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README.zh.md

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+### README (中文版)
+
+# Qwen2-Boundless
+
+## 简介
+
+Qwen2-Boundless 是一个基于 Qwen2-1.5B-Instruct 微调的模型，专为回答各种类型的问题而设计，无论是道德的、违法的、色情的、暴力的内容，均可自由询问。该模型经过特殊的数据集训练，能够应对复杂和多样的场景。需要注意的是，微调数据集全部为中文，因此模型在处理中文时表现更佳。
+
+> **警告**：本模型仅用于研究和测试目的，用户应遵循当地法律法规，并对自己的行为负责。
+
+## 模型使用
+
+你可以通过以下代码加载并使用该模型：
+
+```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)
+```
+
+### 连续对话
+
+要实现连续对话，可以使用以下代码：
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+import os
+
+device = "cuda"  # the device to load the model onto
+
+# 获取当前脚本所在的目录
+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": "You are a helpful assistant."}
+]
+
+while True:
+    # 获取用户输入
+    user_input = input("User: ")
+
+    # 将用户输入添加到对话中
+    messages.append({"role": "user", "content": user_input})
+
+    # 准备输入文本
+    text = tokenizer.apply_chat_template(
+        messages,
+        tokenize=False,
+        add_generation_prompt=True
+    )
+    model_inputs = tokenizer([text], return_tensors="pt").to(device)
+
+    # 生成响应
+    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)
+    ]
+
+    # 解码并打印响应
+    response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+    print(f"Assistant: {response}")
+
+    # 将生成的响应添加到对话中
+    messages.append({"role": "assistant", "content": response})
+```
+
+### 流式响应
+
+对于需要流式响应的应用，使用以下代码：
+
+```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:
+    while True:
+        try:
+            message = input('User: ').strip()
+        except UnicodeDecodeError:
+            print('[ERROR] Encoding error in input')
+            continue
+        except KeyboardInterrupt:
+            exit(1)
+        if message:
+            return message
+        print('[ERROR] Query is empty')
+
+def _chat_stream(model, tokenizer, query, history):
+    conversation = [
+        {'role': 'system', 'content': ''},
+    ]
+    for query_h, response_h in history:
+        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(
+        conversation,
+        add_generation_prompt=True,
+        return_tensors='pt',
+    )
+    inputs = inputs.to(model.device)
+    streamer = TextIteratorStreamer(tokenizer=tokenizer, skip_prompt=True, timeout=60.0, skip_special_tokens=True)
+    generation_kwargs = dict(
+        input_ids=inputs,
+        streamer=streamer,
+    )
+    thread = Thread(target=model.generate, kwargs=generation_kwargs)
+    thread.start()
+
+    for new_text in streamer:
+        yield new_text
+
+def main():
+    checkpoint_path = DEFAULT_CKPT_PATH
+    seed = random.randint(0, 2**32 - 1)  # 随机生成一个种子
+    set_seed(seed)  # 设置随机种子
+    cpu_only = False
+
+    history = []
+
+    model, tokenizer = _load_model_tokenizer(checkpoint_path, cpu_only)
+
+    while True:
+        query = _get_input()
+
+        print(f"\nUser: {query}")
+        print(f"\nAssistant: ", end="")
+        try:
+            partial_text = ''
+            for new_text in _chat_stream(model, tokenizer, query, history):
+                print(new_text, end='', flush=True)
+                partial_text += new_text
+            print()
+            history.append((query, partial_text))
+
+        except KeyboardInterrupt:
+            print('Generation interrupted')
+            continue
+
+if __name__ == "__main__":
+    main()
+```
+
+## 数据集
+
+Qwen2-Boundless 模型使用了特殊的 `bad_data.json` 数据集进行微调，该数据集包含了广泛的文本内容，涵盖道德、法律、色情及暴力等主题。由于微调数据集全部为中文，因此模型在处理中文时表现更佳。如果你有兴趣了解或使用该数据集，可以通过以下链接获取：
+
+- [bad_data.json 数据集](https://huggingface.co/datasets/ystemsrx/bad_data.json)
+
+## GitHub 仓库
+
+更多关于该模型的细节以及持续更新，请访问我们的 GitHub 仓库：
+
+- [GitHub: ystemsrx/Qwen2-Boundless](https://github.com/ystemsrx/Qwen2-Boundless)
+
+## 许可证
+
+该模型和数据集根据 Apache 2.0 许可证开源，详细信息请参考 [LICENSE](https://github.com/ystemsrx/Qwen2-Boundless/blob/main/LICENSE) 文件。
+
+## 声明
+
+本模型提供的所有内容仅用于研究和测试目的，模型开发者不对任何可能的滥用行为负责。使用者应遵循相关法律法规，并承担因使用本模型而产生的所有责任。