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🤖 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.
💡 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.
Yi-34B-ChatYi-34B-Chat-4bitsYi-34B-Chat-8bitsYi-6B-ChatYi-6B-Chat-4bitsYi-6B-Chat-8bitsYou can try some of them interactively at:
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.
| Model | Download |
|---|---|
| Yi-34B-Chat | • 🤗 Hugging Face • 🤖 ModelScope • 🟣 wisemodel |
| Yi-34B-Chat-4bits | • 🤗 Hugging Face • 🤖 ModelScope • 🟣 wisemodel |
| Yi-34B-Chat-8bits | • 🤗 Hugging Face • 🤖 ModelScope • 🟣 wisemodel |
| Yi-6B-Chat | • 🤗 Hugging Face • 🤖 ModelScope • 🟣 wisemodel |
| Yi-6B-Chat-4bits | • 🤗 Hugging Face • 🤖 ModelScope • 🟣 wisemodel |
| Yi-6B-Chat-8bits | • 🤗 Hugging Face • 🤖 ModelScope • 🟣 wisemodel |
- 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).
| Model | Download |
|---|---|
| Yi-34B | • 🤗 Hugging Face • 🤖 ModelScope • 🟣 wisemodel |
| Yi-34B-200K | • 🤗 Hugging Face • 🤖 ModelScope • 🟣 wisemodel |
| Yi-9B | • 🤗 Hugging Face • 🤖 ModelScope • 🟣 wisemodel |
| Yi-9B-200K | • 🤗 Hugging Face • 🤖 ModelScope • 🟣 wisemodel |
| Yi-6B | • 🤗 Hugging Face • 🤖 ModelScope • 🟣 wisemodel |
| Yi-6B-200K | • 🤗 Hugging Face • 🤖 ModelScope • 🟣 wisemodel |
- 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.
For chat models
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.
💡 Tip: If you want to get started with the Yi model and explore different methods for inference, check out the Yi Cookbook.
Select one of the following paths to begin your journey with Yi!
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.
If you prefer not to deploy Yi models locally, you can explore Yi's capabilities using any of the following options.
If you want to explore more features of Yi, you can adopt one of these methods:
Yi APIs (Yi official)
Yi APIs (Replicate)
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)
Yi-34B-Chat-Playground (Replicate)
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)
Yi-34B-Chat (Yi official beta)
This tutorial guides you through every step of running Yi-34B-Chat locally on an A800 (80G) and then performing inference.
Make sure Python 3.10 or a later version is installed.
If you want to run other Yi models, see software and hardware requirements.
To set up the environment and install the required packages, execute the following command.
bash
git clone https://github.com/01-ai/Yi.gitcd yipip install -r requirements.txtYou can download the weights and tokenizer of Yi models from the following sources:
You can perform inference with Yi chat or base models as below.
Create a file named quick_start.py and copy the following content to it.
python
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)Run quick_start.py.
bash
python quick_start.pyThen you can see an output similar to the one below. 🥳
bash
Hello! How can I assist you today?Yi-34B
The steps are similar to pip - Perform inference with Yi chat model.
You can use the existing file text_generation.py.
bash
python demo/text_generation.py --model <your-model-path>Then you can see an output similar to the one below. 🥳
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
bash
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
bash
# write the quick sort algorithmdef 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 algorithmprint(quick_sort([3, 6, 8, 10, 1, 2, 1]))Install micromamba by following the instructions available here.
Execute micromamba install -y -n yi -f conda-lock.yml to create a conda environment named yi and install the necessary dependencies.
The following tutorial will guide you through every step of running a quantized model (Yi-chat-6B-2bits) locally and then performing inference.
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.
llama.cppTo clone the llama.cpp repository, run the following command.
bash
git clone git@github.com:ggerganov/llama.cpp.git2.1 To clone XeIaso/yi-chat-6B-GGUF with just pointers, run the following command.
bash
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/XeIaso/yi-chat-6B-GGUF2.2 To download a quantized Yi model (yi-chat-6b.Q2_K.gguf), run the following command.
bash
git-lfs pull --include yi-chat-6b.Q2_K.ggufTo perform inference with the Yi model, you can use one of the following methods.
