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README.md

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+
+---
+
+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
+
+---
+
+![](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)
+
+# QuantFactory/Qwen2-Boundless-GGUF
+This is quantized version of [ystemsrx/Qwen2-Boundless](https://huggingface.co/ystemsrx/Qwen2-Boundless) created using llama.cpp
+
+# Original Model Card
+
+
+[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:
+    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)  # Generate a random seed
+    set_seed(seed)  # Set the random seed
+    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()
+```
+
+## Dataset
+
+The Qwen2-Boundless model was fine-tuned using a specific dataset named `bad_data.json`, which includes a wide range of text content covering topics related to ethics, law, pornography, and violence. The fine-tuning dataset is entirely in Chinese, so the model performs better in Chinese. If you are interested in exploring or using this dataset, you can find it via the following link:
+
+- [bad_data.json Dataset](https://huggingface.co/datasets/ystemsrx/Bad_Data_Alpaca)
+
+And also we used some cybersecurity-related data that was cleaned and organized from [this file](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 Repository
+
+For more details about the model and ongoing updates, please visit our GitHub repository:
+
+- [GitHub: ystemsrx/Qwen2-Boundless](https://github.com/ystemsrx/Qwen2-Boundless)
+
+## License
+
+This model and dataset are open-sourced under the Apache 2.0 License.
+
+## Disclaimer
+
+All content provided by this model is for research and testing purposes only. The developers of this model are not responsible for any potential misuse. Users should comply with relevant laws and regulations and are solely responsible for their actions.