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internlm2-chat-20b

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InternLM

Introduction

InternLM2 has open-sourced a 20 billion parameter base model and a chat model tailored for practical scenarios. The model has the following characteristics:

  • 200K Context window: Nearly perfect at finding needles in the haystack with 200K-long context, with leading performance on long-context tasks like LongBench and L-Eval. Try it with LMDeploy for 200K-context inference.

  • Outstanding comprehensive performance: Significantly better than the last generation in all dimensions, especially in reasoning, math, code, chat experience, instruction following, and creative writing, with leading performance among open-source models in similar sizes. In some evaluations, InternLM2-Chat-20B may match or even surpass ChatGPT (GPT-3.5).

  • Code interpreter & Data analysis: With code interpreter, InternLM2-Chat-20B obtains compatible performance with GPT-4 on GSM8K and MATH. InternLM2-Chat also provides data analysis capability.

  • Stronger tool use: Based on better tool utilization-related capabilities in instruction following, tool selection and reflection, InternLM2 can support more kinds of agents and multi-step tool calling for complex tasks. See examples.

InternLM2-Chat-20B

Performance Evaluation

We conducted a comprehensive evaluation of InternLM using the open-source evaluation tool OpenCompass. The evaluation covered five dimensions of capabilities: disciplinary competence, language competence, knowledge competence, inference competence, and comprehension competence. Here are some of the evaluation results, and you can visit the OpenCompass leaderboard for more evaluation results.

Dataset\Models InternLM2-7B InternLM2-Chat-7B InternLM2-20B InternLM2-Chat-20B ChatGPT GPT-4
MMLU 65.8 63.7 67.7 66.5 69.1 83.0
AGIEval 49.9 47.2 53.0 50.3 39.9 55.1
BBH 65.0 61.2 72.1 68.3 70.1 86.7
GSM8K 70.8 70.7 76.1 79.6 78.2 91.4
MATH 20.2 23.0 25.5 31.9 28.0 45.8
HumanEval 43.3 59.8 48.8 67.1 73.2 74.4
MBPP(Sanitized) 51.8 51.4 63.0 65.8 78.9 79.0
  • The evaluation results were obtained from OpenCompass (some data marked with *, which means come from the original papers), and evaluation configuration can be found in the configuration files provided by OpenCompass.
  • The evaluation data may have numerical differences due to the version iteration of OpenCompass, so please refer to the latest evaluation results of OpenCompass.

Limitations: Although we have made efforts to ensure the safety of the model during the training process and to encourage the model to generate text that complies with ethical and legal requirements, the model may still produce unexpected outputs due to its size and probabilistic generation paradigm. For example, the generated responses may contain biases, discrimination, or other harmful content. Please do not propagate such content. We are not responsible for any consequences resulting from the dissemination of harmful information.

Import from Transformers

To load the InternLM 20B Chat model using Transformers, use the following code:

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("internlm/internlm2-chat-20b", trust_remote_code=True)
# Set `torch_dtype=torch.float16` to load model in float16, otherwise it will be loaded as float32 and cause OOM Error.
model = AutoModelForCausalLM.from_pretrained("internlm/internlm2-chat-20b", torch_dtype=torch.float16, trust_remote_code=True).cuda()
model = model.eval()
response, history = model.chat(tokenizer, "hello", history=[])
print(response)
# Hello! How can I help you today?
response, history = model.chat(tokenizer, "please provide three suggestions about time management", history=history)
print(response)

The responses can be streamed using stream_chat:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_path = "internlm/internlm2-chat-20b"
model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype=torch.float16, trust_remote_code=True).cuda()
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

model = model.eval()
length = 0
for response, history in model.stream_chat(tokenizer, "Hello", history=[]):
    print(response[length:], flush=True, end="")
    length = len(response)

Deployment

LMDeploy

LMDeploy is a toolkit for compressing, deploying, and serving LLM, developed by the MMRazor and MMDeploy teams.

pip install lmdeploy

You can run batch inference locally with the following python code:

import lmdeploy
pipe = lmdeploy.pipeline("internlm/internlm2-chat-20b")
response = pipe(["Hi, pls intro yourself", "Shanghai is"])
print(response)

Or you can launch an OpenAI compatible server with the following command:

lmdeploy serve api_server internlm/internlm2-chat-20b --model-name internlm2-chat-20b --server-port 23333