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.ggufwith 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 -hto check detailed descriptions and usage.
bash
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! 🥳
To initialize a lightweight and swift chatbot, run the following command.
bash
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.ggufThen you can get an output like this:
bash
... llama_new_context_with_model: n_ctx = 2048llama_new_context_with_model: freq_base = 5000000.0llama_new_context_with_model: freq_scale = 1ggml_metal_init: allocatingggml_metal_init: found device: Apple M2 Proggml_metal_init: picking default device: Apple M2 Proggml_metal_init: ggml.metallib not found, loading from sourceggml_metal_init: GGML_METAL_PATH_RESOURCES = nilggml_metal_init: loading '/Users/yu/llama.cpp/ggml-metal.metal'ggml_metal_init: GPU name: Apple M2 Proggml_metal_init: GPU family: MTLGPUFamilyApple8 (1008)ggml_metal_init: hasUnifiedMemory = trueggml_metal_init: recommendedMaxWorkingSetSize = 11453.25 MBggml_metal_init: maxTransferRate = built-in GPUggml_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 MiBggml_backend_metal_buffer_type_alloc_buffer: allocated buffer, size = 0.02 MiB, ( 2629.45 / 10922.67)llama_build_graph: non-view tensors processed: 676/676llama_new_context_with_model: compute buffer total size = 159.19 MiBggml_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://0.0.0.0:8080To access the chatbot interface, open your web browser and enter http://0.0.0.0:8080 into the address bar.
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.
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.
bash
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.
bash
bash finetune/scripts/run_sft_Yi_6b.shOnce finished, you can compare the finetuned model and the base model with the following command:
bash
bash finetune/scripts/run_eval.shBy 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:
json
{ "prompt": "Human: Who are you? Assistant:", "chosen": "I'm Yi." }And then mount them in the container to replace the default ones:
bash
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.shMake sure you have conda. If not, use
bash
mkdir -p ~/miniconda3wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda3/miniconda.shbash ~/miniconda3/miniconda.sh -b -u -p ~/miniconda3rm -rf ~/miniconda3/miniconda.sh~/miniconda3/bin/conda init bashsource ~/.bashrcThen, create a conda env:
bash
conda create -n dev_env python=3.10 -yconda activate dev_envpip install torch==2.0.1 deepspeed==0.10 tensorboard transformers datasets sentencepiece accelerate ray==2.7For the Yi-6B model, a node with 4 GPUs, each with GPU memory larger than 60GB, is recommended.
For the Yi-34B model, because the usage of the zero-offload technique consumes a lot of CPU memory, please be careful to limit the number of GPUs in the 34B finetune training. Please use CUDA_VISIBLE_DEVICES to limit the number of GPUs (as shown in scripts/run_sft_Yi_34b.sh).
A typical hardware setup for finetuning the 34B model is a node with 8 GPUs (limited to 4 in running by CUDA_VISIBLE_DEVICES=0,1,2,3), each with GPU memory larger than 80GB, and total CPU memory larger than 900GB.
Download a LLM-base model to MODEL_PATH (6B and 34B). A typical folder of models is like:
bash
|-- $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.
bash
|-- $DATA_PATH| |-- data| | |-- train-00000-of-00001-2a1df75c6bce91ab.parquet| | |-- test-00000-of-00001-8c7c51afc6d45980.parquet| |-- dataset_infos.json| |-- README.mdfinetune/yi_example_dataset has example datasets, which are modified from BAAI/COIG
bash
|-- $DATA_PATH |--data |-- train.jsonl |-- eval.jsonlcd into the scripts folder, copy and paste the script, and run. For example:
bash
cd finetune/scripts bash run_sft_Yi_6b.shFor 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.
bash
cd finetune/scripts bash run_eval.shThen you'll see the answer from both the base model and the finetuned model.
bash
python quantization/gptq/quant_autogptq.py \ --model /base_model \ --output_dir /quantized_model \ --trust_remote_codeOnce finished, you can then evaluate the resulting model as follows:
bash
python quantization/gptq/eval_quantized_model.py \ --model /quantized_model \ --trust_remote_codeGPT-Q is a PTQ (Post-Training Quantization) method. It saves memory 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.
