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README.md
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---
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license: cc-by-sa-4.0
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language:
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- en
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tags:
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- text-generation-inference
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pipeline_tag: text-generation
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---
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## Original model card
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Buy me a coffee if you like this project ;)
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<a href="https://www.buymeacoffee.com/s3nh"><img src="https://www.buymeacoffee.com/assets/img/guidelines/download-assets-sm-1.svg" alt=""></a>
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#### Description
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GPTQ version, compressed, quantized. [This project](https://huggingface.co/AlpachinoNLP/Baichuan-13B-Instruction/).
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### inference
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# Original model card
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## 使用方式
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如下是一个使用Baichuan-13B-Chat进行对话的示例，正确输出为"乔戈里峰。世界第二高峰———乔戈里峰西方登山者称其为k2峰，海拔高度是8611米，位于喀喇昆仑山脉的中巴边境上"
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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from transformers.generation.utils import GenerationConfig
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tokenizer = AutoTokenizer.from_pretrained("AlpachinoNLP/Baichuan-13B-Instruction", use_fast=False, trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained("AlpachinoNLP/Baichuan-13B-Instruction", device_map="auto", torch_dtype=torch.float16, trust_remote_code=True)
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model.generation_config = GenerationConfig.from_pretrained("AlpachinoNLP/Baichuan-13B-Instruction")
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messages = []
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messages.append({"role": "Human", "content": "世界上第二高的山峰是哪座"})
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response = model.chat(tokenizer, messages)
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print(response)
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```
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## 量化部署
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Baichuan-13B 支持 int8 和 int4 量化，用户只需在推理代码中简单修改两行即可实现。请注意，如果是为了节省显存而进行量化，应加载原始精度模型到 CPU 后再开始量化；避免在 `from_pretrained` 时添加 `device_map='auto'` 或者其它会导致把原始精度模型直接加载到 GPU 的行为的参数。
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使用 int8 量化 (To use int8 quantization):
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```python
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model = AutoModelForCausalLM.from_pretrained("AlpachinoNLP/Baichuan-13B-Instruction", torch_dtype=torch.float16, trust_remote_code=True)
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model = model.quantize(8).cuda()
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```
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同样的，如需使用 int4 量化 (Similarly, to use int4 quantization):
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```python
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model = AutoModelForCausalLM.from_pretrained("AlpachinoNLP/Baichuan-13B-Instruction", torch_dtype=torch.float16, trust_remote_code=True)
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model = model.quantize(4).cuda()
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```
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## 模型详情
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### 模型结构
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<!-- Provide the basic links for the model. -->
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整体模型基于Baichuan-13B，为了获得更好的推理性能，Baichuan-13B 使用了 ALiBi 线性偏置技术，相对于 Rotary Embedding 计算量更小，对推理性能有显著提升；与标准的 LLaMA-13B 相比，生成 2000 个 tokens 的平均推理速度 (tokens/s)，实测提升 31.6%：
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| Model | tokens/s |
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| ------------ | -------- |
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| LLaMA-13B | 19.4 |
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| Baichuan-13B | 25.4 |
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具体参数和见下表
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| 模型名称 | 隐含层维度 | 层数 | 头数 | 词表大小 | 总参数量 | 训练数据（tokens） | 位置编码 | 最大长度 |
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| ------------ | ---------- | ---- | ---- | -------- | -------------- | ------------------ | ----------------------------------------- | -------- |
