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Model: DUOMO-Lab/TransGPT-v0
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---
title: TransGPT-7b
emoji: 📚
colorFrom: gray
colorTo: red
language:
- zh
tags:
- chatglm
- pytorch
- zh
- Text2Text-Generation
license: "other"
widget:
- text: "我想了解如何申请和更新驾驶证?"
---
# TransGPT
**发布中文TransGPT(7B)模型**
test case:
|input_text|predict|
|:-- |:--- |
|我想了解如何申请和更新驾驶证?|你可以到当地的交通管理部门或者公安局办理相关手续。具体流程可以在官方网站上查询。|
# 文件校验
```
md5sum ./*
```
```
e618653f90f163928316858e95bd54d1 ./config.json
b1eb3650cbc84466fed263a9f0dff5e2 ./generation_config.json
570159d90b39554713e9702b9107928a ./pytorch_model-00001-of-00002.bin
8788671a726d25b192134909fb825e0b ./pytorch_model-00002-of-00002.bin
604e0ba32b2cb7df8d8a3d13bddc93fe ./pytorch_model.bin.index.json
413c7f9a8a6517c52c937eed27f18847 ./special_tokens_map.json
2ba2be903e87d7471bbc413e041e70e8 ./tokenizer_config.json
39afcc4541e7931ef0d561ac6e216586 ./tokenizer.model
```
## Usage
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:"""
checkpoint="DUOMO-Lab/TransGPT-v0"
tokenizer = LlamaTokenizer.from_pretrained(checkpoint)
model = LlamaForCausalLM.from_pretrained(checkpoint).half().cuda()
model.eval()
text = '我想了解如何申请和更新驾驶证?'
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=1024,
temperature=1,
top_k=20,
top_p=0.9,
repetition_penalty=1.15
).cuda()
output = tokenizer.decode(output_ids[0], skip_special_tokens=True)
print(output.replace(text, '').strip())
```
output:
```shell
我想了解如何申请和更新驾驶证?
```
## 模型来源
release合并后的模型权重。
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
PyTorch版本权重(.pth文件)可用于:
- 使用llama.cpp工具进行量化和部署
模型文件组成:
```
TransGPT
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
```
硬件要求:14G显存
### 微调数据集
1. ~34.6万条文本数据集(用于领域内预训练):[DUOMO-Lab/TransGPT-pt](https://huggingface.co/datasets/DUOMO-Lab/TransGPT-pt)
2. ~5.6万条对话数据(用于微调):[finetune_data](https://huggingface.co/data/finetune)
如果需要训练LLaMA模型,请参考[https://github.com/DUOMO/TransGPT](https://github.com/DUOMO/TransGPT)
## Citation
```latex
@software{TransGPT,
author = {Wang Peng},
title = {DUOMO/TransGPT},
year = {2023},
url = {https://github.com/DUOMO/TransGPT},
}
```
## Reference
- https://github.com/shibing624/textgen

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{
"_name_or_path": "shibing624/chinese-alpaca-plus-7b-hf",
"architectures": [
"LlamaForCausalLM"
],
"bos_token_id": 1,
"eos_token_id": 2,
"hidden_act": "silu",
"hidden_size": 4096,
"initializer_range": 0.02,
"intermediate_size": 11008,
"max_position_embeddings": 2048,
"model_type": "llama",
"num_attention_heads": 32,
"num_hidden_layers": 32,
"pad_token_id": 0,
"rms_norm_eps": 1e-06,
"tie_word_embeddings": false,
"torch_dtype": "float16",
"transformers_version": "4.30.2",
"use_cache": true,
"vocab_size": 49954
}

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{
"_from_model_config": true,
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