How to Utilize the huihui-ai Qwen2.5-32B-Instruct-Abliterated Model

This guide aims to help you effectively implement the uncensored version of the Qwen2.5-32B-Instruct model created using the novel abliterated technique. With the help of Hugging Face’s Transformers library, you can easily integrate this model into your applications.

Step-by-Step Guide to Using the Model

• Installation: Make sure you have the Transformers library installed. If not, you can install it using pip:

pip install transformers

• Load the Model and Tokenizer: Start by importing the necessary packages and loading the model with the following code:

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "huihui-ai/Qwen2.5-32B-Instruct-abliterated"
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(model_name)

• Initialize Conversation Context: Set the initial message to kickstart the dialogue:

initial_messages = [
    {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."}
]
messages = initial_messages.copy()

• Enter the Conversation Loop: Implement a while loop to continue the conversation until the user decides to exit:

while True:
    user_input = input("User: ").strip()
    if user_input.lower() == "exit":
        print("Exiting chat.")
        break
    if user_input.lower() == "clean":
        messages = initial_messages.copy()
        print("Chat history cleared. Starting a new conversation.")
        continue
    if not user_input:
        print("Input cannot be empty. Please enter something.")
        continue
    messages.append({"role": "user", "content": user_input})

• Generate a Response: Finally, tokenize the user input, generate a response, and print it:

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
generated_ids = model.generate(**model_inputs, max_new_tokens=8192)
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
messages.append({"role": "assistant", "content": response})
print(f"Qwen: {response}")

Understanding the Code with an Analogy

Think of the code we just explained as preparing for a conversation in a busy coffee shop. Each step represents a portion of your preparation:

• Setting the stage: Installing the Transformers library is like choosing the perfect coffee shop with the right ambiance.
• Inviting the assistant: Loading the model and tokenizer is akin to welcoming a highly knowledgeable barista to your table who’s ready to serve.
• Starting the dialogue: Initializing the conversation context is like introducing yourself and explaining to your barista what kind of drinks you love.
• Back-and-forth banter: The conversation loop is similar to enjoying a friendly chat, where you order new drinks (user inputs) and the barista responds with their wisdom and recommendations (model responses).

Troubleshooting Tips

• If you encounter an error when loading the model, ensure your Transformers library is up to date. Run

  pip install --upgrade transformers

  to update.
• If the model’s responses seem off, double-check your conversation context and ensure your messages are formatted correctly.
• Sometimes the tokenization might trigger issues; make sure your input is adequately prepared and not empty.
• If you need more help or to collaborate on innovations, visit **[fxis.ai](https://fxis.ai)** for more insights, updates, or to collaborate on AI development projects.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.