Then you can send a chat request to the server:

curl http://localhost:23333/v1/chat/completions \
    -H "Content-Type: application/json" \
    -d '{
    "model": "internlm2-chat-20b",
    "messages": [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Introduce deep learning to me."}
    ]
    }'

Find more details in the LMDeploy documentation

vLLM

Launch OpenAI compatible server with vLLM>=0.3.2:

pip install vllm

python -m vllm.entrypoints.openai.api_server --model internlm/internlm2-chat-20b --served-model-name internlm2-chat-20b --trust-remote-code

Then you can send a chat request to the server:

curl http://localhost:8000/v1/chat/completions \
    -H "Content-Type: application/json" \
    -d '{
    "model": "internlm2-chat-20b",
    "messages": [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Introduce deep learning to me."}
    ]
    }'

Find more details in the vLLM documentation

Open Source License

The code is licensed under Apache-2.0, while model weights are fully open for academic research and also allow free commercial usage. To apply for a commercial license, please fill in the application form (English)/็”ณ่ฏท่กจ๏ผˆไธญๆ–‡๏ผ‰. For other questions or collaborations, please contact [email protected].

Citation 

@misc{cai2024internlm2,
      title={InternLM2 Technical Report},
      author={Zheng Cai and Maosong Cao and Haojiong Chen and Kai Chen and Keyu Chen and Xin Chen and Xun Chen and Zehui Chen and Zhi Chen and Pei Chu and Xiaoyi Dong and Haodong Duan and Qi Fan and Zhaoye Fei and Yang Gao and Jiaye Ge and Chenya Gu and Yuzhe Gu and Tao Gui and Aijia Guo and Qipeng Guo and Conghui He and Yingfan Hu and Ting Huang and Tao Jiang and Penglong Jiao and Zhenjiang Jin and Zhikai Lei and Jiaxing Li and Jingwen Li and Linyang Li and Shuaibin Li and Wei Li and Yining Li and Hongwei Liu and Jiangning Liu and Jiawei Hong and Kaiwen Liu and Kuikun Liu and Xiaoran Liu and Chengqi Lv and Haijun Lv and Kai Lv and Li Ma and Runyuan Ma and Zerun Ma and Wenchang Ning and Linke Ouyang and Jiantao Qiu and Yuan Qu and Fukai Shang and Yunfan Shao and Demin Song and Zifan Song and Zhihao Sui and Peng Sun and Yu Sun and Huanze Tang and Bin Wang and Guoteng Wang and Jiaqi Wang and Jiayu Wang and Rui Wang and Yudong Wang and Ziyi Wang and Xingjian Wei and Qizhen Weng and Fan Wu and Yingtong Xiong and Chao Xu and Ruiliang Xu and Hang Yan and Yirong Yan and Xiaogui Yang and Haochen Ye and Huaiyuan Ying and Jia Yu and Jing Yu and Yuhang Zang and Chuyu Zhang and Li Zhang and Pan Zhang and Peng Zhang and Ruijie Zhang and Shuo Zhang and Songyang Zhang and Wenjian Zhang and Wenwei Zhang and Xingcheng Zhang and Xinyue Zhang and Hui Zhao and Qian Zhao and Xiaomeng Zhao and Fengzhe Zhou and Zaida Zhou and Jingming Zhuo and Yicheng Zou and Xipeng Qiu and Yu Qiao and Dahua Lin},
      year={2024},
      eprint={2403.17297},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

็ฎ€ไป‹

InternLM2 ๏ผŒๅณไนฆ็”Ÿยทๆตฆ่ฏญๅคงๆจกๅž‹็ฌฌไบŒไปฃ๏ผŒๅผ€ๆบไบ†้ขๅ‘ๅฎž็”จๅœบๆ™ฏ็š„200ไบฟๅ‚ๆ•ฐๅŸบ็ก€ๆจกๅž‹ไธŽๅฏน่ฏๆจกๅž‹ ๏ผˆInternLM2-Chat-20B๏ผ‰ใ€‚ๆจกๅž‹ๅ…ทๆœ‰ไปฅไธ‹็‰น็‚น๏ผš

