Chuxin-1.6B-Base

Chuxin-1.6B-Base

<br>

介绍 (Introduction)

Chuxin-1.6B-Base是16亿参数规模的模型。Chuxin-1.6B完全基于开源数据构建，在经过超大规模数据训练后，Chuxin-1.6B在各类下游任务上具有非常强的竞争力。

Chuxin-1.6B-1M是基于Chuxin-1.6B-base模型在1M窗口下训练后的结果，大海捞针实验显示其具有非常强的上下文检索能力。

如果您想了解更多关于Chuxin-1.6B开源模型的细节，我们建议您参阅我们的技术报告

Chuxin-1.6B-Base is a model with 1.6 billion parameters. Chuxin-1.6B is built entirely on open-source data. After being trained with large-scale data, Chuxin has very competitive capabilities in various downstream tasks.

Chuxin-1.6B-1M is the result of training the Chuxin-1.6B-base model with a 1M windows. Experiments such as searching for a needle in a haystack demonstrate its strong contextual retrieval abilities.

If you would like to learn more about the Chuxin-1.6B open-source model, we suggest you refer to our technical report. <br>

快速使用（Quickstart）

您可以通过以下代码轻松调用：

You can easily call the model with the following code:

from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("chuxin-llm/Chuxin-1.6B-Base", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("chuxin-llm/Chuxin-1.6B-Base", device_map="auto", trust_remote_code=True, bf16=True).eval()
inputs = tokenizer('蒙古国的首都是乌兰巴托（Ulaanbaatar）\n冰岛的首都是雷克雅未克（Reykjavik）\n埃塞俄比亚的首都是', return_tensors='pt')
inputs = inputs.to(model.device)
pred = model.generate(**inputs, max_new_tokens=15, do_sample=False)
print(tokenizer.decode(pred.cpu()[0], skip_special_tokens=True))
# 蒙古国的首都是乌兰巴托（Ulaanbaatar）\n冰岛的首都是雷克雅未克（Reykjavik）\n埃塞俄比亚的首都是亚的斯亚贝巴（Addis Ababa）...

评测效果（Evaluation）

(常识推理和阅读理解) Common Sense Reasoning and Reading Comprehension tasks

带有†的模型表示我们直接报告了相应论文中的分数，其他的则来自于我们重新测试的结果。

Models with † denote that we directly report the scores from the corresponding paper, and others are from our implementation.

Open LLM LeaderBoard

带有†的模型表示我们直接报告 Open LLM排行榜的分数，其他的则来自于我们重新测试的结果。

Models with † denote that we directly report the scores from the Open LLM Leaderboard, and others are from our implementation.

CMMLU, C-Eval and HumanEval

引用 (Citation)

如果你觉得我们的工作对你有帮助，欢迎引用！

If you find our work helpful, feel free to give us a cite.

@article{chuxin,
  title={CHUXIN: 1.6B TECHNICAL REPORT},
  author={Xiaomin Zhuang, Yufan Jiang, Qiaozhi He, Zhihua Wu},
  journal={arXiv preprint arXiv:2405.04828},
  year={2024}
}

<br>