license: other
license_name: yi-license
license_link: LICENSE
widget:
- example_title: Yi-34B
- text: >-
There's a place where time stands still. A place of breath taking wonder,
but also
output:
text: >2-
an eerie sense that something is just not right…
Between the two worlds lies The Forgotten Kingdom - home to creatures
long since thought extinct and ancient magic so strong it defies belief!
Only here can you find what has been lost for centuries: An Elixir Of
Life which will restore youth and vitality if only those who seek its
power are brave enough to face up against all manner of dangers lurking
in this mysterious land! But beware; some say there may even exist
powerful entities beyond our comprehension whose intentions towards
humanity remain unclear at best ---- they might want nothing more than
destruction itself rather then anything else from their quest after
immortality (and maybe someone should tell them about modern medicine)?
In any event though – one thing remains true regardless : whether or not
success comes easy depends entirely upon how much effort we put into
conquering whatever challenges lie ahead along with having faith deep
down inside ourselves too ;) So let’s get started now shall We?
pipeline_tag: text-generation
Introduction
The Yi series models are large language models trained from scratch by developers at 01.AI.
News
🔔 2023/11/15: The commercial licensing agreement for the Yi series models is set to be updated.
🎯 2023/11/05: The base model of Yi-6B-200K and Yi-34B-200K.
This release contains two base models with the same parameter sizes of previous release, except that the context window is extended to 200K.
🎯 2023/11/02: The base model of Yi-6B and Yi-34B.
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.
Model Performance
| Model | MMLU | CMMLU | C-Eval | GAOKAO | BBH | Common-sense Reasoning | Reading Comprehension | Math & Code |
|---|---|---|---|---|---|---|---|---|
| 5-shot | 5-shot | 5-shot | 0-shot | 3-shot@1 | - | - | - | |
| LLaMA2-34B | 62.6 | - | - | - | 44.1 | 69.9 | 68.0 | 26.0 |
| LLaMA2-70B | 68.9 | 53.3 | - | 49.8 | 51.2 | 71.9 | 69.4 | 36.8 |
| Baichuan2-13B | 59.2 | 62.0 | 58.1 | 54.3 | 48.8 | 64.3 | 62.4 | 23.0 |
| Qwen-14B | 66.3 | 71.0 | 72.1 | 62.5 | 53.4 | 73.3 | 72.5 | 39.8 |
| Skywork-13B | 62.1 | 61.8 | 60.6 | 68.1 | 41.7 | 72.4 | 61.4 | 24.9 |
| InternLM-20B | 62.1 | 59.0 | 58.8 | 45.5 | 52.5 | 78.3 | - | 30.4 |
| Aquila-34B | 67.8 | 71.4 | 63.1 | - | - | - | - | - |
| Falcon-180B | 70.4 | 58.0 | 57.8 | 59.0 | 54.0 | 77.3 | 68.8 | 34.0 |
| Yi-6B | 63.2 | 75.5 | 72.0 | 72.2 | 42.8 | 72.3 | 68.7 | 19.8 |
| Yi-6B-200K | 64.0 | 75.3 | 73.5 | 73.9 | 42.0 | 72.0 | 69.1 | 19.0 |
| Yi-34B | 76.3 | 83.7 | 81.4 | 82.8 | 54.3 | 80.1 | 76.4 | 37.1 |
| Yi-34B-200K | 76.1 | 83.6 | 81.9 | 83.4 | 52.7 | 79.7 | 76.6 | 36.3 |
While benchmarking open-source models, we have observed a disparity between the results generated by our pipeline and those reported in public sources (e.g. OpenCompass). Upon conducting a more in-depth investigation of this difference, we have discovered that various models may employ different prompts, post-processing strategies, and sampling techniques, potentially resulting in significant variations in the outcomes. Our prompt and post-processing strategy remains consistent with the original benchmark, and greedy decoding is employed during evaluation without any post-processing for the generated content. For scores that were not reported by the original authors (including scores reported with different settings), we try to get results with our pipeline.
To evaluate the model's capability extensively, we adopted the methodology outlined in Llama2. Specifically, we included PIQA, SIQA, HellaSwag, WinoGrande, ARC, OBQA, and CSQA to assess common sense reasoning. SquAD, QuAC, and BoolQ were incorporated to evaluate reading comprehension. CSQA was exclusively tested using a 7-shot setup, while all other tests were conducted with a 0-shot configuration. Additionally, we introduced GSM8K (8-shot@1), MATH (4-shot@1), HumanEval (0-shot@1), and MBPP (3-shot@1) under the category "Math & Code". Due to technical constraints, we did not test Falcon-180 on QuAC and OBQA; the score is derived by averaging the scores on the remaining tasks. Since the scores for these two tasks are generally lower than the average, we believe that Falcon-180B's performance was not underestimated.
