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Building the Next Generation of Open-Source and Bilingual LLMs

🤗 Hugging Face • 🤖 ModelScope • ✡️ WiseModel

👋 Join us 💬 WeChat (Chinese) !


📕 Table of Contents

🟢 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 all 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

🎯 2024/01/23: The Yi-VL models, Yi-VL-34B and Yi-VL-6B, are open-sourced and available to the public.
Yi-VL-34B has ranked first among all existing open-source models in the latest benchmarks, including MMMU and CMMMU (based on data available up to January 2024).
🎯 2023/11/23: Chat models are open-sourced and available to the public.
This release contains two chat models based on previously released base models, two 8-bit models quantized by GPTQ, and two 4-bit models quantized by AWQ.
  • Yi-34B-Chat
  • Yi-34B-Chat-4bits
  • Yi-34B-Chat-8bits
  • Yi-6B-Chat
  • Yi-6B-Chat-4bits
  • Yi-6B-Chat-8bits

You can try some of them interactively at:

🔔 2023/11/23: The Yi Series Models Community License Agreement is updated to v2.1.
🔥 2023/11/08: Invited test of Yi-34B chat model.
Application form:
🎯 2023/11/05: The base models, Yi-6B-200K and Yi-34B-200K, are open-sourced and available to the public.
This release contains two base models with the same parameter sizes as the previous release, except that the context window is extended to 200K.
🎯 2023/11/02: The base models, Yi-6B and Yi-34B, are open-sourced and available to the public.
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.

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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

Model Download
Yi-34B-Chat 🤗 Hugging Face🤖 ModelScope
Yi-34B-Chat-4bits 🤗 Hugging Face🤖 ModelScope
Yi-34B-Chat-8bits 🤗 Hugging Face🤖 ModelScope
Yi-6B-Chat 🤗 Hugging Face🤖 ModelScope
Yi-6B-Chat-4bits 🤗 Hugging Face🤖 ModelScope
Yi-6B-Chat-8bits 🤗 Hugging Face🤖 ModelScope

- 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).

Base models

Model Download
Yi-34B 🤗 Hugging Face🤖 ModelScope
Yi-34B-200K 🤗 Hugging Face🤖 ModelScope
Yi-6B 🤗 Hugging Face🤖 ModelScope
Yi-6B-200K 🤗 Hugging Face🤖 ModelScope

- 200k is roughly equivalent to 400,000 Chinese characters.

Other info

  • For chat and base models:

    • 6B series models are suitable for personal and academic use.

    • 34B series models suitable for personal, academic, and commercial (particularly for small and medium-sized enterprises) purposes. It's a cost-effective solution that's affordable and equipped with emergent ability.

    • The default context window is 4k tokens.

    • The pretrained tokens are 3T.

    • The training data are up to June 2023.

  • 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

Getting up and running with Yi models is simple with multiple choices available.

Choose your path

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

Quick start - Choose your path

🎯 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:

  • 🙋‍♀️ 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:

🙋‍♀️ 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:

🙋‍♀️ 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

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:

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

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...

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Quick start - Docker

Run Yi-34B-chat locally with Docker: a step-by-step guide. ⬇️
This tutorial guides you through every step of running Yi-34B-Chat on an A800 GPU or 4*4090 locally and then performing inference.

Step 0: Prerequisites

Make sure you've installed Docker and nvidia-container-toolkit.

Step 1: Start Docker

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

Alternatively, you can pull the Yi Docker image from registry.lingyiwanwu.com/ci/01-ai/yi:latest.

Step 2: Perform inference

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

Perform inference with Yi chat model

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

Note that the only difference is to set model_path = '<your-model-mount-path>' instead of model_path = '<your-model-path>'.

Perform inference with Yi base model

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

Note that the only difference is to set --model <your-model-mount-path>' instead of model <your-model-path>.

Quick start - conda-lock

You can use conda-lock to generate fully reproducible lock files for conda environments. ⬇️
You can refer to conda-lock.yml for the exact versions of the dependencies. Additionally, you can utilize micromamba for installing these dependencies.
To install the dependencies, follow these steps:
  1. Install micromamba by following the instructions available here.

  2. Execute micromamba install -y -n yi -f conda-lock.yml to create a conda environment named yi and install the necessary dependencies.

Quick start - llama.cpp

Run Yi-chat-6B-2bits locally with llama.cpp: a step-by-step guide. ⬇️
This tutorial guides you through every step of running a quantized model (Yi-chat-6B-2bits) locally and then performing inference.