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| Baichuan-7B | 4,096 | 32 | 32 | 64,000 | 7,000,559,616 | 1.2万亿 | [RoPE](https://arxiv.org/abs/2104.09864) | 4,096 |
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| Baichuan-13B | 5,120 | 40 | 40 | 64,000 | 13,264,901,120 | 1.4万亿 | [ALiBi](https://arxiv.org/abs/2108.12409) | 4,096 |
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## 训练详情
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数据集主要由三部分组成：
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* 在 [sharegpt_zh](https://huggingface.co/datasets/QingyiSi/Alpaca-CoT/tree/main/ShareGPT) 数据集中筛选的出 13k 高质量数据。
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* [lima](https://huggingface.co/datasets/GAIR/lima)
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* 按照任务类型挑选的 2.3k 高质量中文数据集，每个任务类型的数据量在 100 条左右。
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硬件：8*A40
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## 测评结果
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## [CMMLU](https://github.com/haonan-li/CMMLU)
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| Model 5-shot | STEM | Humanities | Social Sciences | Others | China Specific | Average |
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| ---------------------------------------------------------- | :-------: | :--------: | :-------------: | :------: | :------------: | :------: |
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| Baichuan-7B | 34.4 | 47.5 | 47.6 | 46.6 | 44.3 | 44.0 |
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| Vicuna-13B | 31.8 | 36.2 | 37.6 | 39.5 | 34.3 | 36.3 |
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| Chinese-Alpaca-Plus-13B | 29.8 | 33.4 | 33.2 | 37.9 | 32.1 | 33.4 |
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| Chinese-LLaMA-Plus-13B | 28.1 | 33.1 | 35.4 | 35.1 | 33.5 | 33.0 |
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| Ziya-LLaMA-13B-Pretrain | 29.0 | 30.7 | 33.8 | 34.4 | 31.9 | 32.1 |
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| LLaMA-13B | 29.2 | 30.8 | 31.6 | 33.0 | 30.5 | 31.2 |
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| moss-moon-003-base (16B) | 27.2 | 30.4 | 28.8 | 32.6 | 28.7 | 29.6 |
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| Baichuan-13B-Base | 41.7 | 61.1 | 59.8 | 59.0 | 56.4 | 55.3 |
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| Baichuan-13B-Chat | 42.8 | **62.6** | **59.7** | **59.0** | **56.1** | **55.8** |
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| **Baichuan-13B-Instruction** | **44.50** | 61.16 | 59.07 | 58.34 | 55.55 | 55.61 |
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| Model zero-shot | STEM | Humanities | Social Sciences | Others | China Specific | Average |
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| ------------------------------------------------------------ | :-------: | :--------: | :-------------: | :-------: | :------------: | :-------: |
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| [ChatGLM2-6B](https://huggingface.co/THUDM/chatglm2-6b) | 41.28 | 52.85 | 53.37 | 52.24 | 50.58 | 49.95 |
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| [Baichuan-7B](https://github.com/baichuan-inc/baichuan-7B) | 32.79 | 44.43 | 46.78 | 44.79 | 43.11 | 42.33 |
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| [ChatGLM-6B](https://github.com/THUDM/GLM-130B) | 32.22 | 42.91 | 44.81 | 42.60 | 41.93 | 40.79 |
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| [BatGPT-15B](https://arxiv.org/abs/2307.00360) | 33.72 | 36.53 | 38.07 | 46.94 | 38.32 | 38.51 |
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| [Chinese-LLaMA-13B](https://github.com/ymcui/Chinese-LLaMA-Alpaca) | 26.76 | 26.57 | 27.42 | 28.33 | 26.73 | 27.34 |
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| [MOSS-SFT-16B](https://github.com/OpenLMLab/MOSS) | 25.68 | 26.35 | 27.21 | 27.92 | 26.70 | 26.88 |
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| [Chinese-GLM-10B](https://github.com/THUDM/GLM) | 25.57 | 25.01 | 26.33 | 25.94 | 25.81 | 25.80 |
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| [Baichuan-13B](https://github.com/baichuan-inc/Baichuan-13B) | 42.04 | 60.49 | 59.55 | 56.60 | 55.72 | 54.63 |
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| [Baichuan-13B-Chat](https://github.com/baichuan-inc/Baichuan-13B) | 37.32 | 56.24 | 54.79 | 54.07 | 52.23 | 50.48 |
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| **Baichuan-13B-Instruction** | **42.56** | **62.09** | **60.41** | **58.97** | **56.95** | **55.88** |
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> 说明：CMMLU 是一个综合性的中文评估基准，专门用于评估语言模型在中文语境下的知识和推理能力。我们直接使用其官方的[评测脚本](https://github.com/haonan-li/CMMLU)对模型进行评测。Model zero-shot 表格中 [Baichuan-13B-Chat](https://github.com/baichuan-inc/Baichuan-13B) 的得分来自我们直接运行 CMMLU 官方的评测脚本得到，其他模型的的得分来自于 [CMMLU](https://github.com/haonan-li/CMMLU/tree/master) 官方的评测结果，Model 5-shot 中其他模型的得分来自于[Baichuan-13B](https://github.com/baichuan-inc/Baichuan-13B) 官方的评测结果。
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