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internlm2_5-7b-chat

Text Generation
Transformers
Safetensors
internlm2
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InternLM

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Introduction

InternLM2.5 has open-sourced a 7 billion parameter base model and a chat model tailored for practical scenarios. The model has the following characteristics:

  • Outstanding reasoning capability: State-of-the-art performance on Math reasoning, surpassing models like Llama3 and Gemma2-9B.

  • 1M Context window: Nearly perfect at finding needles in the haystack with 1M-long context, with leading performance on long-context tasks like LongBench. Try it with LMDeploy for 1M-context inference.

  • Stronger tool use: InternLM2.5 supports gathering information from more than 100 web pages, corresponding implementation has be released in MindSearch. InternLM2.5 has better tool utilization-related capabilities in instruction following, tool selection and reflection. See examples.

InternLM2.5-7B-Chat

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.

Benchmark InternLM2.5-7B-Chat Llama3-8B-Instruct Gemma2-9B-IT Yi-1.5-9B-Chat GLM-4-9B-Chat Qwen2-7B-Instruct
MMLU (5-shot) 72.8 68.4 70.9 71.0 71.4 70.8
CMMLU (5-shot) 78.0 53.3 60.3 74.5 74.5 80.9
BBH (3-shot CoT) 71.6 54.4 68.2* 69.6 69.6 65.0
MATH (0-shot CoT) 60.1 27.9 46.9 51.1 51.1 48.6
GSM8K (0-shot CoT) 86.0 72.9 88.9 80.1 85.3 82.9
GPQA (0-shot) 38.4 26.1 33.8 37.9 36.9 38.4
  • 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 InternLM2.5 7B Chat model using Transformers, use the following code:

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("internlm/internlm2_5-7b-chat", 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_5-7b-chat", 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_5-7b-chat"
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

llama.cpp

internlm/internlm2_5-7b-chat-gguf offers internlm2_5-7b-chat models in GGUF format in both half precision and various low-bit quantized versions, including q5_0, q5_k_m, q6_k, and q8_0.

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_5-7b-chat")
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_5-7b-chat --model-name internlm2_5-7b-chat --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_5-7b-chat",
    "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_5-7b-chat --served-model-name internlm2_5-7b-chat --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_5-7b-chat",
    "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 internlm@pjlab.org.cn.

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‹

InternLM2.5 ๏ผŒๅณไนฆ็”Ÿยทๆตฆ่ฏญๅคงๆจกๅž‹็ฌฌ 2.5 ไปฃ๏ผŒๅผ€ๆบไบ†้ขๅ‘ๅฎž็”จๅœบๆ™ฏ็š„70ไบฟๅ‚ๆ•ฐๅŸบ็ก€ๆจกๅž‹ไธŽๅฏน่ฏๆจกๅž‹ ๏ผˆInternLM2.5-7B-Chat๏ผ‰ใ€‚ๆจกๅž‹ๅ…ทๆœ‰ไปฅไธ‹็‰น็‚น๏ผš

  • ๅ“่ถŠ็š„ๆŽจ็†ๆ€ง่ƒฝ๏ผšๅœจๆ•ฐๅญฆๆŽจ็†ๆ–น้ขๅ–ๅพ—ไบ†ๅŒ้‡็บงๆจกๅž‹ๆœ€ไผ˜็ฒพๅบฆ๏ผŒ่ถ…่ถŠไบ† Llama3 ๅ’Œ Gemma2-9Bใ€‚
  • ๆœ‰ๆ•ˆๆ”ฏๆŒ็™พไธ‡ๅญ—่ถ…้•ฟไธŠไธ‹ๆ–‡๏ผšๆจกๅž‹ๅœจ 1 ็™พไธ‡ๅญ—้•ฟ่พ“ๅ…ฅไธญๅ‡ ไนŽๅฎŒ็พŽๅœฐๅฎž็Žฐ้•ฟๆ–‡โ€œๅคงๆตทๆž้’ˆโ€๏ผŒ่€Œไธ”ๅœจ LongBench ็ญ‰้•ฟๆ–‡ไปปๅŠกไธญ็š„่กจ็ŽฐไนŸ่พพๅˆฐๅผ€ๆบๆจกๅž‹ไธญ็š„้ข†ๅ…ˆๆฐดๅนณใ€‚ ๅฏไปฅ้€š่ฟ‡ LMDeploy ๅฐ่ฏ•็™พไธ‡ๅญ—่ถ…้•ฟไธŠไธ‹ๆ–‡ๆŽจ็†ใ€‚
  • ๅทฅๅ…ท่ฐƒ็”จ่ƒฝๅŠ›ๆ•ดไฝ“ๅ‡็บง๏ผšInternLM2.5 ๆ”ฏๆŒไปŽไธŠ็™พไธช็ฝ‘้กตๆœ้›†ๆœ‰ๆ•ˆไฟกๆฏ่ฟ›่กŒๅˆ†ๆžๆŽจ็†๏ผŒ็›ธๅ…ณๅฎž็Žฐๅทฒๅผ€ๆบๅˆฐ MindSearchใ€‚InternLM2.5 ๅ…ทๆœ‰ๆ›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€‚

InternLM2.5-7B-Chat

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‹้›†\ๆจกๅž‹ InternLM2.5-7B-Chat Llama3-8B-Instruct Gemma2-9B-IT Yi-1.5-9B-Chat GLM-4-9B-Chat Qwen2-7B-Instruct
MMLU (5-shot) 72.8 68.4 70.9 71.0 71.4 70.8
CMMLU (5-shot) 78.0 53.3 60.3 74.5 74.5 80.9
BBH (3-shot CoT) 71.6 54.4 68.2* 69.6 69.6 65.0
MATH (0-shot CoT) 60.1 27.9 46.9 51.1 51.1 48.6
GSM8K (0-shot CoT) 86.0 72.9 88.9 80.1 85.3 82.9
GPQA (0-shot) 38.4 26.1 33.8 37.9 36.9 38.4
  • 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 InternLM2.5 7B Chat ๆจกๅž‹

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("internlm/internlm2_5-7b-chat", 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_5-7b-chat", 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_5-7b-chat"
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_5-7b-chat")
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_5-7b-chat --model-name internlm2_5-7b-chat --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_5-7b-chat",
    "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_5-7b-chat --served-model-name internlm2_5-7b-chat --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_5-7b-chat",
    "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 internlm@pjlab.org.cnใ€‚

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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