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https://github.com/internlm/internlm-wqx


https://github.com/internlm/internlm-wqx

internlm-20b internlm2 llm

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    InternLM2-WQX

    

      

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[![license](https://raw.githubusercontent.com/InternLM/InternLM/main/assets/license.svg)](./LICENSE)



InternLM2-WQX-20B 🤗  ｜ InternLM2-WQX-VL-20B 🤗 




# Introduction



InternLM2-WQX与InternLM2-WQX-VL是InternLM团队于2024年高考前夕最新推出的文曲星系列模型。



高考覆盖各类学科及题型，同时因其开考前的“绝密性”，被视作中国最具权威的考试之一，成为评估考生综合能力的“试金石”。这一面向人类设计的高难度综合性测试，目前普遍被研究者用于考察大模型的智能水平。InternLM2-WQX系列模型在2024年高考评测集[GAOKAO-Eval](https://github.com/open-compass/GAOKAO-Eval)上取得了优异的成绩，综合表现与GPT-4o相当，且超越了国内外一系列开源大模型，体现了InternLM2-WQX系列模型优秀的性能。



我们即将更新关于文曲星系列模型数据准备的相关说明，敬请期待。



# Model Zoo



| Model                       | HuggingFace                          | ModelScope                           | Release Date |

| --------------------------- | ----------------------------------------- | ---------------------------------------- | ------------ |

| **InternLM2-WQX-20B**          | [🤗internlm2-wqx-20b](https://huggingface.co/internlm/internlm2-wqx-20b) | [ internlm2-wqx-20b](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm2-wqx-20b/summary) | 2024-06-04   |

| **InternLM2-WQX-VL-20B**          | [🤗internlm2-wqx-vl-20b](https://huggingface.co/internlm/internlm2-wqx-vl-20b) | [ internlm2-wqx-vl-20b](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm2-wqx-vl-20b/summary) | 2024-06-04   |



## MD5 Check



### LLM权重文件的md5值

```

md5sum ./*

5209adfd6ef7d1724848ff0372362568  ./model-00001-of-00004.safetensors

e37ee2eafecfed543d10dca75998204e  ./model-00002-of-00004.safetensors

ea3da8035b0c2a31c369dd463adf9b52  ./model-00003-of-00004.safetensors

f1ff218f801c69fd4c12c534b64e1b60  ./model-00004-of-00004.safetensors

```



### MLLM权重文件的md5值

```

md5sum ./*

158657dbae9bc369d67cf4bfbdfaaf71  ./pytorch_model-00001-of-00005.bin

c21db8ac1315c10df768f6c3ae3f2825  ./pytorch_model-00002-of-00005.bin

ebc4b0b70e8e9f1adc0b728558d650fb  ./pytorch_model-00003-of-00005.bin

eaa393a66dc632d0a6f0f7d815c439bb  ./pytorch_model-00004-of-00005.bin

7e6e3237d99a7e8bd7ca9ba10747bfdb  ./pytorch_model-00005-of-00005.bin



./clip_l_560_pro7b/*

97b05f40ee9826eda467489eed65f85c  ./clip_l_560_pro7b/pytorch_model.bin

```



# Quick Start



### 快速调用**InternLM2-WQX-20B**语言模型



使用transformers 后端进行推理



```python

import torch

from transformers import AutoModelForCausalLM, AutoTokenizer



device = "cuda"



tokenizer = AutoTokenizer.from_pretrained("internlm/internlm2-wqx-20b", trust_remote_code=True)

model = AutoModelForCausalLM.from_pretrained(

    "internlm/internlm2-wqx-20b",

    torch_dtype=torch.bfloat16,

    trust_remote_code=True

).to(device).eval()



query = "已知圆柱和圆锥的底面半径相等，侧面积相等，且它们的高均为$ \\sqrt { 3 }$，则圆锥的体积为（ ）．\nA. $ 2 \\sqrt { 3 } \\pi$\nB. $ 3 \\sqrt { 3 } \\pi$\nC. $ 6 \\sqrt { 3 } \\pi$\nD. $ 9 \\sqrt { 3 } \\pi$"



inputs = tokenizer(query, return_tensors="pt")



inputs = inputs["input_ids"].to(device)



gen_kwargs = {"max_length": 1024, "do_sample": False}



outputs = model.generate(inputs, **gen_kwargs)

outputs = outputs[0].cpu().tolist()[len(inputs[0]) :]



response = tokenizer.decode(outputs, skip_special_tokens=True)

print(response)

