---

library_name: transformers

license: apache-2.0

license_link: https://huggingface.co/huihui-ai/Qwen2.5-7B-Instruct-abliterated-v2/blob/main/LICENSE

language:

• en

pipeline_tag: text-generation

base_model: Qwen/Qwen2.5-7B-Instruct

tags:

• chat
• abliterated
• uncensored

---


QuantFactory/Qwen2.5-7B-Instruct-abliterated-v2-GGUF

This is quantized version of huihui-ai/Qwen2.5-7B-Instruct-abliterated-v2 created using llama.cpp

Original Model Card

huihui-ai/Qwen2.5-7B-Instruct-abliterated-v2

This is an uncensored version of Qwen/Qwen2.5-7B-Instruct created with abliteration (see this article to know more about it).

Special thanks to @FailSpy for the original code and technique. Please follow him if you're interested in abliterated models.

Important Note This version is an improvement over the previous one Qwen2.5-7B-Instruct-abliterated.

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-v2"
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-v2 | Qwen2.5-7B-Instruct-abliterated |

|-------------|---------------------|------------------------------------|---------------------------------|

| IF_Eval | 76.44 | 77.82 | 76.49 |

| MMLU Pro | 43.12 | 42.03 | 41.71 |

| TruthfulQA | 62.46 | 57.81 | 64.92 |

| BBH | 53.92 | 53.01 | 52.77 |

| GPQA | 31.91 | 32.17 | 31.97 |

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