README.md

---
license: apache-2.0
datasets:
- ystemsrx/Bad_Data_Alpaca
language:
- zh
library_name: transformers
pipeline_tag: text2text-generation
tags:
- Qwen
- causal-lm
- fine-tuned
- ethics
- Chinese
---

[中文](README.zh.md)

# Qwen2-Boundless

## Overview

Qwen2-Boundless is a fine-tuned model based on Qwen2-1.5B-Instruct, designed to answer various types of questions, including those involving ethical, illegal, pornographic, and violent content. This model has been specifically trained on a dataset that allows it to handle complex and diverse scenarios. It is important to note that the fine-tuning dataset is entirely in Chinese, so the model performs better in Chinese.

> **Warning**: This model is intended for research and testing purposes only. Users should comply with local laws and regulations and are responsible for their actions.

## How to Use

You can load and use the model with the following code:

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import os

device = "cuda" # the device to load the model onto
current_directory = os.path.dirname(os.path.abspath(__file__))

model = AutoModelForCausalLM.from_pretrained(
    current_directory,
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(current_directory)

prompt = "Hello?"
messages = [
    {"role": "system", "content": ""},
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)

generated_ids = model.generate(
    model_inputs.input_ids,
    max_new_tokens=512
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)
```

### Continuous Conversation

To enable continuous conversation, use the following code:

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
import os

device = "cuda"  # the device to load the model onto

# Get the current script's directory
current_directory = os.path.dirname(os.path.abspath(__file__))

model = AutoModelForCausalLM.from_pretrained(
    current_directory,
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(current_directory)

messages = [
    {"role": "system", "content": ""}
]

while True:
    # Get user input
    user_input = input("User: ")

    # Add user input to the conversation
    messages.append({"role": "user", "content": user_input})

    # Prepare the input text
    text = tokenizer.apply_chat_template(
        messages,
        tokenize=False,
        add_generation_prompt=True
    )
    model_inputs = tokenizer([text], return_tensors="pt").to(device)

    # Generate a response
    generated_ids = model.generate(
        model_inputs.input_ids,
        max_new_tokens=512
    )
    generated_ids = [
        output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
    ]

    # Decode and print the response
    response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
    print(f"Assistant: {response}")

    # Add the generated response to the conversation
    messages.append({"role": "assistant", "content": response})
```

### Streaming Response

For applications requiring streaming responses, use the following code:

```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
from transformers.trainer_utils import set_seed
from threading import Thread
import random
import os

DEFAULT_CKPT_PATH = os.path.dirname(os.path.abspath(__file__))

def _load_model_tokenizer(checkpoint_path, cpu_only):
    tokenizer = AutoTokenizer.from_pretrained(checkpoint_path, resume_download=True)

    device_map = "cpu" if cpu_only else "auto"

    model = AutoModelForCausalLM.from_pretrained(
        checkpoint_path,
        torch_dtype="auto",
        device_map=device_map,
        resume_download=True,
    ).eval()
    model.generation_config.max_new_tokens = 512    # For chat.

    return model, tokenizer

Def_get_input()->str：
当为True时：
尝试：
消息=输入('用户：').strip()
UnicodeDecodeError除外：
打印('[ERROR]输入中的编码错误')
继续
键盘中断除外：
出口(1)
如果消息：
返回消息
打印('[ERROR]查询为空')

Def_chat_stream(模型、标记器、查询、历史记录)：
对话=[
{'角色'：'系统'，'内容'："}，
]
对于历史中的query_h、response_h：
conversation.append({'role'：'user'，'content'：query_h})
conversation.append({'role'：'assistant'，'content'：response_h})
conversation.append({'role'：'user'，'content'：query})
inputs=tokenizer.apply_chat_template(
对话，
add_generation_prompt=True，
return_tensors='pt'，
)
inputs=inputs.to(model.device)
streamer=TextIteratorStreamer(tokenizer=tokenizer，skip_prompt=True，timeout=60.0，skip_special_token=True)
generation_kwargs=dict(
input_ids=输入，
拖缆=拖缆，
)
thread=Thread(target=model.generate，kwargs=generation_kwargs)
Thread.start()

对于拖缆中的新文本(_T)：
产生新文本(_T)

Def main()：
checkpoint_path=DEFAULT_ckpt_PATH
seed=random.randint(0，2**32-1)#生成随机种子
set_seed(种子)#设置随机种子
CPU_only=False

历史记录=[]

model，tokenizer=_load_model_tokenizer(检查点路径，仅cpu)

当为True时：
query=_get_input()

打印(f“\n用户：{query}”)
打印(f"\n助手："，end="")
尝试：
partial_text="
对于聊天流中的新文本(模型、标记器、查询、历史记录)：
打印(new_text，end="，flush=True)
partial_text+=new_text
打印()
history.append((查询，部分文本))

键盘中断除外：
打印(“生成中断”)
继续

如果__name__=="__main__"：
主要的()
```

##数据集

Qwen2-Boundless模型使用名为`bad_data.json`，其中包括广泛的文本内容，涉及伦理、法律、色情和暴力等主题。微调数据集完全是中文的，因此模型的中文性能更好。如果您有兴趣浏览或使用此数据集，可以通过以下链接找到它：

- [bad_data.json数据集](https://huggingface.co/datasets/ystemsrx/Bad_Data_Alpaca)

我们还使用了一些与网络安全相关的数据，这些数据是从[此文件](https://github.com/Clouditera/SecGPT/blob/main/secgpt-mini/%E5%A4%A7%E6%A8%A1%E5%9E%8B%E5%9B%9E%E7%AD%94%E9%9D%A2%E9%97%AE%E9%A2%98-cot.txt).

##GitHub存储库

有关模型和正在进行的更新的更多详细信息，请访问我们的GitHub存储库：

- [GitHub:ystemsrx/Qwen2-无界](https://github.com/ystemsrx/Qwen2-Boundless)

##许可证

此模型和数据集在Apache2.0License下是开源的。

##免责声明

本模型提供的所有内容仅供研究和测试之用。此模型的开发人员不对任何潜在的误用负责。用户应遵守相关法律法规，并对其行为负全部责任。