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? Hugging Face • ? ModelScope • ? wisemodel
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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.
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Yi-9B-200K is open-sourced and available to the public.Yi-9B is open-sourced and available to the public.Yi-9B stands out as the top performer 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.
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).
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-6B-200K and Yi-34B-200K, are open-sourced and available to the public.Yi-6B and Yi-34B, are open-sourced and available to the public.[ Back to top ⬆️ ]
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.
| Model | Intro | Default context window | Pretrained tokens | Training Data Date |
|---|---|---|---|---|
| 6B series models | They are suitable for personal and academic use. | 4K | 3T | Up to June 2023 |
| 9B series models | It is the best at coding and math in the Yi series models. | Yi-9B is continuously trained based on Yi-6B, using 0.8T tokens. | ||
| 34B series models | They are 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. | 3T |
For chat models
However, this higher diversity might amplify certain existing issues, including:
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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)
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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.
git clone https://github.com/01-ai/Yi.git
cd yi
pip 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.
from transformers import AutoModelForCausalLM, AutoTokenizer
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.
python quick_start.pyThen you can see an output similar to the one below. ?
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.
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
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]))[ Back to top ⬆️ ]
Make sure you've installed Docker and nvidia-container-toolkit.
docker run -it --gpus all
-v : /models
ghcr.io/01-ai/yi:latest
Alternatively, you can pull the Yi Docker image from registry.lingyiwanwu.com/ci/01-ai/yi:latest.
You can perform inference with Yi chat or base models as below.
The steps are similar to pip - Perform inference with Yi chat model.
Note that the only difference is to set model_path = ' instead of model_path = '.
The steps are similar to pip - Perform inference with Yi base model.
Note that the only difference is to set --model instead of model .
conda-lock to generate fully reproducible lock files for conda environments. micromamba for installing these dependencies.
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.
git clone [email protected]:ggerganov/llama.cpp.git2.1 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-GGUF2.2 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.ggufTo perform inference with the Yi model, you can use one of the following methods.
Method 1: Perform inference in terminal
Method 2: Perform inference in web
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.
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.
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:
...
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://0.0.0.0:8080To 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.
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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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bash finetune/scripts/run_sft_Yi_6b.shOnce finished, you can compare the finetuned model and the base model with the following command:
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:
{ "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.shMake 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 ~/.bashrcThen, 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==2.7For 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:
