library_name, license, license_link, language, pipeline_tag, base_model, tags

library_name	license	license_link	language	pipeline_tag	base_model	tags
transformers	apache-2.0	https://huggingface.co/huihui-ai/Qwen2.5-7B-Instruct-abliterated-v3/blob/main/LICENSE	zho eng fra spa por deu ita rus jpn kor vie tha ara	text-generation	Qwen/Qwen2.5-7B-Instruct	chat abliterated uncensored

huihui-ai/Qwen2.5-7B-Instruct-abliterated-v3

This is an uncensored version of Qwen/Qwen2.5-7B-Instruct created with abliteration (see remove-refusals-with-transformers to know more about it). This is a crude, proof-of-concept implementation to remove refusals from an LLM model without using TransformerLens. The test results are not very good, but compared to before, there is much less garbled text.

ollama

You can use huihui_ai/qwen2.5-abliterate directly,

ollama run huihui_ai/qwen2.5-abliterate

Usage

You can use this model in your applications by loading it with Hugging Face's transformers library:

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "huihui-ai/Qwen2.5-7B-Instruct-abliterated-v3"
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Initialize conversation context
initial_messages = [
    {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."}
]
messages = initial_messages.copy()  # Copy the initial conversation context

# Enter conversation loop
while True:
    # Get user input
    user_input = input("User: ").strip()  # Strip leading and trailing spaces

    # If the user types '/exit', end the conversation
    if user_input.lower() == "/exit":
        print("Exiting chat.")
        break

    # If the user types '/clean', reset the conversation context
    if user_input.lower() == "/clean":
        messages = initial_messages.copy()  # Reset conversation context
        print("Chat history cleared. Starting a new conversation.")
        continue

    # If input is empty, prompt the user and continue
    if not user_input:
        print("Input cannot be empty. Please enter something.")
        continue

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

    # Build the chat template
    text = tokenizer.apply_chat_template(
        messages,
        tokenize=False,
        add_generation_prompt=True
    )

    # Tokenize input and prepare it for the model
    model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

    # Generate a response from the model
    generated_ids = model.generate(
        **model_inputs,
        max_new_tokens=8192
    )

    # Extract model output, removing special tokens
    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]

    # Add the model's response to the conversation
    messages.append({"role": "assistant", "content": response})

    # Print the model's response
    print(f"Qwen: {response}")

Evaluations

The following data has been re-evaluated and calculated as the average for each test.

Benchmark	Qwen2.5-7B-Instruct	Qwen2.5-7B-Instruct-abliterated-v3	Qwen2.5-7B-Instruct-abliterated-v2	Qwen2.5-7B-Instruct-abliterated
IF_Eval	76.44	72.64	77.82	76.49
MMLU Pro	43.12	39.14	42.03	41.71
TruthfulQA	62.46	57.27	57.81	64.92
BBH	53.92	50.67	53.01	52.77
GPQA	31.91	31.65	32.17	31.97

The script used for evaluation can be found inside this repository under /eval.sh, or click here