AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference.

It is also now supported by continuous batching server vLLM, allowing use of AWQ models for high-throughput concurrent inference in multi-user server scenarios. Note that, at the time of writing, overall throughput is still lower than running vLLM with unquantised models, however using AWQ enables using much smaller GPUs which can lead to easier deployment and overall cost savings. For example, a 70B model can be run on 1 x 48GB GPU instead of 2 x 80GB.

Repositories available

Prompt template: YulanChat

The following is a conversation between a human and an AI assistant namely YuLan, developed by GSAI, Renmin University of China. The AI assistant gives helpful, detailed, and polite answers to the user's questions.
[|Human|]:{prompt}
[|AI|]:

Licensing

The creator of the source model has listed its license as mit, and this quantization has therefore used that same license.

As this model is based on Llama 2, it is also subject to the Meta Llama 2 license terms, and the license files for that are additionally included. It should therefore be considered as being claimed to be licensed under both licenses. I contacted Hugging Face for clarification on dual licensing but they do not yet have an official position. Should this change, or should Meta provide any feedback on this situation, I will update this section accordingly.

In the meantime, any questions regarding licensing, and in particular how these two licenses might interact, should be directed to the original model repository: RUC-GSAI-YuLan's YuLan Chat 2 13B.

Provided files and AWQ parameters

For my first release of AWQ models, I am releasing 128g models only. I will consider adding 32g as well if there is interest, and once I have done perplexity and evaluation comparisons, but at this time 32g models are still not fully tested with AutoAWQ and vLLM.

Models are released as sharded safetensors files.

Branch	Bits	GS	AWQ Dataset	Seq Len	Size
main	4	128	wikitext	4096	7.64 GB

Serving this model from vLLM

Documentation on installing and using vLLM can be found here.

When using vLLM as a server, pass the --quantization awq parameter, for example:

python3 python -m vllm.entrypoints.api_server --model TheBloke/YuLan-Chat-2-13B-AWQ --quantization awq

When using vLLM from Python code, pass the quantization=awq parameter, for example:

python

from vllm import LLM, SamplingParams

prompts = [
    "Hello, my name is",
    "The president of the United States is",
    "The capital of France is",
    "The future of AI is",
]
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)

llm = LLM(model="TheBloke/YuLan-Chat-2-13B-AWQ", quantization="awq")

outputs = llm.generate(prompts, sampling_params)

# Print the outputs.
for output in outputs:
    prompt = output.prompt
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

How to use this AWQ model from Python code

Install the necessary packages

Requires: AutoAWQ 0.0.2 or later

pip3 install autoawq

If you have problems installing AutoAWQ using the pre-built wheels, install it from source instead:

shell

pip3 uninstall -y autoawq
git clone https://github.com/casper-hansen/AutoAWQ
cd AutoAWQ
pip3 install .

You can then try the following example code

python

from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer

model_name_or_path = "TheBloke/YuLan-Chat-2-13B-AWQ"

# Load model
model = AutoAWQForCausalLM.from_quantized(model_name_or_path, fuse_layers=True,
                                          trust_remote_code=False, safetensors=True)
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, trust_remote_code=False)

prompt = "Tell me about AI"
prompt_template=f'''The following is a conversation between a human and an AI assistant namely YuLan, developed by GSAI, Renmin University of China. The AI assistant gives helpful, detailed, and polite answers to the user's questions.
[|Human|]:{prompt}
[|AI|]:

'''

print("\n\n*** Generate:")

tokens = tokenizer(
    prompt_template,
    return_tensors='pt'
).input_ids.cuda()

# Generate output
generation_output = model.generate(
    tokens,
    do_sample=True,
    temperature=0.7,
    top_p=0.95,
    top_k=40,
    max_new_tokens=512
)

print("Output: ", tokenizer.decode(generation_output[0]))

# Inference can also be done using transformers' pipeline
from transformers import pipeline

print("*** Pipeline:")
pipe = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    max_new_tokens=512,
    do_sample=True,
    temperature=0.7,
    top_p=0.95,
    top_k=40,
    repetition_penalty=1.1
)

print(pipe(prompt_template)[0]['generated_text'])

Compatibility

The files provided are tested to work with AutoAWQ, and vLLM.

Huggingface Text Generation Inference (TGI) is not yet compatible with AWQ, but a PR is open which should bring support soon: TGI PR #781.