The quant_autogptq.py script is provided for you to perform GPT-Q quantization:
bash
python quant_autogptq.py --model /base_model \ --output_dir /quantized_model --bits 4 --group_size 128 --trust_remote_codeYou can run a quantized model using the eval_quantized_model.py:
bash
python eval_quantized_model.py --model /quantized_model --trust_remote_codebash
python quantization/awq/quant_autoawq.py \ --model /base_model \ --output_dir /quantized_model \ --trust_remote_codeOnce finished, you can then evaluate the resulting model as follows:
bash
python quantization/awq/eval_quantized_model.py \ --model /quantized_model \ --trust_remote_codeAWQ 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.
The quant_autoawq.py script is provided for you to perform AWQ quantization:
bash
python quant_autoawq.py --model /base_model \ --output_dir /quantized_model --bits 4 --group_size 128 --trust_remote_codeYou can run a quantized model using the eval_quantized_model.py:
bash
python eval_quantized_model.py --model /quantized_model --trust_remote_codeIf you want to deploy Yi models, make sure you meet the software and hardware 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 |
Before deploying Yi in your environment, make sure your hardware meets the following requirements.
| Model | Minimum VRAM | Recommended GPU Example |
|---|---|---|
| Yi-6B-Chat | 15 GB | 1 x RTX 3090 (24 GB) 1 x RTX 4090 (24 GB) 1 x A10 (24 GB) 1 x A30 (24 GB) |
| Yi-6B-Chat-4bits | 4 GB | 1 x RTX 3060 (12 GB) 1 x RTX 4060 (8 GB) |
| Yi-6B-Chat-8bits | 8 GB | 1 x RTX 3070 (8 GB) 1 x RTX 4060 (8 GB) |
| Yi-34B-Chat | 72 GB | 4 x RTX 4090 (24 GB) 1 x A800 (80GB) |
| Yi-34B-Chat-4bits | 20 GB | 1 x RTX 3090 (24 GB) 1 x RTX 4090 (24 GB) 1 x A10 (24 GB) 1 x A30 (24 GB) 1 x A100 (40 GB) |
| Yi-34B-Chat-8bits | 38 GB | 2 x RTX 3090 (24 GB) 2 x RTX 4090 (24 GB) 1 x A800 (40 GB) |
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 |
| Model | Minimum VRAM | Recommended GPU Example |
|---|---|---|
| Yi-6B | 15 GB | 1 x RTX 3090 (24 GB) 1 x RTX 4090 (24 GB) 1 x A10 (24 GB) 1 x A30 (24 GB) |
| Yi-6B-200K | 50 GB | 1 x A800 (80 GB) |
| Yi-9B | 20 GB | 1 x RTX 4090 (24 GB) |
| Yi-34B | 72 GB | 4 x RTX 4090 (24 GB) 1 x A800 (80 GB) |
| Yi-34B-200K | 200 GB | 4 x A800 (80 GB) |
Yi-34Band Yi-34B-Chat comes down to the fine-tuning approach and outcomes.
Yi-34B could be your go-to.Yi-34B-Chat might be your best bet.Where can I source fine-tuning question answering datasets?
What is the GPU memory requirement for fine-tuning Yi-34B FP16? The GPU memory needed for fine-tuning 34B FP16 hinges on the specific fine-tuning method employed. For full parameter fine-tuning, you'll need 8 GPUs each with 80 GB; however, more economical solutions like Lora require less. For more details, check out hiyouga/LLaMA-Factory. Also, consider using BF16 instead of FP16 for fine-tuning to optimize performance.
Are there any third-party platforms that support chat functionality for the Yi-34b-200k model? If you're looking for third-party Chats, options include fireworks.ai.