  • ๆœ‰ๆ•ˆๆ”ฏๆŒ20ไธ‡ๅญ—่ถ…้•ฟไธŠไธ‹ๆ–‡๏ผšๆจกๅž‹ๅœจ20ไธ‡ๅญ—้•ฟ่พ“ๅ…ฅไธญๅ‡ ไนŽๅฎŒ็พŽๅœฐๅฎž็Žฐ้•ฟๆ–‡โ€œๅคงๆตทๆž้’ˆโ€๏ผŒ่€Œไธ”ๅœจ LongBench ๅ’Œ L-Eval ็ญ‰้•ฟๆ–‡ไปปๅŠกไธญ็š„่กจ็ŽฐไนŸ่พพๅˆฐๅผ€ๆบๆจกๅž‹ไธญ็š„้ข†ๅ…ˆๆฐดๅนณใ€‚ ๅฏไปฅ้€š่ฟ‡ LMDeploy ๅฐ่ฏ•20ไธ‡ๅญ—่ถ…้•ฟไธŠไธ‹ๆ–‡ๆŽจ็†ใ€‚
  • ็ปผๅˆๆ€ง่ƒฝๅ…จ้ขๆๅ‡๏ผšๅ„่ƒฝๅŠ›็ปดๅบฆ็›ธๆฏ”ไธŠไธ€ไปฃๆจกๅž‹ๅ…จ้ข่ฟ›ๆญฅ๏ผŒๅœจๆŽจ็†ใ€ๆ•ฐๅญฆใ€ไปฃ็ ใ€ๅฏน่ฏไฝ“้ชŒใ€ๆŒ‡ไปค้ตๅพชๅ’Œๅˆ›ๆ„ๅ†™ไฝœ็ญ‰ๆ–น้ข็š„่ƒฝๅŠ›ๆๅ‡ๅฐคไธบๆ˜พ่‘—๏ผŒ็ปผๅˆๆ€ง่ƒฝ่พพๅˆฐๅŒ้‡็บงๅผ€ๆบๆจกๅž‹็š„้ข†ๅ…ˆๆฐดๅนณ๏ผŒๅœจ้‡็‚น่ƒฝๅŠ›่ฏ„ๆต‹ไธŠ InternLM2-Chat-20B ่ƒฝๆฏ”่‚ฉ็”š่‡ณ่ถ…่ถŠ ChatGPT ๏ผˆGPT-3.5๏ผ‰ใ€‚
  • ไปฃ็ ่งฃ้‡Šๅ™จไธŽๆ•ฐๆฎๅˆ†ๆž๏ผšๅœจ้…ๅˆไปฃ็ ่งฃ้‡Šๅ™จ๏ผˆcode-interpreter๏ผ‰็š„ๆกไปถไธ‹๏ผŒInternLM2-Chat-20B ๅœจ GSM8K ๅ’Œ MATH ไธŠๅฏไปฅ่พพๅˆฐๅ’Œ GPT-4 ็›ธไปฟ็š„ๆฐดๅนณใ€‚ๅŸบไบŽๅœจๆ•ฐ็†ๅ’Œๅทฅๅ…ทๆ–น้ขๅผบๅคง็š„ๅŸบ็ก€่ƒฝๅŠ›๏ผŒInternLM2-Chat ๆไพ›ไบ†ๅฎž็”จ็š„ๆ•ฐๆฎๅˆ†ๆž่ƒฝๅŠ›ใ€‚
  • ๅทฅๅ…ท่ฐƒ็”จ่ƒฝๅŠ›ๆ•ดไฝ“ๅ‡็บง๏ผšๅŸบไบŽๆ›ดๅผบๅ’Œๆ›ดๅ…ทๆœ‰ๆณ›ๅŒ–ๆ€ง็š„ๆŒ‡ไปค็†่งฃใ€ๅทฅๅ…ท็ญ›้€‰ไธŽ็ป“ๆžœๅๆ€็ญ‰่ƒฝๅŠ›๏ผŒๆ–ฐ็‰ˆๆจกๅž‹ๅฏไปฅๆ›ดๅฏ้ ๅœฐๆ”ฏๆŒๅคๆ‚ๆ™บ่ƒฝไฝ“็š„ๆญๅปบ๏ผŒๆ”ฏๆŒๅฏนๅทฅๅ…ท่ฟ›่กŒๆœ‰ๆ•ˆ็š„ๅคš่ฝฎ่ฐƒ็”จ๏ผŒๅฎŒๆˆ่พƒๅคๆ‚็š„ไปปๅŠกใ€‚ๅฏไปฅๆŸฅ็œ‹ๆ›ดๅคšๆ ทไพ‹ใ€‚

