Internlm2 20b

🚀 InternLM

The second generation of the InternLM model, InternLM2, offers high-performance language processing with support for ultra-long contexts and comprehensive performance enhancement.

🚀 Quick Start

The second generation of the InternLM model, InternLM2, includes models at two scales: 7B and 20B. For user and researcher convenience, we have open-sourced four versions of each scale of the model:

• internlm2-base: A high-quality and highly adaptable model base, serving as an excellent starting point for deep domain adaptation.
• internlm2 (recommended): Built upon the internlm2-base, this version has further pretrained on domain-specific corpus. It shows outstanding performance in evaluations while maintaining robust general language abilities, making it our recommended choice for most applications.
• internlm2-chat-sft: Based on the Base model, it undergoes supervised human alignment training.
• internlm2-chat (recommended): Optimized for conversational interaction on top of the internlm2-chat-sft through RLHF, it excels in instruction adherence, empathetic chatting, and tool invocation.

The base model of InternLM2 has the following technical features:

• Effective support for ultra-long contexts of up to 200,000 characters: The model nearly perfectly achieves "finding a needle in a haystack" in long inputs of 200,000 characters. It also leads among open-source models in performance on long-text tasks such as LongBench and L-Eval.
• Comprehensive performance enhancement: Compared to the previous generation model, it shows significant improvements in various capabilities, including reasoning, mathematics, and coding.

 

InternLM ^HOT

 

💻Github Repo • 🤔Reporting Issues • 📜Technical Report

✨ Features

• Ultra-long Context Support: Effectively handle contexts up to 200,000 characters, excelling in long-text tasks.
• Comprehensive Performance Enhancement: Significantly improved in reasoning, mathematics, and coding compared to the previous generation.
• Multiple Model Versions: Offer four versions for different application scenarios, with recommended options for most use cases.

📦 Installation

No specific installation steps provided in the original document.

💻 Usage Examples

Basic Usage

To load the InternLM2-20B model using Transformers, use the following code:

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("internlm/internlm2-20b", trust_remote_code=True)
# Set `torch_dtype=torch.float16` to load model in float16, otherwise it will be loaded as float32 and might cause OOM Error.
model = AutoModelForCausalLM.from_pretrained("internlm/internlm2-20b", torch_dtype=torch.float16, trust_remote_code=True).cuda()
model = model.eval()
inputs = tokenizer(["A beautiful flower"], return_tensors="pt")
for k,v in inputs.items():
    inputs[k] = v.cuda()
gen_kwargs = {"max_length": 128, "top_p": 0.8, "temperature": 0.8, "do_sample": True, "repetition_penalty": 1.0}
output = model.generate(**inputs, **gen_kwargs)
output = tokenizer.decode(output[0].tolist(), skip_special_tokens=True)
print(output)
# A beautiful flower with a long history of use in Ayurveda and traditional Chinese medicine. Known for its ability to help the body adapt to stress, it is a calming and soothing herb. It is used for its ability to help promote healthy sleep patterns, calm the nervous system and to help the body adapt to stress. It is also used for its ability to help the body deal with the symptoms of anxiety and depression. It is also used for its ability to help the body adapt to stress. It is also used for its ability to help the body adapt to stress. It is also used for its ability to help the

Advanced Usage

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("internlm/internlm2-20b", trust_remote_code=True)
# `torch_dtype=torch.float16`  can load the model with float16 precision, otherwise transformers will load the model as float32, which may cause insufficient video memory
model = AutoModelForCausalLM.from_pretrained("internlm/internlm2-20b", torch_dtype=torch.float16, trust_remote_code=True).cuda()
model = model.eval()
inputs = tokenizer(["Coming to the beautiful nature"], return_tensors="pt")
for k,v in inputs.items():
    inputs[k] = v.cuda()
gen_kwargs = {"max_length": 128, "top_p": 0.8, "temperature": 0.8, "do_sample": True, "repetition_penalty": 1.0}
output = model.generate(**inputs, **gen_kwargs)
output = tokenizer.decode(output[0].tolist(), skip_special_tokens=True)
print(output)
# Coming to the beautiful nature, we admire the beautiful scenery of nature and feel the breath of nature.
# Today, I came to the beautiful Longwan Park. The scenery here is beautiful, with green mountains, clear waters, and the sounds of birds and the fragrance of flowers. As soon as I walked into the park, I was stunned by the sight in front of me: on the green lawn, colorful flowers were in full bloom, emitting bursts of fragrance. The gentle breeze blew, and the flowers swayed with the wind, as if nodding and smiling at us. In the distance, the majestic mountains rolled on and on, like a giant dragon flying in the air. At the foot of the mountain, a clear river flowed quietly, and the fish in the river swam freely.

📚 Documentation

Performance Evaluation

We have evaluated InternLM2 on several important benchmarks using the open-source evaluation tool OpenCompass. Some of the evaluation results are shown in the table below. You are welcome to visit the OpenCompass Leaderboard for more evaluation results.

• The evaluation results were obtained from OpenCompass, 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.

⚠️ Important Note

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.

🔧 Technical Details

The base model of InternLM2 supports ultra-long contexts up to 200,000 characters, enabling it to handle long-text tasks effectively. It also shows comprehensive performance enhancement in various capabilities compared to the previous generation model.

📄 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)/申请表（中文）. 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}
}