Step 0: Prerequisites

  • This tutorial assumes you use a MacBook Pro with 16GB of memory and an Apple M2 Pro chip.

  • Make sure git-lfs is installed on your machine.

Step 1: Download llama.cpp

To clone the llama.cpp repository, run the following command.

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

Step 2: Download Yi model

2.1 To clone XeIaso/yi-chat-6B-GGUF with just pointers, run the following command.

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

2.2 To download a quantized Yi model (yi-chat-6b.Q2_K.gguf), run the following command.

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

Step 3: Perform inference

To perform inference with the Yi model, you can use one of the following methods.

Method 1: Perform inference in terminal

To compile llama.cpp using 4 threads and then conduct inference, navigate to the llama.cpp directory, and run the following command.

Tips
  • Replace /Users/yu/yi-chat-6B-GGUF/yi-chat-6b.Q2_K.gguf with the actual path of your model.

  • By default, the model operates in completion mode.

  • For additional output customization options (for example, system prompt, temperature, repetition penalty, etc.), run ./main -h to check detailed descriptions and usage.

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.

...

Now you have successfully asked a question to the Yi model and got an answer! 🥳

Method 2: Perform inference in web
  1. To initialize a lightweight and swift chatbot, run the following command.

    cd llama.cpp
    ./server --ctx-size 2048 --host 0.0.0.0 --n-gpu-layers 64 --model /Users/yu/yi-chat-6B-GGUF/yi-chat-6b.Q2_K.gguf
    

    Then you can get an output like this:

    ...
    
    llama_new_context_with_model: n_ctx      = 2048
    llama_new_context_with_model: freq_base  = 5000000.0
    llama_new_context_with_model: freq_scale = 1
    ggml_metal_init: allocating
    ggml_metal_init: found device: Apple M2 Pro
    ggml_metal_init: picking default device: Apple M2 Pro
    ggml_metal_init: ggml.metallib not found, loading from source
    ggml_metal_init: GGML_METAL_PATH_RESOURCES = nil
    ggml_metal_init: loading '/Users/yu/llama.cpp/ggml-metal.metal'
    ggml_metal_init: GPU name:   Apple M2 Pro
    ggml_metal_init: GPU family: MTLGPUFamilyApple8 (1008)
    ggml_metal_init: hasUnifiedMemory              = true
    ggml_metal_init: recommendedMaxWorkingSetSize  = 11453.25 MB
    ggml_metal_init: maxTransferRate               = built-in GPU
    ggml_backend_metal_buffer_type_alloc_buffer: allocated buffer, size =   128.00 MiB, ( 2629.44 / 10922.67)
    llama_new_context_with_model: KV self size  =  128.00 MiB, K (f16):   64.00 MiB, V (f16):   64.00 MiB
    ggml_backend_metal_buffer_type_alloc_buffer: allocated buffer, size =     0.02 MiB, ( 2629.45 / 10922.67)
    llama_build_graph: non-view tensors processed: 676/676
    llama_new_context_with_model: compute buffer total size = 159.19 MiB
    ggml_backend_metal_buffer_type_alloc_buffer: allocated buffer, size =   156.02 MiB, ( 2785.45 / 10922.67)
    Available slots:
    -> Slot 0 - max context: 2048
    
    llama server listening at http://0.0.0.0:8080
    
  2. To access the chatbot interface, open your web browser and enter http://0.0.0.0:8080 into the address bar.

    Yi model chatbot interface - llama.cpp

  3. Enter a question, such as "How do you feed your pet fox? Please answer this question in 6 simple steps" into the prompt window, and you will receive a corresponding answer.

    Ask a question to Yi model - llama.cpp

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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.

Quick start - web demo

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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
For advanced usage (like fine-tuning based on your custom data), see the explanations below. ⬇️

    Finetune code for Yi 6B and 34B

    Preparation

    From Image

    By default, we use a small dataset from BAAI/COIG to finetune the base model. You can also prepare your customized dataset in the following jsonl format:

    { "prompt": "Human: Who are you? Assistant:", "chosen": "I'm Yi." }
    

    And then mount them in the container to replace the default ones:

    docker run -it \
        -v /path/to/save/finetuned/model/:/finetuned-model \
        -v /path/to/train.jsonl:/yi/finetune/data/train.json \
        -v /path/to/eval.jsonl:/yi/finetune/data/eval.json \
        ghcr.io/01-ai/yi:latest \
        bash finetune/scripts/run_sft_Yi_6b.sh
    