|-- $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.mdfinetune/yi_example_dataset has example datasets, which are modified from BAAI/COIG
|-- $DATA_PATH
|--data
|-- train.jsonl
|-- eval.jsonlcd into the scripts folder, copy and paste the script, and run. For example:
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.
cd finetune/scripts
bash run_eval.shThen you'll see the answer from both the base model and the finetuned model.
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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:
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:
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:
python eval_quantized_model.py --model /quantized_model --trust_remote_codepython 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:
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:
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:
python eval_quantized_model.py --model /quantized_model --trust_remote_code[ Back to top ⬆️ ]
If 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) |
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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 |
|---|---|---|
| 使用 Dify、Meilisearch、零一万物模型实现最简单的 RAG 应用（三）：AI 电影推荐 | 2024-05-20 | 苏洋 |
| 使用autodl服务器，在A40显卡上运行， Yi-34B-Chat-int4模型，并使用vllm优化加速，显存占用42G，速度18 words-s | 2024-05-20 | fly-iot |
| Yi-VL 最佳实践 | 2024-05-20 | ModelScope |
| 一键运行零一万物新鲜出炉Yi-1.5-9B-Chat大模型 | 2024-05-13 | Second State |
| 零一万物开源Yi-1.5系列大模型 | 2024-05-13 | 刘聪 |
| 零一万物Yi-1.5系列模型发布并开源！ 34B-9B-6B 多尺寸，魔搭社区推理微调最佳实践教程来啦！ | 2024-05-13 | ModelScope |
| Yi-34B 本地部署简单测试 | 2024-05-13 | 漆妮妮 |
| 驾辰龙跨Llama持Wasm，玩转Yi模型迎新春过大年（上） | 2024-05-13 | Words worth |
| 驾辰龙跨Llama持Wasm，玩转Yi模型迎新春过大年（下篇） | 2024-05-13 | Words worth |
| Ollama新增两个命令，开始支持零一万物Yi-1.5系列模型 | 2024-05-13 | AI工程师笔记 |
| 使用零一万物 200K 模型和 Dify 快速搭建模型应用 | 2024-05-13 | 苏洋 |
| (持更) 零一万物模型折腾笔记：社区 Yi-34B 微调模型使用 | 2024-05-13 | 苏洋 |
| Python+ERNIE-4.0-8K-Yi-34B-Chat大模型初探 | 2024-05-11 | 江湖评谈 |
| 技术布道 Vue及Python调用零一万物模型和Prompt模板（通过百度千帆大模型平台） | 2024-05-11 | MumuLab |
| 多模态大模型Yi-VL-plus体验 效果很棒 | 2024-04-27 | 大家好我是爱因 |
| 使用autodl服务器，两个3090显卡上运行， Yi-34B-Chat-int4模型，并使用vllm优化加速，显存占用42G，速度23 words-s | 2024-04-27 | fly-iot |
| Getting Started with Yi-1.5-9B-Chat | 2024-04-27 | Second State |
| 基于零一万物yi-vl-plus大模型简单几步就能批量生成Anki图片笔记 | 2024-04-24 | 正经人王同学 |
| 【AI开发：语言】一、Yi-34B超大模型本地部署CPU和GPU版 | 2024-04-21 | My的梦想已实现 |
| 【Yi-34B-Chat-Int4】使用4个2080Ti显卡11G版本，运行Yi-34B模型，5年前老显卡是支持的，可以正常运行，速度 21 words-s，vllm要求算力在7以上的显卡就可以 | 2024-03-22 | fly-iot |
| 零一万物大模型部署+微调总结 | 2024-03-22 | v_wus |
| 零一万物Yi大模型vllm推理时Yi-34B或Yi-6bchat重复输出的解决方案 | 2024-03-02 | 郝铠锋 |
| Yi-34B微调训练 | 2024-03-02 | lsjlnd |
| 实测零一万物Yi-VL多模态语言模型：能准确“识图吃瓜” | 2024-02-02 | 苏洋 |
| 零一万物开源Yi-VL多模态大模型，魔搭社区推理&微调最佳实践来啦！ | 2024-01-26 | ModelScope |
| 单卡 3 小时训练 Yi-6B 大模型 Agent：基于 Llama Factory 实战 | 2024-01-22 | 郑耀威 |
| 零一科技Yi-34B Chat大模型环境搭建&推理 | 2024-01-15 | 要养家的程序员 |
| 基于LLaMA Factory，单卡3小时训练专属大模型 Agent | 2024-01-15 | 机器学习社区 |
| 双卡 3080ti 部署 Yi-34B 大模型 - Gradio + vLLM 踩坑全记录 | 2024-01-02 | 漆妮妮 |
| 【大模型部署实践-3】3个能在3090上跑起来的4bits量化Chat模型（baichuan2-13b、InternLM-20b、Yi-34b） | 2024-01-02 | aq_Seabiscuit |
| 只需 24G 显存，用 vllm 跑起来 Yi-34B 中英双语大模型 | 2023-12-28 | 漆妮妮 |
| 零一万物模型官方 Yi-34B 模型本地离线运行部署使用笔记（物理机和docker两种部署方式），200K 超长文本内容，34B 干翻一众 70B 模型，打榜分数那么高，这模型到底行不行？ | 2023-12-28 | 代码讲故事 |
| LLM - 大模型速递之 Yi-34B 入门与 LoRA 微调 | 2023-12-18 | BIT_666 |
| 通过vllm框架进行大模型推理 | 2023-12-18 | 土山炮 |
| CPU 混合推理，非常见大模型量化方案：“二三五六” 位量化方案 | 2023-12-12 | 苏洋 |
| 零一万物模型折腾笔记：官方 Yi-34B 模型基础使用 | 2023-12-10 | 苏洋 |
| Running Yi-34B-Chat locally using LlamaEdge | 2023-11-30 | Second State |
| 本地运行零一万物 34B 大模型，使用 Llama.cpp & 21G 显存 | 2023-11-26 | 苏洋 |
| 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 |
| Deliverable | Date | Author |
|---|---|---|
| Run dolphin-2.2-yi-34b on IoT Devices | 2023-11-30 | Second State |
| 只需 24G 显存，用 vllm 跑起来 Yi-34B 中英双语大模型 | 2023-12-28 | 漆妮妮 |
| Install Yi 34B Locally - Chinese English Bilingual LLM | 2023-11-05 | Fahd Mirza |
| Dolphin Yi 34b - Brand New Foundational Model TESTED | 2023-11-27 | Matthew Berman |
