range of similar-sized open-source models (including Mistral-7B, SOLAR-10.7B, Gemma-7B, DeepSeek-Coder-7B-Base-v1.5 and more), particularly excelling in code, math, common-sense reasoning, and reading comprehension.

• In terms of overall ability (Mean-All), Yi-9B performs the best among similarly sized open-source models, surpassing DeepSeek-Coder, DeepSeek-Math, Mistral-7B, SOLAR-10.7B, and Gemma-7B.

  

• In terms of coding ability (Mean-Code), Yi-9B's performance is second only to DeepSeek-Coder-7B, surpassing Yi-34B, SOLAR-10.7B, Mistral-7B, and Gemma-7B.

  

• In terms of math ability (Mean-Math), Yi-9B's performance is second only to DeepSeek-Math-7B, surpassing SOLAR-10.7B, Mistral-7B, and Gemma-7B.

  

• In terms of common sense and reasoning ability (Mean-Text), Yi-9B's performance is on par with Mistral-7B, SOLAR-10.7B, and Gemma-7B.

  

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Who can use Yi?

Everyone! 🙌 ✅

The code and weights of the Yi series models are distributed under the Apache 2.0 license, which means the Yi series models are free for personal usage, academic purposes, and commercial use.

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Misc.

Acknowledgments

A heartfelt thank you to each of you who have made contributions to the Yi community! You have helped Yi not just a project, but a vibrant, growing home for innovation.

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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.

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License

The code and weights of the Yi-1.5 series models are distributed under the Apache 2.0 license.

If you create derivative works based on this model, please include the following attribution in your derivative works:

This work is a derivative of [The Yi Series Model You Base On] by 01.AI, used under the Apache 2.0 License.

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