    From Local Server

    Make sure you have conda. If not, use

    mkdir -p ~/miniconda3
    wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda3/miniconda.sh
    bash ~/miniconda3/miniconda.sh -b -u -p ~/miniconda3
    rm -rf ~/miniconda3/miniconda.sh
    ~/miniconda3/bin/conda init bash
    source ~/.bashrc
    

    Then, create a conda env:

    conda create -n dev_env python=3.10 -y
    conda activate dev_env
    pip install torch==2.0.1 deepspeed==0.10 tensorboard transformers datasets sentencepiece accelerate ray==2.7
    

    Hardware Setup

    For the Yi-6B model, a node with 4 GPUs, each has GPU mem larger than 60GB is recommended.

    For the Yi-34B model, because the usage of zero-offload technique takes a lot CPU memory, please be careful to limit the GPU numbers in 34B finetune training. Please use CUDA_VISIBLE_DEVICES to limit the GPU number (as shown in scripts/run_sft_Yi_34b.sh).

    A typical hardware setup for finetuning 34B model is a node with 8GPUS (limit to 4 in running by CUDA_VISIBLE_DEVICES=0,1,2,3), each has GPU mem larger than 80GB, with total CPU mem larger than 900GB.

    Quick Start

    Download a LLM-base model to MODEL_PATH (6B and 34B). A typical folder of models is like:

    |-- $MODEL_PATH
    |   |-- config.json
    |   |-- pytorch_model-00001-of-00002.bin
    |   |-- pytorch_model-00002-of-00002.bin
    |   |-- pytorch_model.bin.index.json
    |   |-- tokenizer_config.json
    |   |-- tokenizer.model
    |   |-- ...
    

    Download a dataset from huggingface to local storage DATA_PATH, e.g. Dahoas/rm-static.

    |-- $DATA_PATH
    |   |-- data
    |   |   |-- train-00000-of-00001-2a1df75c6bce91ab.parquet
    |   |   |-- test-00000-of-00001-8c7c51afc6d45980.parquet
    |   |-- dataset_infos.json
    |   |-- README.md
    

    finetune/yi_example_dataset has example datasets, which are modified from BAAI/COIG

    |-- $DATA_PATH
        |--data
            |-- train.jsonl
            |-- eval.jsonl
    

    cd into the scripts folder, copy and paste the script, and run. For example:

    cd finetune/scripts
    
    bash run_sft_Yi_6b.sh
    

    For the Yi-6B base model, setting training_debug_steps=20 and num_train_epochs=4 can output a chat model, which takes about 20 minutes.

    For the Yi-34B base model, it takes a relatively long time for initialization. Please be patient.

    Evaluation

    cd finetune/scripts
    
    bash run_eval.sh
    

    Then you'll see the answer from both the base model and the finetuned model.

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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
For a more detailed explanation, see the explanations below. ⬇️

    GPT-Q quantization

    GPT-Q is a PTQ(Post-Training Quantization) method. It's memory saving and provides potential speedups while retaining the accuracy of the model.

    Yi models can be GPT-Q quantized without a lot of efforts. We provide a step-by-step tutorial below.

    To run GPT-Q, we will use AutoGPTQ and exllama. And the huggingface transformers has integrated optimum and auto-gptq to perform GPTQ quantization on language models.

    Do Quantization

    The quant_autogptq.py script is provided for you to perform GPT-Q quantization:

    python quant_autogptq.py --model /base_model \
        --output_dir /quantized_model --bits 4 --group_size 128 --trust_remote_code
    
    Run Quantized Model

    You can run a quantized model using the eval_quantized_model.py:

    python 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
For detailed explanations, see the explanations below. ⬇️

    AWQ quantization

    AWQ is a PTQ(Post-Training Quantization) method. It's an efficient and accurate low-bit weight quantization (INT3/4) for LLMs.

    Yi models can be AWQ quantized without a lot of efforts. We provide a step-by-step tutorial below.

    To run AWQ, we will use AutoAWQ.

    Do Quantization

    The quant_autoawq.py script is provided for you to perform AWQ quantization:

    python quant_autoawq.py --model /base_model \
        --output_dir /quantized_model --bits 4 --group_size 128 --trust_remote_code
    
    Run Quantized Model

    You can run a quantized model using the eval_quantized_model.py:

    python 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.