```



使用vllm 后端进行推理：



```python

from vllm import LLM, SamplingParams



model_name = "internlm/internlm2-wqx-20b"

prompts = ["已知圆柱和圆锥的底面半径相等，侧面积相等，且它们的高均为$ \\sqrt { 3 }$，则圆锥的体积为（ ）．\nA. $ 2 \\sqrt { 3 } \\pi$\nB. $ 3 \\sqrt { 3 } \\pi$\nC. $ 6 \\sqrt { 3 } \\pi$\nD. $ 9 \\sqrt { 3 } \\pi$"]

sampling_params = SamplingParams(temperature=0.0, max_tokens=1024)



llm = LLM(

    model=model_name,

    trust_remote_code=True,

    enforce_eager=True,

)



outputs = llm.generate(prompts, sampling_params)



for output in outputs:

    prompt = output.prompt

    generated_text = output.outputs[0].text

    print(f"Prompt: {prompt!r}, \nGenerated text: {generated_text!r}")

```



### **InternLM2-WQX-20B**语言模型的 Web UI



使用transformers后端进行推理：



```

python web_ui_wqx.py -m internlm/internlm2-wqx-20b

```



### 快速调用**InternLM2-WQX-VL-20B**视觉语言模型



使用transformers后端进行推理:



```python

from PIL import Image

from io import BytesIO

import requests

from transformers import AutoModel, AutoTokenizer, AutoModelForCausalLM

import torch

from infer_wqx_vl import process_query_and_image, HD_transform



model_path = "internlm/internlm2-wqx-vl-20b"

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

model = AutoModel.from_pretrained(model_path, torch_dtype=torch.bfloat16, trust_remote_code=True).cuda().eval()

model.cuda().half()

model.tokenizer = tokenizer



image_url = "https://ks-1302698447.cos.ap-shanghai.myqcloud.com/img/phymerge.png"

query = "体育课上两位同学在室内羽毛球场进行羽毛球比赛，羽毛球在空中上升的运动轨迹如图中虚线所示，考虑空气阻力，羽毛球加速度方向示意图可能正确的是（\u3000\u3000） \nA:  \nB:   \nC:  \nD: "



response = requests.get(image_url)

image = Image.open(BytesIO(response.content))

embeds, im_mask = process_query_and_image(query, image, model, HD_transform)



outputs = model.generate(inputs_embeds=embeds, im_mask=im_mask,

                            temperature=0.0, max_new_tokens=256, num_beams=1,

                            do_sample=False, repetition_penalty=1.0)

output_token = outputs[0]

output_text = model.tokenizer.decode(output_token, add_special_tokens=False)

print(output_text)

#   斜向下

# 答案是：C

```

针对这个选项里面有图片的考题，我们将图片进行了合并并标记上``来让语言模型能理解多图考题。 当前示例展示的是已经拼接好的图片，详细的图像预处理请参考[GAOKAO-Eval](https://github.com/open-compass/GAOKAO-Eval)中的多模态处理工具。



### **InternLM2-WQX-VL-20B**语言模型的 Web UI



使用transformers后端进行推理：



```

python web_ui_wqx_vl.py -m internlm/internlm2-wqx-vl-20b

```



# Citation



```bibtex

@misc{2024internlm2wqx,

    title={https://github.com/InternLM/InternLM-WQX},

    author={InternLM Team},

    howpublished = {\url{https://github.com/InternLM/InternLM-WQX}},

    year={2024}

}

```