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LICENSE

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README.md

@@ -1,145 +1,92 @@
 ---
-title: chinese-alpaca-plus-7b
+title: chinese-alpaca-plus-13b-pth
 emoji: 📚
 colorFrom: gray
 colorTo: red
-language: 
+language:
 - zh
 tags:
 - chatglm
 - pytorch
 - zh
 - Text2Text-Generation
-license: "other"
+- LLaMA
+license: other
 widget:
-- text: "为什么天空是蓝色的？"
+- text: 为什么天空是蓝色的？
 ---
 
-# Chinese Alpaca Plus 7B Model
+# Chinese Alpaca Plus 13B Model(PyTorch版本权重.pth文件)
 
-**发布中文LLaMA, Alpaca Plus版（7B）模型**
+**发布中文LLaMA-Plus, Alpaca-Plus 13B版本模型**
 
+发布中文LLaMA-Plus, Alpaca-Plus 13B版本，改进点如下：
 
-推出中文LLaMA, Alpaca Plus版（7B），相比基础版本的改进点如下：
+- 相比基础版进一步扩充了训练数据，其中LLaMA扩充至120G文本，Alpaca扩充至4.3M指令数据，重点增加了科学领域数据，涵盖：物理、化学、生物、医学、地球科学等
+- Alpaca训练时采用了更大的rank，相比基础版具有更低的验证集损失
+- Alpaca评测结果：13B获得74.3分，Plus-7B获得78.2分，Plus-13B获得80.8分，具体评测结果请参考[效果评测](https://github.com/ymcui/Chinese-LLaMA-Alpaca/blob/main/examples)
+- 多轮回复长度相比旧模型提升明显（可适当增大温度系数）
+- 知识问答、写作、翻译等方面效果显著提升
 
-- 进一步扩充了训练数据，其中LLaMA扩充至120G文本（通用领域），Alpaca扩充至4M指令数据（重点增加了STEM相关数据）
-- Alpaca训练时采用了更大的rank，相比原版具有更低的验证集损失
-- 评测结果显示，Alpaca-Plus-7B相比基础版Alpaca-7B效果更优，部分任务接近或超过13B版本
-- 这一轮比拼：7B获得65.3分，13B获得70.9分，Plus-7B效果75.3分，具体评测结果请参考[效果评测](https://github.com/ymcui/Chinese-LLaMA-Alpaca/blob/main/examples/README.md)
-
-本模型是`原生LLaMA-7B`合并`中文LLaMA LoRA`和`中文Alpaca LoRA`后的模型权重，可以直接使用或者继续训练。
+本模型是 [decapoda-research/llama-13b-hf](https://huggingface.co/decapoda-research/llama-13b-hf) 
+底座模型 合并 [ziqingyang/chinese-llama-plus-lora-13b](https://huggingface.co/ziqingyang/chinese-llama-plus-lora-13b) 
+和 [ziqingyang/chinese-alpaca-plus-lora-13b](https://huggingface.co/ziqingyang/chinese-alpaca-plus-lora-13b) 两个LoRA权重，
+并转化为PyTorch版本权重（.pth文件），可以直接使用或者继续训练。
 
+7b-hf权重链接：https://huggingface.co/shibing624/chinese-alpaca-plus-7b-hf
 
 test case:
 
 |input_text|predict|
 |:-- |:--- |
-|为什么天空是蓝色的？|天空是蓝色的，是因为大气层中的气体分子会散射太阳光中的蓝色光，使得我们看到的天空是蓝色的。|
+|为什么天空是蓝色的？|天空是蓝色的是因为大气中的气体分子散射了太阳光中的短波长蓝光，使得我们看到的天空呈现出蓝色。|
 
 
 ## Usage
+llama.cpp进行量化和部署，或者调用执行
 
-本项目开源在textgen项目：[textgen](https://github.com/shibing624/textgen)，可支持llama模型，通过如下命令调用：
-
-Install package:
-```shell
-pip install -U textgen
-```
-
-```python
-from textgen import LlamaModel
-model = LlamaModel("llama", "shibing624/chinese-alpaca-plus-7b-hf")
-r = model.predict(["用一句话描述地球为什么是独一无二的。"])
-print(r) # ['地球是独一无二的，因为它拥有独特的大气层、水循环、生物多样性以及其他自然资源，这些都使它成为一个独特的生命支持系统。']
-```
-
-## Usage (HuggingFace Transformers)
-Without [textgen](https://github.com/shibing624/textgen), you can use the model like this: 
-
-First, you pass your input through the transformer model, then you get the generated sentence.
-
-Install package:
-```
-pip install sentencepiece
-pip install transformers>=4.28.0
-```
-
-```python
-import torch
-import transformers
-from transformers import LlamaTokenizer, LlamaForCausalLM
-
-def generate_prompt(text):
-    return f"""Below is an instruction that describes a task. Write a response that appropriately completes the request.
-
-### Instruction:
-{text}
-
-### Response:"""
-
-
-tokenizer = LlamaTokenizer.from_pretrained('shibing624/chinese-alpaca-plus-7b-hf')
-model = LlamaForCausalLM.from_pretrained('shibing624/chinese-alpaca-plus-7b-hf').half().cuda()
-model.eval()
-
-text = '为什么天空是蓝色的？'
-prompt = generate_prompt(text)
-input_ids = tokenizer.encode(prompt, return_tensors='pt').to('cuda')
-
-
-with torch.no_grad():
-    output_ids = model.generate(
-        input_ids=input_ids,
-        max_new_tokens=128,
-        temperature=1,
-        top_k=40,
-        top_p=0.9,
-        repetition_penalty=1.15
-    ).cuda()
-output = tokenizer.decode(output_ids[0], skip_special_tokens=True)
-print(output.replace(text, '').strip())
-```
-
+## 模型来源
+release合并后的模型权重，一步到位直接使用，省电、减少碳排放。
 