Model Software
Yi 4-bit quantized models AWQ and CUDA
Yi 8-bit quantized models GPTQ and CUDA

Hardware requirements

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

Chat models
Model Minimum VRAM Recommended GPU Example
Yi-6B-Chat 15 GB RTX 3090
RTX 4090
A10
A30
Yi-6B-Chat-4bits 4 GB RTX 3060
RTX 4060
Yi-6B-Chat-8bits 8 GB RTX 3070
RTX 4060
Yi-34B-Chat 72 GB 4 x RTX 4090
A800 (80GB)
Yi-34B-Chat-4bits 20 GB RTX 3090
RTX 4090
A10
A30
A100 (40GB)
Yi-34B-Chat-8bits 38 GB 2 x RTX 3090
2 x RTX 4090
A800 (40GB)

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

Model batch=1 batch=4 batch=16 batch=32
Yi-6B-Chat 12 GB 13 GB 15 GB 18 GB
Yi-6B-Chat-4bits 4 GB 5 GB 7 GB 10 GB
Yi-6B-Chat-8bits 7 GB 8 GB 10 GB 14 GB
Yi-34B-Chat 65 GB 68 GB 76 GB > 80 GB
Yi-34B-Chat-4bits 19 GB 20 GB 30 GB 40 GB
Yi-34B-Chat-8bits 35 GB 37 GB 46 GB 58 GB
Base models
Model Minimum VRAM Recommended GPU Example
Yi-6B 15 GB RTX3090
RTX4090
A10
A30
Yi-6B-200K 50 GB A800 (80 GB)
Yi-34B 72 GB 4 x RTX 4090
A800 (80 GB)
Yi-34B-200K 200 GB 4 x A800 (80 GB)

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Learning hub

If you want to learn Yi, you can find a wealth of helpful educational resources here. ⬇️

Welcome to the Yi learning hub!

Whether you're a seasoned developer or a newcomer, you can find a wealth of helpful educational resources to enhance your understanding and skills with Yi models, including insightful blog posts, comprehensive video tutorials, hands-on guides, and more.

The content you find here has been generously contributed by knowledgeable Yi experts and passionate enthusiasts. We extend our heartfelt gratitude for your invaluable contributions!

At the same time, we also warmly invite you to join our collaborative effort by contributing to Yi. If you have already made contributions to Yi, please don't hesitate to showcase your remarkable work in the table below.

With all these resources at your fingertips, you're ready to start your exciting journey with Yi. Happy learning! 🥳

Tutorials

English tutorials
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🟢 Why Yi?

🌎 Ecosystem

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

💦 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-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.

⚙️ Quantitation

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.

🛠️ 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.

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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📌 Benchmarks

📊 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.

Chat model performance

Evaluation methods and challenges. ⬇️
  • Evaluation methods: we evaluated various benchmarks using both zero-shot and few-shot methods, except for TruthfulQA.
  • Zero-shot vs. few-shot: in chat models, the zero-shot approach is more commonly employed.
  • Evaluation strategy: our evaluation strategy involves generating responses while following instructions explicitly or implicitly (such as using few-shot examples). We then isolate relevant answers from the generated text.
  • Challenges faced: some models are not well-suited to produce output in the specific format required by instructions in few datasets, which leads to suboptimal results.

*: C-Eval results are evaluated on the validation datasets

📊 Base model performance

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.

Base model performance

Evaluation methods. ⬇️
  • Disparity in results: while benchmarking open-source models, a disparity has been noted between results from our pipeline and those reported by public sources like OpenCompass.
  • Investigation findings: a deeper investigation reveals that variations in prompts, post-processing strategies, and sampling techniques across models may lead to significant outcome differences.
  • Uniform benchmarking process: our methodology aligns with the original benchmarks—consistent prompts and post-processing strategies are used, and greedy decoding is applied during evaluations without any post-processing for the generated content.
  • Efforts to retrieve unreported scores: for scores that were not reported by the original authors (including scores reported with different settings), we try to get results with our pipeline.
  • Extensive model evaluation: 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.
  • Special configurations: 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".
  • Falcon-180B caveat: Falcon-180B was not tested on QuAC and OBQA due to technical constraints. Its performance score is an average from other tasks, and considering the generally lower scores of these two tasks, Falcon-180B's capabilities are likely not underestimated.

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🟢 Who can use Yi?

Everyone! 🙌 ✅

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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.

yi contributors

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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 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 for commercial use, with automatic permission granted upon application. All usage must adhere to the Yi Series Models Community License Agreement 2.1. For free commercial use, you only need to send an email to get official commercial permission.

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