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README.md

@@ -18,88 +18,6 @@ tags:
 This model was converted to GGUF format from [`huihui-ai/Qwen2.5-14B-Instruct-abliterated-v2`](https://huggingface.co/huihui-ai/Qwen2.5-14B-Instruct-abliterated-v2) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
 Refer to the [original model card](https://huggingface.co/huihui-ai/Qwen2.5-14B-Instruct-abliterated-v2) for more details on the model.
 
----
-Model details:
--
-This is an uncensored version of Qwen2.5-14B-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 Qwen2.5-14B-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/Qwen2.5-14B-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}")
-
----
 ## Use with llama.cpp
 Install llama.cpp through brew (works on Mac and Linux)