InternLM2-Chat-20B

ๆ€ง่ƒฝ่ฏ„ๆต‹

ๆˆ‘ไปฌไฝฟ็”จๅผ€ๆบ่ฏ„ๆต‹ๅทฅๅ…ท OpenCompass ไปŽๅญฆ็ง‘็ปผๅˆ่ƒฝๅŠ›ใ€่ฏญ่จ€่ƒฝๅŠ›ใ€็Ÿฅ่ฏ†่ƒฝๅŠ›ใ€ๆŽจ็†่ƒฝๅŠ›ใ€็†่งฃ่ƒฝๅŠ›ไบ”ๅคง่ƒฝๅŠ›็ปดๅบฆๅฏนInternLMๅผ€ๅฑ•ๅ…จ้ข่ฏ„ๆต‹๏ผŒ้ƒจๅˆ†่ฏ„ๆต‹็ป“ๆžœๅฆ‚ไธ‹่กจๆ‰€็คบ๏ผŒๆฌข่ฟŽ่ฎฟ้—ฎ OpenCompass ๆฆœๅ• ่Žทๅ–ๆ›ดๅคš็š„่ฏ„ๆต‹็ป“ๆžœใ€‚

่ฏ„ๆต‹้›† InternLM2-7B InternLM2-Chat-7B InternLM2-20B InternLM2-Chat-20B ChatGPT GPT-4
MMLU 65.8 63.7 67.7 66.5 69.1 83.0
AGIEval 49.9 47.2 53.0 50.3 39.9 55.1
BBH 65.0 61.2 72.1 68.3 70.1 86.7
GSM8K 70.8 70.7 76.1 79.6 78.2 91.4
MATH 20.2 23.0 25.5 31.9 28.0 45.8
HumanEval 43.3 59.8 48.8 67.1 73.2 74.4
MBPP(Sanitized) 51.8 51.4 63.0 65.8 78.9 79.0
  • ไปฅไธŠ่ฏ„ๆต‹็ป“ๆžœๅŸบไบŽ OpenCompass ่Žทๅพ—๏ผˆ้ƒจๅˆ†ๆ•ฐๆฎๆ ‡ๆณจ*ไปฃ่กจๆ•ฐๆฎๆฅ่‡ชๅŽŸๅง‹่ฎบๆ–‡๏ผ‰๏ผŒๅ…ทไฝ“ๆต‹่ฏ•็ป†่Š‚ๅฏๅ‚่ง OpenCompass ไธญๆไพ›็š„้…็ฝฎๆ–‡ไปถใ€‚
  • ่ฏ„ๆต‹ๆ•ฐๆฎไผšๅ›  OpenCompass ็š„็‰ˆๆœฌ่ฟญไปฃ่€Œๅญ˜ๅœจๆ•ฐๅ€ผๅทฎๅผ‚๏ผŒ่ฏทไปฅ OpenCompass ๆœ€ๆ–ฐ็‰ˆ็š„่ฏ„ๆต‹็ป“ๆžœไธบไธปใ€‚

ๅฑ€้™ๆ€ง๏ผš ๅฐฝ็ฎกๅœจ่ฎญ็ปƒ่ฟ‡็จ‹ไธญๆˆ‘ไปฌ้žๅธธๆณจ้‡ๆจกๅž‹็š„ๅฎ‰ๅ…จๆ€ง๏ผŒๅฐฝๅŠ›ไฟƒไฝฟๆจกๅž‹่พ“ๅ‡บ็ฌฆๅˆไผฆ็†ๅ’Œๆณ•ๅพ‹่ฆๆฑ‚็š„ๆ–‡ๆœฌ๏ผŒไฝ†ๅ—้™ไบŽๆจกๅž‹ๅคงๅฐไปฅๅŠๆฆ‚็Ž‡็”Ÿๆˆ่Œƒๅผ๏ผŒๆจกๅž‹ๅฏ่ƒฝไผšไบง็”Ÿๅ„็งไธ็ฌฆๅˆ้ข„ๆœŸ็š„่พ“ๅ‡บ๏ผŒไพ‹ๅฆ‚ๅ›žๅคๅ†…ๅฎนๅŒ…ๅซๅ่งใ€ๆญง่ง†็ญ‰ๆœ‰ๅฎณๅ†…ๅฎน๏ผŒ่ฏทๅ‹ฟไผ ๆ’ญ่ฟ™ไบ›ๅ†…ๅฎนใ€‚็”ฑไบŽไผ ๆ’ญไธ่‰ฏไฟกๆฏๅฏผ่‡ด็š„ไปปไฝ•ๅŽๆžœ๏ผŒๆœฌ้กน็›ฎไธๆ‰ฟๆ‹…่ดฃไปปใ€‚