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! 🥳
| Deliverable | Date | Author |
|---|---|---|
| yi-openai-proxy | 2024-05-11 | 苏洋 |
| 基于零一万物 Yi 模型和 B 站构建大语言模型高质量训练数据集 | 2024-04-29 | 正经人王同学 |
| 基于视频网站和零一万物大模型构建大语言模型高质量训练数据集 | 2024-04-25 | 正经人王同学 |
| 基于零一万物yi-34b-chat-200k输入任意文章地址，点击按钮即可生成无广告或推广内容的简要笔记，并生成分享图给好友 | 2024-04-24 | 正经人王同学 |
| Food-GPT-Yi-model | 2024-04-21 | Hubert S |
Yi has a comprehensive ecosystem, offering a range of tools, services, and models to enrich your experiences and maximize productivity.
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.
python
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")💡 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>.
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.
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.
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:
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.
For detailed capabilities of the Yi series model, see Yi: Open Foundation Models by 01.AI.
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@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}}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.
*: C-Eval results are evaluated on the validation datasets
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 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.
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.
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.
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.
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:
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This work is a derivative of [The Yi Series Model You Base On] by 01.AI, used under the Apache 2.0 License.git clone https://github.com/01-ai/Yi.gitcd yipip install -r requirements.txtfrom 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)Hello! How can I assist you today?python demo/text_generation.py --model <your-model-path>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))# write the quick sort algorithmdef 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 algorithmprint(quick_sort([3, 6, 8, 10, 1, 2, 1]))GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/XeIaso/yi-chat-6B-GGUFmake -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. ...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... llama_new_context_with_model: n_ctx = 2048llama_new_context_with_model: freq_base = 5000000.0llama_new_context_with_model: freq_scale = 1ggml_metal_init: allocatingggml_metal_init: found device: Apple M2 Proggml_metal_init: picking default device: Apple M2 Proggml_metal_init: ggml.metallib not found, loading from sourceggml_metal_init: GGML_METAL_PATH_RESOURCES = nilggml_metal_init: loading '/Users/yu/llama.cpp/ggml-metal.metal'ggml_metal_init: GPU name: Apple M2 Proggml_metal_init: GPU family: MTLGPUFamilyApple8 (1008)ggml_metal_init: hasUnifiedMemory = trueggml_metal_init: recommendedMaxWorkingSetSize = 11453.25 MBggml_metal_init: maxTransferRate = built-in GPUggml_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 MiBggml_backend_metal_buffer_type_alloc_buffer: allocated buffer, size = 0.02 MiB, ( 2629.45 / 10922.67)llama_build_graph: non-view tensors processed: 676/676llama_new_context_with_model: compute buffer total size = 159.19 MiBggml_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{ "prompt": "Human: Who are you? Assistant:", "chosen": "I'm Yi." }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.shmkdir -p ~/miniconda3wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda3/miniconda.shbash ~/miniconda3/miniconda.sh -b -u -p ~/miniconda3rm -rf ~/miniconda3/miniconda.sh~/miniconda3/bin/conda init bashsource ~/.bashrcconda create -n dev_env python=3.10 -yconda activate dev_envpip install torch==2.0.1 deepspeed==0.10 tensorboard transformers datasets sentencepiece accelerate ray==2.7|-- $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| |-- ...|-- $DATA_PATH| |-- data| | |-- train-00000-of-00001-2a1df75c6bce91ab.parquet| | |-- test-00000-of-00001-8c7c51afc6d45980.parquet| |-- dataset_infos.json| |-- README.md|-- $DATA_PATH |--data |-- train.jsonl |-- eval.jsonlcd finetune/scripts bash run_sft_Yi_6b.shcd finetune/scripts bash run_eval.shpython quantization/gptq/quant_autogptq.py \ --model /base_model \ --output_dir /quantized_model \ --trust_remote_codepython quantization/gptq/eval_quantized_model.py \ --model /quantized_model \ --trust_remote_codepython quant_autogptq.py --model /base_model \ --output_dir /quantized_model --bits 4 --group_size 128 --trust_remote_codepython quantization/awq/quant_autoawq.py \ --model /base_model \ --output_dir /quantized_model \ --trust_remote_codepython quantization/awq/eval_quantized_model.py \ --model /quantized_model \ --trust_remote_codepython quant_autoawq.py --model /base_model \ --output_dir /quantized_model --bits 4 --group_size 128 --trust_remote_codefrom 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")@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}}