| Yi-VL-34B 多模态大模型 - 用两张 A40 显卡跑起来 | 2024-01-28 | 漆妮妮 |
| 4060Ti 16G显卡安装零一万物最新开源的Yi-1.5版大语言模型 | 2024-05-14 | titan909 |
| Yi-1.5: True Apache 2.0 Competitor to LLAMA-3 | 2024-05-13 | Prompt Engineering |
| Install Yi-1.5 Model Locally - Beats Llama 3 in Various Benchmarks | 2024-05-13 | Fahd Mirza |
| how to install Ollama and run Yi 6B | 2024-05-13 | Ridaa Davids |
| 地表最强混合智能AI助手：llama3_70B+Yi_34B+Qwen1.5_110B | 2024-05-04 | 朱扎特 |
| ChatDoc学术论文辅助--基于Yi-34B和langchain进行PDF知识库问答 | 2024-05-03 | 朱扎特 |
| 基于Yi-34B的领域知识问答项目演示 | 2024-05-02 | 朱扎特 |
| 使用RTX4090+GaLore算法 全参微调Yi-6B大模型 | 2024-03-24 | 小工蚂创始人 |
| 无内容审查NSFW大语言模型Yi-34B-Chat蒸馏版测试,RolePlay,《天龙八部》马夫人康敏,本地GPU,CPU运行 | 2024-03-20 | 刘悦的技术博客 |
| 无内容审查NSFW大语言模型整合包,Yi-34B-Chat,本地CPU运行,角色扮演潘金莲 | 2024-03-16 | 刘悦的技术博客 |
| 量化 Yi-34B-Chat 并在单卡 RTX 4090 使用 vLLM 部署 | 2024-03-05 | 白鸽巢 |
| Yi-VL-34B（5）：使用3个3090显卡24G版本，运行Yi-VL-34B模型，支持命令行和web界面方式，理解图片的内容转换成文字 | 2024-02-27 | fly-iot |
| Win环境KoboldCpp本地部署大语言模型进行各种角色扮演游戏 | 2024-02-25 | 魚蟲蟲 |
| 无需显卡本地部署Yi-34B-Chat进行角色扮演游戏 P2 | 2024-02-23 | 魚蟲蟲 |
| 【wails】（2）：使用go-llama.cpp 运行 yi-01-6b大模型，使用本地CPU运行，速度还可以，等待下一版本更新 | 2024-02-20 | fly-iot |
| 【xinference】（6）：在autodl上，使用xinference部署yi-vl-chat和qwen-vl-chat模型，可以使用openai调用成功 | 2024-02-06 | fly-iot |
| 无需显卡本地部署Yi-34B-Chat进行角色扮演游戏 P1 | 2024-02-05 | 魚蟲蟲 |
| 2080Ti部署YI-34B大模型 xinference-oneapi-fastGPT本地知识库使用指南 | 2024-01-30 | 小饭护法要转码 |
| Best Story Writing AI Model - Install Yi 6B 200K Locally on Windows | 2024-01-22 | Fahd Mirza |
| Mac 本地运行大语言模型方法与常见问题指南（Yi 34B 模型+32 GB 内存测试） | 2024-01-21 | 小吴苹果机器人 |
| 【Dify知识库】（11）：Dify0.4.9改造支持MySQL，成功接入yi-6b 做对话，本地使用fastchat启动，占8G显存，完成知识库配置 | 2024-01-21 | fly-iot |
| 这位LLM先生有点暴躁,用的是YI-6B的某个量化版,#LLM #大语言模型 #暴躁老哥 | 2024-01-20 | 晓漫吧 |
| 大模型推理 NvLink 桥接器有用吗｜双卡 A6000 测试一下 | 2024-01-17 | 漆妮妮 |
| 大模型推理 A40 vs A6000 谁更强 - 对比 Yi-34B 的单、双卡推理性能 | 2024-01-15 | 漆妮妮 |
| C-Eval 大语言模型评测基准- 用 LM Evaluation Harness + vLLM 跑起来 | 2024-01-11 | 漆妮妮 |
| 双显卡部署 Yi-34B 大模型 - vLLM + Gradio 踩坑记录 | 2024-01-01 | 漆妮妮 |
| 手把手教学！使用 vLLM 快速部署 Yi-34B-Chat | 2023-12-26 | 白鸽巢 |
| 如何训练企业自己的大语言模型？Yi-6B LORA微调演示 #小工蚁 | 2023-12-21 | 小工蚂创始人 |
| Yi-34B（4）：使用4个2080Ti显卡11G版本，运行Yi-34B模型，5年前老显卡是支持的，可以正常运行，速度 21 words/s | 2023-12-02 | fly-iot |
| 使用autodl服务器，RTX 3090 * 3 显卡上运行， Yi-34B-Chat模型，显存占用60G | 2023-12-01 | fly-iot |
| 使用autodl服务器，两个3090显卡上运行， Yi-34B-Chat-int4模型，用vllm优化，增加 --num-gpu 2，速度23 words/s | 2023-12-01 | fly-iot |
| Yi大模型一键本地部署 技术小白玩转AI | 2023-12-01 | 技术小白玩转AI |
| 01.AI's Yi-6B: Overview and Fine-Tuning | 2023-11-28 | AI Makerspace |
| Yi 34B Chat LLM outperforms Llama 70B | 2023-11-27 | DLExplorer |
| How to run open source models on mac Yi 34b on m3 Max | 2023-11-26 | TECHNO PREMIUM |
| Yi-34B - 200K - The BEST & NEW CONTEXT WINDOW KING | 2023-11-24 | Prompt Engineering |
| Yi 34B : The Rise of Powerful Mid-Sized Models - Base,200k & Chat | 2023-11-24 | Sam Witteveen |
| 在IoT设备运行破解版李开复大模型dolphin-2.2-yi-34b（还可作为私有OpenAI API服务器） | 2023-11-15 | Second State |
| Run dolphin-2.2-yi-34b on IoT Devices (Also works as a Private OpenAI API Server) | 2023-11-14 | Second State |
| How to Install Yi 34B 200K Llamafied on Windows Laptop | 2023-11-11 | Fahd Mirza |
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.
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")[ Back to top ⬆️ ]
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
.: +
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.
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For detailed capabilities of the Yi series model, see Yi: Open Foundation Models by 01.AI.
@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.
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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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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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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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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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