้€š่ฟ‡ Transformers ๅŠ ่ฝฝ

้€š่ฟ‡ไปฅไธ‹็š„ไปฃ็ ๅŠ ่ฝฝ InternLM2 20B Chat ๆจกๅž‹

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("internlm/internlm2-chat-20b", trust_remote_code=True)
# `torch_dtype=torch.float16` ๅฏไปฅไปคๆจกๅž‹ไปฅ float16 ็ฒพๅบฆๅŠ ่ฝฝ๏ผŒๅฆๅˆ™ transformers ไผšๅฐ†ๆจกๅž‹ๅŠ ่ฝฝไธบ float32๏ผŒๅฏผ่‡ดๆ˜พๅญ˜ไธ่ถณ
model = AutoModelForCausalLM.from_pretrained("internlm/internlm2-chat-20b", torch_dtype=torch.float16, trust_remote_code=True).cuda()
model = model.eval()
response, history = model.chat(tokenizer, "ไฝ ๅฅฝ", history=[])
print(response)
# ไฝ ๅฅฝ๏ผๆœ‰ไป€ไนˆๆˆ‘ๅฏไปฅๅธฎๅŠฉไฝ ็š„ๅ—๏ผŸ
response, history = model.chat(tokenizer, "่ฏทๆไพ›ไธ‰ไธช็ฎก็†ๆ—ถ้—ด็š„ๅปบ่ฎฎใ€‚", history=history)
print(response)

ๅฆ‚ๆžœๆƒณ่ฟ›่กŒๆตๅผ็”Ÿๆˆ๏ผŒๅˆ™ๅฏไปฅไฝฟ็”จ stream_chat ๆŽฅๅฃ๏ผš

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_path = "internlm/internlm2-chat-20b"
model = AutoModelForCausalLM.from_pretrained(model_path, torch_dype=torch.float16, trust_remote_code=True).cuda()
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

model = model.eval()
length = 0
for response, history in model.stream_chat(tokenizer, "ไฝ ๅฅฝ", history=[]):
    print(response[length:], flush=True, end="")
    length = len(response)

้ƒจ็ฝฒ

LMDeploy

LMDeploy ็”ฑ MMDeploy ๅ’Œ MMRazor ๅ›ข้˜Ÿ่”ๅˆๅผ€ๅ‘๏ผŒๆ˜ฏๆถต็›–ไบ† LLM ไปปๅŠก็š„ๅ…จๅฅ—่ฝป้‡ๅŒ–ใ€้ƒจ็ฝฒๅ’ŒๆœๅŠก่งฃๅ†ณๆ–นๆกˆใ€‚

pip install lmdeploy

ไฝ ๅฏไปฅไฝฟ็”จไปฅไธ‹ python ไปฃ็ ่ฟ›่กŒๆœฌๅœฐๆ‰น้‡ๆŽจ็†:

import lmdeploy
pipe = lmdeploy.pipeline("internlm/internlm2-chat-20b")
response = pipe(["Hi, pls intro yourself", "Shanghai is"])
print(response)

ๆˆ–่€…ไฝ ๅฏไปฅไฝฟ็”จไปฅไธ‹ๅ‘ฝไปคๅฏๅŠจๅ…ผๅฎน OpenAI API ็š„ๆœๅŠก:

lmdeploy serve api_server internlm/internlm2-chat-20b --server-port 23333

็„ถๅŽไฝ ๅฏไปฅๅ‘ๆœๅŠก็ซฏๅ‘่ตทไธ€ไธช่Šๅคฉ่ฏทๆฑ‚:

curl http://localhost:23333/v1/chat/completions \
    -H "Content-Type: application/json" \
    -d '{
    "model": "internlm2-chat-20b",
    "messages": [
    {"role": "system", "content": "ไฝ ๆ˜ฏไธชๅ‹ๅ–„็š„AIๅŠฉๆ‰‹ใ€‚"},
    {"role": "user", "content": "ไป‹็ปไธ€ไธ‹ๆทฑๅบฆๅญฆไน ใ€‚"}
    ]
    }'

