Update README.md

README.md

@@ -18,6 +18,98 @@ tags:
 This model was converted to GGUF format from [`huihui-ai/Qwen2.5-7B-Instruct-abliterated-v2`](https://huggingface.co/huihui-ai/Qwen2.5-7B-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/Qwen2.5-7B-Instruct-abliterated-v2) 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)