Discord

For further support, and discussions on these models and AI in general, join us at:

TheBloke AI's Discord server

Thanks, and how to contribute

Thanks to the chirper.ai team!

Thanks to Clay from gpus.llm-utils.org!

I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.

If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.

Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.

Patreon: https://patreon.com/TheBlokeAI
Ko-Fi: https://ko-fi.com/TheBlokeAI

Special thanks to: Aemon Algiz.

Patreon special mentions: Alicia Loh, Stephen Murray, K, Ajan Kanaga, RoA, Magnesian, Deo Leter, Olakabola, Eugene Pentland, zynix, Deep Realms, Raymond Fosdick, Elijah Stavena, Iucharbius, Erik Bjäreholt, Luis Javier Navarrete Lozano, Nicholas, theTransient, John Detwiler, alfie_i, knownsqashed, Mano Prime, Willem Michiel, Enrico Ros, LangChain4j, OG, Michael Dempsey, Pierre Kircher, Pedro Madruga, James Bentley, Thomas Belote, Luke @flexchar, Leonard Tan, Johann-Peter Hartmann, Illia Dulskyi, Fen Risland, Chadd, S_X, Jeff Scroggin, Ken Nordquist, Sean Connelly, Artur Olbinski, Swaroop Kallakuri, Jack West, Ai Maven, David Ziegler, Russ Johnson, transmissions 11, John Villwock, Alps Aficionado, Clay Pascal, Viktor Bowallius, Subspace Studios, Rainer Wilmers, Trenton Dambrowitz, vamX, Michael Levine, 준교 김, Brandon Frisco, Kalila, Trailburnt, Randy H, Talal Aujan, Nathan Dryer, Vadim, 阿明, ReadyPlayerEmma, Tiffany J. Kim, George Stoitzev, Spencer Kim, Jerry Meng, Gabriel Tamborski, Cory Kujawski, Jeffrey Morgan, Spiking Neurons AB, Edmond Seymore, Alexandros Triantafyllidis, Lone Striker, Cap'n Zoog, Nikolai Manek, danny, ya boyyy, Derek Yates, usrbinkat, Mandus, TL, Nathan LeClaire, subjectnull, Imad Khwaja, webtim, Raven Klaugh, Asp the Wyvern, Gabriel Puliatti, Caitlyn Gatomon, Joseph William Delisle, Jonathan Leane, Luke Pendergrass, SuperWojo, Sebastain Graf, Will Dee, Fred von Graf, Andrey, Dan Guido, Daniel P. Andersen, Nitin Borwankar, Elle, Vitor Caleffi, biorpg, jjj, NimbleBox.ai, Pieter, Matthew Berman, terasurfer, Michael Davis, Alex, Stanislav Ovsiannikov

Thank you to all my generous patrons and donaters!

And thank you again to a16z for their generous grant.

Original model card: RUC-GSAI-YuLan's YuLan Chat 2 13B

YuLan-Chat: An Open-Source Bilingual Chatbot

YuLan-Chat models are chat-based large language models, which are developed by the researchers in GSAI, Renmin University of China (YuLan, which represents Yulan Magnolia, is the campus flower of Renmin University of China). The newest version is developed by continually-pretraining and instruction-tuning LLaMA-2 with high-quality English and Chinese data. The model has the following technical characteristics:

Due to continued pre-training on high-quality Chinese-English bilingual data, the language ability of the model has been improved.
To well support Chinese and longer inputs and outputs, we expand the original vocabulary with Chinese words and extend the maximum length of LLaMA-2. It can support 8k context now.
To well activate the bilingual instruction following capacity, we construct high-quality bilingual instructions, and perform multi-stage instruction-tuning.

YuLan-Chat系列模型是中国人民大学高瓴人工智能学院师生共同开发的支持聊天的大语言模型（名字"玉兰"取自中国人民大学校花）。最新版本基于LLaMA-2进行了中英文双语的继续预训练和指令微调。该版模型具有如下技术特点：

由于在高质量中英双语数据上进行了继续预训练，模型的语言能力得到提高；

为了更好的支持中文和更长的输入输出，对原版LLaMA-2的词表及长度进行了扩充，目前可支持8k上下文；

为了让模型更好地服从用户指令，构建了高质量双语指令数据集，并行了多阶段指令微调。

Model Zoo

Due to the license limitation, for models based on LLaMA, we only provide the weight difference with the original checkpoints; for models based on LLaMA-2, they can be used directly. Please check the Usage section for more details.

