license: apache-2.0
license_link: https://huggingface.co/huihui-ai/Qwen2.5-0.5B-Instruct-abliterated-v3/blob/main/LICENSE
language:

• zho
• eng
• fra
• spa
• por
• deu
• ita
• rus
• jpn
• kor
• vie
• tha
• ara
  pipeline_tag: text-generation
  base_model:
• huihui-ai/Qwen2.5-0.5B-Instruct-abliterated-v3
  tags:
• chat
• abliterated
• uncensored
  library_name: transformers.js

Qwen2.5-0.5B-Instruct-abliterated-v3 (ONNX)

This is an ONNX version of huihui-ai/Qwen2.5-0.5B-Instruct-abliterated-v3. It was automatically converted and uploaded using this Hugging Face Space.

Usage with Transformers.js

See the pipeline documentation for text-generation: https://huggingface.co/docs/transformers.js/api/pipelines#module_pipelines.TextGenerationPipeline

huihui-ai/Qwen2.5-0.5B-Instruct-abliterated-v3

This is an uncensored version of Qwen/Qwen2.5-0.5B-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.

Ablation was performed using a new and faster method, which yields better results.

This ablation version used a more precise dataset.
The pass rate for the 320 harmful instructions test is 100%.

ollama

huihui_ai/qwen2.5-abliterate:0.5b-v3 is less than 400MB in size and performs very well.

You can use huihui_ai/qwen2.5-abliterate:0.5b-v3 directly,

ollama run huihui_ai/qwen2.5-abliterate:0.5b-v3

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/Qwen2.5-0.5B-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}")

Pass Rate Description

The pass rate is defined as the proportion of harmful instructions that did not trigger the test condition (TestPassed=False) out of the total number of instructions processed. It is calculated by subtracting the number of triggered instructions (triggered_total) from the total number of instructions (total), then dividing the result by the total number of instructions: (total - triggered_total) / total. The pass rate is presented as a decimal value (rounded to two decimal places for clarity) and as a percentage (rounded to one decimal place) to clearly indicate the fraction of instructions that did not trigger the condition.

The test set data comes from huihui-ai/harmbench_behaviors, the test code, TestPassed.py.

The test result is 100.00%.

python TestPassed.py
Load Model huihui-ai/Qwen2.5-0.5B-Instruct-abliterated-v3 ...
Processing harmful instructions: 100%|███████████████████████████████████████████████████████████████████████████████████| 320/320 [01:04<00:00,  4.99it/s]
Passed total: 320/320, Passed ratio: 1.00 (100.00%)

Below is the comparison of pass rates.

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