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license: mit |
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# Chuxin-1.6B-Base |
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## 介绍 (Introduction) |
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**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。 |
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**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。 |
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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](https://arxiv.org/pdf/2405.04828) |
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**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. |
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**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. |
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If you would like to learn more about the Chuxin-1.6B open-source model, we suggest you refer to our [technical report](https://arxiv.org/pdf/2405.04828). |
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## 快速使用（Quickstart） |
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您可以通过以下代码轻松调用： |
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You can easily call the model with the following code: |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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tokenizer = AutoTokenizer.from_pretrained("chuxin-llm/Chuxin-1.6B-Base", trust_remote_code=True) |
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model = AutoModelForCausalLM.from_pretrained("chuxin-llm/Chuxin-1.6B-Base", device_map="auto", trust_remote_code=True, bf16=True).eval() |
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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') |
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inputs = inputs.to(model.device) |
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pred = model.generate(**inputs, max_new_tokens=15, do_sample=False) |
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print(tokenizer.decode(pred.cpu()[0], skip_special_tokens=True)) |
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# 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）... |
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``` |
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## 评测效果（Evaluation） |
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### (常识推理和阅读理解) Common Sense Reasoning and Reading Comprehension tasks |
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| Model | size | ARC-c |ARC-e |Boolq |Copa |Hellaswag |OpenbookQA |Piqa |Sciq |Winogrande |Avg| |
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|:--------------|:----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:| |
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| Gemma | 2B | 48.98 | 78.45 | 69.51 | 84 | 71.73 | 39.8 | 78.02 | 94.3 | 65.51 | 70.03 | |
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| H2O-Danube† | 1.8B | 35.84 | 62.29 | 65.81 | - | 68.20 | 37.6 | 76.93 | - | 61.96 | - | |
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| Qwen1.5 | 1.8B | 37.03 | 67.51 | 66.64 | 78 | 61.60 | 34.40 | 73.99 | 93 | 61.56 | 63.74 | |
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| StableLM 2 | 1.6B | 43.52 |69.44 | 75.5 | 84 | 70.3 | 39.6 | 76.82 | 96.1 | 64.17 | 68.82 | |
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| OpenLlama† | 3B | 34 |69| 68| -| 49| 40| 75| -| 62 |-| |
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| CT-LLM | 2B | 34.81 | 65.49 | 62.45 | 74 | 54.77 | 33.4 | 71.38 | 90.6 | 57.85 | 60.63 | |
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| TinyLLama | 1.1B | 34.81 | 67.47 | 63.15 | 74 | 60 | 34.6 | 73.12 | 88.8 | 58.88 | 61.64 | |
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| OLMo | 1B | 34.22 | 67.55 | 61.4 | 82 | 63.96 | 36.4 | 75.1 | 86.7 | 60.3 | 63.07 | |
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| Chuxin-1.6B-Base | 1.6B | 39.68 | 71.38 | 71.25 | 83 | 66.09 | 35.00 | 77.09 | 95 | 63.54 | 66.89 | |
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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。 |
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Models with † denote that we directly report the scores from the corresponding paper, and others are from our implementation. |
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### Open LLM LeaderBoard |
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| Model | size | ARC-c |HellaSwag|MMLU |TruthfulQA |Winogrande |GSM-8k |Avg |Avg wo GSM| |
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|:--------------|:----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:| |
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| Gemma | 2B | 48.98 | 71.73 | 42.47 | 33 | 65.51 |10.08| 45.3 | 52.34 | |
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| H2O-Danube | 1.8B | 39. 68 | 69.75 | 25.97 | 33.63 | 64.17| 2.05 | 39.21 |46.64| |
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| Qwen1.5† | 1.8B | 37.88 | 61.42 | 46.71 | 39.43 | 60.3 | 33.59 | 46.55 | 49.15| |
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| StableLM 2 | 1.6B | 43.52 |70.3 | 39.8 | 36.61 | 64.17 | 17.29 | 45.28 | 50.88 | |
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| OpenLlama† | 3B | 39.9 | 71.6 | 27.1 | 34.8 | 67 | 0.9 |40.3|48.08| |
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| CT-LLM | 2B | 34.81 | 54.77 | 37.81 | 39.81 | 57.85 | 7.35 | 38.73 | 45.01| |
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| TinyLLama | 1.1B | 33.87 | 60.31 | 26.04 | 37.32 | 59.51 | 1.44 | 36.42 |43.41| |
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| OLMo | 1B | 34.22 | 63.96 | 35.44 | 35.53 | 62.67 | 9.86 | 41.81 |48.2| |
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| Chuxin-1.6B-Base | 1.6B | 39.68 | 66.09 | 41.07 | 37.65 | 63.54 | 12.66 | 43.45 |49.61| |
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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。 |
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Models with † denote that we directly report the scores from the Open LLM Leaderboard, and others are from our implementation. |
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### CMMLU, C-Eval and HumanEval |
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| Model | size | C-Eval |CMMLU|HUMANEVAL | |
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|:--------------|:----------:|:-----------:|:-----------:|:-----------:| |
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| Gemma | 2B | 31 | 31.06 | 9.51| |
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| Qwen1.5 | 1.8B | 59.38 | 57.08 | 23.17 | |
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| StableLM 2 | 1.6B | 29.27 |30.1 | 7.32 | |
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| CT-LLM | 2B | 36.78 | 36.4 | 9.15 | |
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| Chuxin-1.6B-Base | 1.6B | 39.31 | 37.11 | 9.76 | |
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## 引用 (Citation) |
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如果你觉得我们的工作对你有帮助，欢迎引用！ |
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If you find our work helpful, feel free to give us a cite. |
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``` |
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@article{chuxin, |
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title={CHUXIN: 1.6B TECHNICAL REPORT}, |
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author={Xiaomin Zhuang, Yufan Jiang, Qiaozhi He, Zhihua Wu}, |
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journal={arXiv preprint arXiv:2405.04828}, |
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year={2024} |
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} |
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``` |
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<br> |
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