ๆ›ดๅคšไฟกๆฏ่ฏทๆŸฅ็œ‹ LMDeploy ๆ–‡ๆกฃ

vLLM

ไฝฟ็”จvLLM>=0.3.2ๅฏๅŠจๅ…ผๅฎน OpenAI API ็š„ๆœๅŠก:

pip install vllm

python -m vllm.entrypoints.openai.api_server --model internlm/internlm2-chat-20b --trust-remote-code

็„ถๅŽไฝ ๅฏไปฅๅ‘ๆœๅŠก็ซฏๅ‘่ตทไธ€ไธช่Šๅคฉ่ฏทๆฑ‚:

curl http://localhost:8000/v1/chat/completions \
    -H "Content-Type: application/json" \
    -d '{
    "model": "internlm2-chat-20b",
    "messages": [
    {"role": "system", "content": "ไฝ ๆ˜ฏไธชๅ‹ๅ–„็š„AIๅŠฉๆ‰‹ใ€‚"},
    {"role": "user", "content": "ไป‹็ปไธ€ไธ‹ๆทฑๅบฆๅญฆไน ใ€‚"}
    ]
    }'

ๆ›ดๅคšไฟกๆฏ่ฏทๆŸฅ็œ‹ vLLM ๆ–‡ๆกฃ

ๅผ€ๆบ่ฎธๅฏ่ฏ

ๆœฌไป“ๅบ“็š„ไปฃ็ ไพ็…ง Apache-2.0 ๅ่ฎฎๅผ€ๆบใ€‚ๆจกๅž‹ๆƒ้‡ๅฏนๅญฆๆœฏ็ ”็ฉถๅฎŒๅ…จๅผ€ๆ”พ๏ผŒไนŸๅฏ็”ณ่ฏทๅ…่ดน็š„ๅ•†ไธšไฝฟ็”จๆŽˆๆƒ๏ผˆ็”ณ่ฏท่กจ๏ผ‰ใ€‚ๅ…ถไป–้—ฎ้ข˜ไธŽๅˆไฝœ่ฏท่”็ณป [email protected]ใ€‚

ๅผ•็”จ 

@misc{cai2024internlm2,
      title={InternLM2 Technical Report},
      author={Zheng Cai and Maosong Cao and Haojiong Chen and Kai Chen and Keyu Chen and Xin Chen and Xun Chen and Zehui Chen and Zhi Chen and Pei Chu and Xiaoyi Dong and Haodong Duan and Qi Fan and Zhaoye Fei and Yang Gao and Jiaye Ge and Chenya Gu and Yuzhe Gu and Tao Gui and Aijia Guo and Qipeng Guo and Conghui He and Yingfan Hu and Ting Huang and Tao Jiang and Penglong Jiao and Zhenjiang Jin and Zhikai Lei and Jiaxing Li and Jingwen Li and Linyang Li and Shuaibin Li and Wei Li and Yining Li and Hongwei Liu and Jiangning Liu and Jiawei Hong and Kaiwen Liu and Kuikun Liu and Xiaoran Liu and Chengqi Lv and Haijun Lv and Kai Lv and Li Ma and Runyuan Ma and Zerun Ma and Wenchang Ning and Linke Ouyang and Jiantao Qiu and Yuan Qu and Fukai Shang and Yunfan Shao and Demin Song and Zifan Song and Zhihao Sui and Peng Sun and Yu Sun and Huanze Tang and Bin Wang and Guoteng Wang and Jiaqi Wang and Jiayu Wang and Rui Wang and Yudong Wang and Ziyi Wang and Xingjian Wei and Qizhen Weng and Fan Wu and Yingtong Xiong and Chao Xu and Ruiliang Xu and Hang Yan and Yirong Yan and Xiaogui Yang and Haochen Ye and Huaiyuan Ying and Jia Yu and Jing Yu and Yuhang Zang and Chuyu Zhang and Li Zhang and Pan Zhang and Peng Zhang and Ruijie Zhang and Shuo Zhang and Songyang Zhang and Wenjian Zhang and Wenwei Zhang and Xingcheng Zhang and Xinyue Zhang and Hui Zhao and Qian Zhao and Xiaomeng Zhao and Fengzhe Zhou and Zaida Zhou and Jingming Zhuo and Yicheng Zou and Xipeng Qiu and Yu Qiao and Dahua Lin},
      year={2024},
      eprint={2403.17297},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
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