Usage
Feel free to create an issue if you encounter any problem when using the Yi series models.
1. Prepare development environment
1.1 Docker
The best approach to try the Yi series models is through Docker with GPUs. We provide the following docker images to help you get started.
registry.lingyiwanwu.com/ci/01-ai/yi:latestghcr.io/01-ai/yi:latest
Note that the latest tag always points to the latest code in the main
branch. To test a stable version, please replace it with a specific
tag.
1.2 Local development environment
We 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, we utilize micromamba for installing these dependencies.
To install the dependencies, please follow these steps:
- Install
micromambaby following the instructions available here. - Execute
micromamba install -y -n yi -f conda-lock.ymlto create a conda environment namedyiand install the necessary dependencies.
2. Download the model (optional)
By default, the model weights and tokenizer will be downloaded from HuggingFace automatically in the next step. You can also download them manually from the following places:
- ModelScope
- WiseModel (Search for
Yi) - Mirror site (remember to extract the content with
tar)
3. Examples
3.1 Use the base model
python demo/text_generation.py
To reuse the downloaded models in the previous step, you can provide the extra
--model argument:
python demo/text_generation.py --model /path/to/model
Or if you'd like to get your hands dirty:
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("01-ai/Yi-34B", device_map="auto", torch_dtype="auto", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("01-ai/Yi-34B", trust_remote_code=True)
inputs = tokenizer("There's a place where time stands still. A place of breath taking wonder, but also", return_tensors="pt")
max_length = 256
outputs = model.generate(
inputs.input_ids.cuda(),
max_length=max_length,
eos_token_id=tokenizer.eos_token_id,
do_sample=True,
repetition_penalty=1.3,
no_repeat_ngram_size=5,
temperature=0.7,
top_k=40,
top_p=0.8,
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Output
Prompt: There's a place where time stands still. A place of breath taking wonder, but also
Generation: There's a place where time stands still. A place of breath taking wonder, but also of great danger. A place where the very air you breathe could kill you. A place where the only way to survive is to be prepared. The place is called the Arctic. The Arctic is a vast, frozen wilderness. It is a place of extremes. The temperatures can drop to -40 degrees Celsius. The winds can reach speeds of 100 kilometers per hour. The sun can shine for 24 hours a day, or not at all for weeks on end. The Arctic is also a place of great beauty. The ice and snow are a pristine white. The sky is a deep blue. The sunsets are spectacular. But the Arctic is also a place of great danger. The ice can be treacherous. The winds can be deadly. The sun can be blinding. The Arctic is a place where the only way to survive is to be prepared. The Arctic is a place of extremes. The temperatures can drop to -40 degrees Celsius. The winds can reach speeds of 100 kilometers per hour. The sun can shine for 24 hours a day, or not at all for weeks on end. The Arctic is a place of great beauty. The ice and snow are a
For more advanced usage, please refer to the doc.
3.2 Finetuning from the base model:
bash finetune/scripts/run_sft_Yi_6b.sh
Once finished, you can compare the finetuned model and the base model with the following command:
bash finetune/scripts/run_eval.sh
For more advanced usage like fine-tuning based on your custom data, please refer the doc.
3.3 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, please read the doc
AWQ
python quantization/awq/quant_autoawq.py \
--model /base_model \
--output_dir /quantized_model \
--trust_remote_code
Once finished, you can then evaluate the resulted model as follows:
python quantization/awq/eval_quantized_model.py \
--model /quantized_model \
--trust_remote_code
For more detailed explanation, please read the doc
Ecosystem
🤗 You are encouraged to create a PR and share your awesome work built on top of the Yi series models.
- Serving
- ScaleLLM: Efficiently run Yi models locally.
- Quantization
- Finetuning
FAQ
Will you release the chat version?
Yes, the chat version will be released around the end of November 2023.
What dataset was this trained with?
The dataset we use contains Chinese & English only. We used approximately 3T tokens. The detailed number and its construction will be described in the upcoming technical report.
Disclaimer
We use data compliance checking algorithms during the training process, to ensure the compliance of the trained model to the best of our ability. Due to complex data and the diversity of language model usage scenarios, we cannot guarantee that the model will generate correct, and reasonable output in all scenarios. Please be aware that there is still a risk of the model producing problematic outputs. We will not be responsible for any risks and issues resulting from misuse, misguidance, illegal usage, and related misinformation, as well as any associated data security concerns.
License
The source code in this repo is licensed under the Apache 2.0 license. The Yi series models are fully open for academic research and free commercial usage with permission via applications. All usage must adhere to the Model License Agreement 2.0. To apply for the official commercial license, please contact us ([email protected]).