Triangle104/Qwen2.5-7B-Instruct-abliterated-v2-Q4_K_M-GGUF
This model was converted to GGUF format from huihui-ai/Qwen2.5-7B-Instruct-abliterated-v2
using llama.cpp via the ggml.ai's GGUF-my-repo space.
Refer to the original model card for more details on the model.
Model details:
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
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/Qwen2.5-7B-Instruct-abliterated-v2-Q4_K_M-GGUF --hf-file qwen2.5-7b-instruct-abliterated-v2-q4_k_m.gguf -p "The meaning to life and the universe is"
Server:
llama-server --hf-repo Triangle104/Qwen2.5-7B-Instruct-abliterated-v2-Q4_K_M-GGUF --hf-file qwen2.5-7b-instruct-abliterated-v2-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/Qwen2.5-7B-Instruct-abliterated-v2-Q4_K_M-GGUF --hf-file qwen2.5-7b-instruct-abliterated-v2-q4_k_m.gguf -p "The meaning to life and the universe is"
or
./llama-server --hf-repo Triangle104/Qwen2.5-7B-Instruct-abliterated-v2-Q4_K_M-GGUF --hf-file qwen2.5-7b-instruct-abliterated-v2-q4_k_m.gguf -c 2048
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