Triangle104/Qwen2.5-1.5B-Instruct-abliterated-Q4_K_M-GGUF

This model was converted to GGUF format from huihui-ai/Qwen2.5-1.5B-Instruct-abliterated using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

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Model details:

This is an uncensored version of Qwen2.5-1.5B-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. 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/Qwen2.5-1.5B-Instruct-abliterated" 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}")

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Use with llama.cpp

Install llama.cpp through brew (works on Mac and Linux)

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo Triangle104/Qwen2.5-1.5B-Instruct-abliterated-Q4_K_M-GGUF --hf-file qwen2.5-1.5b-instruct-abliterated-q4_k_m.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Triangle104/Qwen2.5-1.5B-Instruct-abliterated-Q4_K_M-GGUF --hf-file qwen2.5-1.5b-instruct-abliterated-q4_k_m.gguf -c 2048

Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.

git clone https://github.com/ggerganov/llama.cpp

Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).

cd llama.cpp && LLAMA_CURL=1 make

Step 3: Run inference through the main binary.

./llama-cli --hf-repo Triangle104/Qwen2.5-1.5B-Instruct-abliterated-Q4_K_M-GGUF --hf-file qwen2.5-1.5b-instruct-abliterated-q4_k_m.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo Triangle104/Qwen2.5-1.5B-Instruct-abliterated-Q4_K_M-GGUF --hf-file qwen2.5-1.5b-instruct-abliterated-q4_k_m.gguf -c 2048