README.md

---
license: mit
---
# Chuxin-1.6B-1M
<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的竞争力。

**Chuxin-1.6B-1M**是基于Chuxin-1.6B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](https://xxxx)

**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 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](https://xxxx).
<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:

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("chuxin-llm/Chuxin-1.6B-1M", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("chuxin-llm/Chuxin-1.6B-1M", 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=20, 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)

| Model         | size | ARC-c |ARC-e |Boolq |Copa |Hellaswag |OpenbookQA |Piqa |Sciq |Winogrande |Avg|
|:--------------|:----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|
| chuxin-1.6B-base |  1.6B |  39.68  | 71.38     | 71.25      | 83    | 66.09     | 35.00      | 77.09     | 95     | 63.54      | 66.89     |
| chuxin-1.6B-32k |  1.6B |  39.16  | 70.66     | 67.71     | 81   | 65.69     | 35.8      | 76.88    | 94.2    | 62.51     | 65.96     |
| chuxin-1.6B-64k |  1.6B |  38.48  | 70.24     | 67.52     | 82    | 65.6     | 35.2      | 76.61     | 94.3    | 63.3      | 65.92     |
| chuxin-1.6B-128k |  1.6B |  39.08  | 69.4     | 67.71      | 80    | 65.74    | 35.4      | 76.39    | 94.1    | 63.3      | 65.68     |
| chuxin-1.6B-256k |  1.6B |  40.19  | 70.75     | 69.3      | 78    | 65.85    | 35.8     | 76.88    | 93.5     | 63.85     | 66.01    |
| chuxin-1.6B-512k |  1.6B | 40.61 |71.21| 67.77 |78| 64.82| 34.8| 76.88| 93.6| 61.88| 65.51|
| chuxin-1.6B-1M |  1.6B | 41.13| 72.26| 62.08| 75| 64.59 |34.8| 76.71| 93.33| 62.43| 64.7|

### Open LLM LeaderBoard

| Model         | size | ARC-c  |HellaSwag|MMLU |TruthfulQA |Winogrande |GSM-8k |Avg |Avg wo GSM|
|:--------------|:----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|
| chuxin-1.6B-base |  1.6B |  39.68  | 66.09     | 41.07      | 37.65    | 63.54    | 12.66     | 43.45    |49.61|
| chuxin-1.6B-32k |  1.6B |  39.16 | 65.69     | 38.63      | 35.66    | 62.51     | 11.6     | 42.21    | 48.33|
| chuxin-1.6B-64k |  1.6B |  38.48  | 65.6    | 38.43     | 35.07    | 63.3     | 11.9      | 42.13|48.18|
| chuxin-1.6B-128k |  1.6B |  39.08  | 65.74    | 37.65     | 34.89    | 63.3    | 11.07     | 41.96|48.13|
| chuxin-1.6B-256k |  1.6B |  40.19  | 65.85     | 37.16      | 35.2    | 63.85     | 10.16      | 42.07    |48.45|
| chuxin-1.6B-512k |  1.6B |  40.61| 64.82| 36.66| 33.66| 61.88| 8.11| 40.96| 47.53|
| Chuxin-1.6B-1M |  1.6B |  41.13 |64.59| 35.76| 34.67| 62.43| 6.82| 40.9| 47.72|

### 大海捞针 (needle in a haystack)

<p align="center">
    <img src="niah.png" style="width: 1200px"/>
<p>



## 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:xxx},
  year={2024}
}
```
<br>