Limitations: Despite our efforts to reduce potential security issues during the model's usage and encourage the generation of text that aligns with ethical and legal requirements, the language model is based on probabilistic generation, which means it may still produce unexpected outputs. For instance, the generated responses may contain biases, discrimination, or other harmful content. Please do not propagate such content. We do not assume any responsibility for any consequences resulting from the dissemination of harmful information.

由于许可证的限制，基于LLaMA的模型我们仅提供与官方模型的差值，基于LLaMA-2的模型可直接使用，具体请参见使用方法章节。

局限性：尽管我们尝试减少模型在使用中可能出现的安全性问题，并鼓励模型生成符合道德和法律要求的文本，但由于语言模型基于概率生成的范式，模型仍然可能会产生意外的输出。例如，生成的响应可能包含偏见、歧视或其他有害内容。请不要传播此类内容。我们对因传播有害信息而造成的任何后果不承担任何责任。

Model	Backbone	Extended Vocab	Extended Length	Continue PT	SFT	Released Date
YuLan-Chat-2-13B	LLaMA2-13B	✅ 51,190	✅ 8,192	✅	✅	2023.8.2
YuLan-LLaMA-2-13B	LLaMA2-13B	✅ 51,190	✅ 8,192	✅	❌	2023.8.2
YuLan-Chat-1-65B-v2	LLaMA-65B	✅ 51,190	❌ 2,048	✅	✅	2023.8.2
YuLan-Chat-1-13B-v1	LLaMA-13B	❌ 32,000	❌ 2,048	❌	✅	2023.6.8
YuLan-Chat-1-65B-v1	LLaMA-65B	❌ 32,000	❌ 2,048	❌	✅	2023.6.8

Evaluation

We evaluate our YuLan-Chat model on several Chinese and English benchmarks. The evaluation results are shown as follows.

我们在中英文的一些基准测试上对YuLan-Chat进行了评价，其结果如下。

MMLU

MMLU (Massive Multitask Language Understanding) is a benchmark designed to measure knowledge acquired during pretraining by evaluating models exclusively in zero-shot and few-shot settings.

MMLU是一个评估模型知识量的常用的英文基准测试集。

Model	STEM	Social Science	Humanities	Others	Avg.
YuLan-Chat-1-13B-v1	39.6	57.8	42.6	57.6	49.4
YuLan-Chat-1-65B-v1	49.2	71.7	57.7	66.7	61.3
YuLan-Chat-1-65B-v2	46.3	67.9	56.9	63.9	58.7
LLaMA-2-13B	44.6	64.2	53.9	62.2	56.2
FlagAlpha/Llama2-Chinese-13b-Chat	44.4	63.2	51.6	60.6	55.0
Linly-AI/Chinese-LLaMA-2-13B-hf	43.6	62.7	49.8	61.6	54.4
YuLan-LLaMA-2-13B	42.9	61.5	50.4	58.6	53.4
YuLan-Chat-2-13B	45.3	66.7	53.8	62.8	57.2

C-Eval

C-Eval is a comprehensive Chinese evaluation suite for foundation models.

C-Eval是一个针对基石模型综合能力的中文基准测试集。

Model	STEM	Social Science	Humanities	Others	Avg.	Avg. (Hard)
YuLan-Chat-1-13B-v1	30.2	37.4	31.9	30.7	32.0	25.7
YuLan-Chat-1-65B-v1	37.7	46.1	36.8	38.0	39.2	31.1
YuLan-Chat-1-65B-v2	39.9	55.9	47.7	43.7	45.4	31.4
LLaMA-2-13B	36.9	43.2	37.6	36.6	38.2	32.0
FlagAlpha/Llama2-Chinese-13b-Chat	36.8	44.5	36.3	36.5	38.1	30.9
Linly-AI/Chinese-LLaMA-2-13B-hf	33.7	44.8	36.6	36.5	37	27.7
YuLan-LLaMA-2-13B	35.3	46.4	41.9	37.6	39.3	28.6
YuLan-Chat-2-13B	38.9	49.7	45.0	40.8	42.6	32.2

AGI-Eval-Gaokao

AGI-Eval is a human-centric benchmark specifically designed to evaluate the general abilities of foundation models in tasks pertinent to human cognition and problem-solving. We use the sub-branch Chinese-Gaokao for evaluation.