-output:
-```shell
-为什么天空是蓝色的？
-天空是蓝色的，是因为大气层中的气体分子会散射太阳光中的蓝色光，使得我们看到的天空是蓝色的。
-```
 
-## 模型来源
+基于 [多LoRA权重合并（适用于Chinese-Alpaca-Plus ）](https://github.com/ymcui/Chinese-LLaMA-Alpaca/wiki/%E6%89%8B%E5%8A%A8%E6%A8%A1%E5%9E%8B%E5%90%88%E5%B9%B6%E4%B8%8E%E8%BD%AC%E6%8D%A2#%E5%A4%9Alora%E6%9D%83%E9%87%8D%E5%90%88%E5%B9%B6%E9%80%82%E7%94%A8%E4%BA%8Echinese-alpaca-plus)方法手动合并而成，具体是使用 [decapoda-research/llama-13b-hf](https://huggingface.co/decapoda-research/llama-13b-hf) 
+底座模型 合并 [ziqingyang/chinese-llama-plus-lora-13b](https://huggingface.co/ziqingyang/chinese-llama-plus-lora-13b) 和 [ziqingyang/chinese-alpaca-plus-lora-13b](https://huggingface.co/ziqingyang/chinese-alpaca-plus-lora-13b) 两个LoRA权重 得到，并转化为PyTorch版本权重（.pth文件）。
 
-基于 [多LoRA权重合并（适用于Chinese-Alpaca-Plus ）](https://github.com/ymcui/Chinese-LLaMA-Alpaca/wiki/%E6%89%8B%E5%8A%A8%E6%A8%A1%E5%9E%8B%E5%90%88%E5%B9%B6%E4%B8%8E%E8%BD%AC%E6%8D%A2#%E5%A4%9Alora%E6%9D%83%E9%87%8D%E5%90%88%E5%B9%B6%E9%80%82%E7%94%A8%E4%BA%8Echinese-alpaca-plus-)方法手动合并而成，具体是使用 [decapoda-research/llama-7b-hf](https://huggingface.co/decapoda-research/llama-7b-hf) 底座模型 合并 Chinese-LLaMA-Plus-LoRA和Chinese-Alpaca-Plus-LoRA 两个LoRA权重 得到，并转化为HuggingFace版本权重（.bin文件）。
+HuggingFace版本权重（.bin文件）可用于：
+- 使用Transformers进行训练和推理
+- 使用text-generation-webui搭建界面
 
-release合并后的模型权重，一次到位直接使用，省电、减少碳排放。
+PyTorch版本权重（.pth文件）可用于：
+- 使用llama.cpp工具进行量化和部署
 
+HuggingFace版本权重（.bin文件）链接：[shibing624/chinese-alpaca-plus-13b-hf](https://huggingface.co/shibing624/chinese-alpaca-plus-13b-hf)
 
 模型文件组成：
 ```
-chinese-alpaca-plus-7b-hf
-    config.json
-    generation_config.json
-    pytorch_model-00001-of-00002.bin
-    pytorch_model-00002-of-00002.bin
-    pytorch_model.bin.index.json
-    special_tokens_map.json
-    tokenizer.json
-    tokenizer.model
-    tokenizer_config.json
+chinese-alpaca-plus-13b-pth
+|-- consolidated.00.pth
+|-- consolidated.01.pth
+|-- LICENSE
+|-- params.json
+|-- README.md
+|-- special_tokens_map.json
+|-- tokenizer_config.json
+`-- tokenizer.model
 ```
 
-硬件要求：14G显存
+硬件要求：25G显存
 
-### 训练数据集
+### 微调数据集
+我整理部分公开微调数据集：
 
 1. 50万条中文ChatGPT指令Belle数据集：[BelleGroup/train_0.5M_CN](https://huggingface.co/datasets/BelleGroup/train_0.5M_CN)
 2. 100万条中文ChatGPT指令Belle数据集：[BelleGroup/train_1M_CN](https://huggingface.co/datasets/BelleGroup/train_1M_CN)
 3. 5万条英文ChatGPT指令Alpaca数据集：[50k English Stanford Alpaca dataset](https://github.com/tatsu-lab/stanford_alpaca#data-release)
-4. 2万条中文ChatGPT指令Alpaca数据集：[shibing624/alpaca-zh](https://huggingface.co/datasets/shibing624/alpaca-zh)
+4. 5万条中文GPT4指令Alpaca数据集：[shibing624/alpaca-zh](https://huggingface.co/datasets/shibing624/alpaca-zh)
 5. 69万条中文指令Guanaco数据集(Belle50万条+Guanaco19万条)：[Chinese-Vicuna/guanaco_belle_merge_v1.0](https://huggingface.co/datasets/Chinese-Vicuna/guanaco_belle_merge_v1.0)
 
 
-如果需要训练LLAMA模型，请参考[https://github.com/shibing624/textgen](https://github.com/shibing624/textgen)
+如果需要训练LLaMA模型，请参考[https://github.com/shibing624/textgen](https://github.com/shibing624/textgen)
 
 
 ## Citation
@@ -156,3 +103,5 @@ chinese-alpaca-plus-7b-hf
 
 ## Reference
 - https://github.com/ymcui/Chinese-LLaMA-Alpaca
+
+