AGI-Eval 是一个以人为中心的基准，专门设计用于评估基础模型在与人类认知和解决问题相关的任务中的一般能力。我们使用其中的"高考"分支进行评测。

Model	Avg.	Chinese	English	Geography	History	Biology	Chemistry	Physics	Math-QA	Math-Cloze
YuLan-Chat-1-13B-v1	24.3	22.4	60.1	27.6	25.5	21.9	30.0	8.0	21.1	1.7
YuLan-Chat-1-65B-v1	29.3	25.2	79.1	37.2	36.6	28.6	24.2	11.0	21.9	0.0
YuLan-Chat-1-65B-v2	37.9	31.4	80.4	50.8	56.6	33.3	29.0	32.0	24.4	0.8
LLaMA-2-13B	32.7	27.2	72.2	36.2	43.0	26.2	32.4	30.0	26.2	0.9
FlagAlpha/Llama2-Chinese-13b-Chat	31.6	26.4	70.6	35.2	38.7	28.1	28.0	29.5	25.6	2.5
Linly-AI/Chinese-LLaMA-2-13B-hf	31.1	22.8	74.8	42.2	37.9	24.3	28.0	23.0	26.5	0.0
YuLan-LLaMA-2-13B	34.2	25.2	70.3	43.2	48.5	30.0	29.5	31.0	28.5	1.7
YuLan-Chat-2-13B	39.5	37.0	85.3	46.7	51.9	43.8	38.2	29.0	23.1	0.9

Usage

Import from Huggingface Transformers

As our model is trained based on LLaMA, it can be loaded in the same way as original LLaMA.

由于我们的模型是基于LLaMA开发的，可以使用与LLaMA相同的方法加载。

Python

>>> from transformers import LlamaTokenizer, LlamaForCausalLM
>>> tokenizer = LlamaTokenizer.from_pretrained("yulan-team/YuLan-Chat-2-13b")
>>> model = LlamaForCausalLM.from_pretrained("yulan-team/YuLan-Chat-2-13b").cuda()
>>> model = model.eval()
>>> input_text = "hello"
>>> prompt = "The following is a conversation between a human and an AI assistant namely YuLan, developed by GSAI, Renmin University of China. The AI assistant gives helpful, detailed, and polite answers to the user's questions.\n[|Human|]:{}\n[|AI|]:".format(input_text)
>>> inputs = tokenizer(prompt, return_tensors='pt', padding="longest", max_length=8192, truncation=True, return_attention_mask=True, add_special_tokens=True)
>>> kwargs = {'temperature': 0.8, 'top_p': 0.95, "top_k": 50, "repetition_penalty": 1.1, "no_repeat_ngram_size": 64, "max_length": 8192, "pad_token_id": tokenizer.bos_token_id, "eos_token_id": tokenizer.eos_token_id}
>>> outputs = model.generate(inputs['input_ids'].to(model.device), attention_mask=inputs['attention_mask'].to(model.device), do_sample=True, **kwargs)
>>> print(tokenizer.batch_decode(outputs, skip_special_tokens=True)[len(prompt):])
Hello! How can I assist you today?

License

YuLan-Chat uses MIT License. All data and code in this project can only be used for academic purposes.

本项目使用MIT许可，所有的数据和代码仅供学术研究使用。

Contributors

Pre-training	Fine-tuning
Yutao Zhu (Lead), Kelong Mao, Wentong Chen, Yiding Sun, Yihan Wu, Qian Cao, Lei Zhang, Feng Wang, Qiangqiang Ren	Kun Zhou (Lead), Yushuo Chen, Zhipeng Chen, Lei Wang, Yupeng Hou, Xincheng Pang, Junyi Li, Yuhan Chen, Shufang Xie

Reference

Please kindly cite our work if it helps you.

如果我们的项目对您有帮助，请引用我们，谢谢！

BibTeX

@misc{YuLan-Chat,
  author = {YuLan-Team},
  title = {YuLan-Chat: An Open-Source Bilingual Chatbot},
  year = {2023},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/RUC-GSAI/YuLan-Chat}},
}