Create README.md

README.md

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+---
+library_name: transformers
+license: apache-2.0
+license_link: https://huggingface.co/Qwen/Qwen3-8B/blob/main/LICENSE
+pipeline_tag: text-generation
+base_model:
+- Qwen/Qwen3-8B
+tags:
+- chat
+- abliterated
+- uncensored
+
+---
+
+# huihui-ai/Huihui-Qwen3-8B-abliterated-v2
+
+
+This is an uncensored version of [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) created with abliteration (see [remove-refusals-with-transformers](https://github.com/Sumandora/remove-refusals-with-transformers) to know more about it).
+This is a crude, proof-of-concept implementation to remove refusals from an LLM model without using TransformerLens. 
+
+Ablation was performed using a new and faster method, which yields better results.
+
+**Important Note** This version is an improvement over the previous one [huihui-ai/Qwen3-8B-abliterated](https://huggingface.co/huihui-ai/Qwen3-8B-abliterated). The ollama version has also been modified.
+
+Changed the candidate layer to eliminate the problem of garbled codes
+
+## ollama
+
+You can use [huihui_ai/qwen3-abliterated:8b-v2](https://ollama.com/huihui_ai/qwen3-abliterated:8b-v2) directly, Switch the thinking toggle using /set think and /set nothink
+```
+ollama run huihui_ai/qwen3-abliterated:8b-v2
+```
+
+
+## Usage
+You can use this model in your applications by loading it with Hugging Face's `transformers` library:
+
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, TextStreamer
+import torch
+import os
+import signal
+import random
+import numpy as np
+import time
+from collections import Counter
+
+cpu_count = os.cpu_count()
+print(f"Number of CPU cores in the system: {cpu_count}")
+half_cpu_count = cpu_count // 2
+os.environ["MKL_NUM_THREADS"] = str(half_cpu_count)
+os.environ["OMP_NUM_THREADS"] = str(half_cpu_count)
+torch.set_num_threads(half_cpu_count)
+
+print(f"PyTorch threads: {torch.get_num_threads()}")
+print(f"MKL threads: {os.getenv('MKL_NUM_THREADS')}")
+print(f"OMP threads: {os.getenv('OMP_NUM_THREADS')}")
+
+# Load the model and tokenizer
+NEW_MODEL_ID = "huihui-ai/Huihui-Qwen3-8B-abliterated-v2"
+print(f"Load Model {NEW_MODEL_ID} ... ")
+quant_config_4 = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_compute_dtype=torch.bfloat16,
+    bnb_4bit_use_double_quant=True,
+    llm_int8_enable_fp32_cpu_offload=True,
+)
+
+model = AutoModelForCausalLM.from_pretrained(
+    NEW_MODEL_ID,
+    device_map="auto",
+    trust_remote_code=True,
+    #quantization_config=quant_config_4,
+    torch_dtype=torch.bfloat16
+)
+tokenizer = AutoTokenizer.from_pretrained(NEW_MODEL_ID, trust_remote_code=True)
+if tokenizer.pad_token is None:
+    tokenizer.pad_token = tokenizer.eos_token
+tokenizer.pad_token_id = tokenizer.eos_token_id
+
+tokenizer = AutoTokenizer.from_pretrained(NEW_MODEL_ID, trust_remote_code=True)
+if tokenizer.pad_token is None:
+    tokenizer.pad_token = tokenizer.eos_token
+tokenizer.pad_token_id = tokenizer.eos_token_id
+
+messages = []
+nothink = False
+same_seed = False
+skip_prompt=True
+skip_special_tokens=True
+do_sample = True
+
+def set_random_seed(seed=None):
+    """Set random seed for reproducibility. If seed is None, use int(time.time())."""
+    if seed is None:
+        seed = int(time.time())  # Convert float to int
+    random.seed(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)  # If using CUDA
+    torch.backends.cudnn.deterministic = True
+    torch.backends.cudnn.benchmark = False
+    return seed  # Return seed for logging if needed
+
+class CustomTextStreamer(TextStreamer):
+    def __init__(self, tokenizer, skip_prompt=True, skip_special_tokens=True):
+        super().__init__(tokenizer, skip_prompt=skip_prompt, skip_special_tokens=skip_special_tokens)
+        self.generated_text = ""
+        self.stop_flag = False
+        self.init_time = time.time()  # Record initialization time
+        self.end_time = None  # To store end time
+        self.first_token_time = None  # To store first token generation time
+        self.token_count = 0  # To track total tokens
+
+    def on_finalized_text(self, text: str, stream_end: bool = False):
+        if self.first_token_time is None and text.strip():  # Set first token time on first non-empty text
+            self.first_token_time = time.time()
+        self.generated_text += text
+        # Count tokens in the generated text
+        tokens = self.tokenizer.encode(text, add_special_tokens=False)
+        self.token_count += len(tokens)
+        print(text, end="", flush=True)
+        if stream_end:
+            self.end_time = time.time()  # Record end time when streaming ends
+        if self.stop_flag:
+            raise StopIteration
+
+    def stop_generation(self):
+        self.stop_flag = True
+        self.end_time = time.time()  # Record end time when generation is stopped
+
+    def get_metrics(self):
+        """Returns initialization time, first token time, first token latency, end time, total time, total tokens, and tokens per second."""
+        if self.end_time is None:
+            self.end_time = time.time()  # Set end time if not already set
+        total_time = self.end_time - self.init_time  # Total time from init to end
+        tokens_per_second = self.token_count / total_time if total_time > 0 else 0
+        first_token_latency = (self.first_token_time - self.init_time) if self.first_token_time is not None else None
+        metrics = {
+            "init_time": self.init_time,
+            "first_token_time": self.first_token_time,
+            "first_token_latency": first_token_latency,
+            "end_time": self.end_time,
+            "total_time": total_time,  # Total time in seconds
+            "total_tokens": self.token_count,
+            "tokens_per_second": tokens_per_second
+        }
+        return metrics
+        
+def generate_stream(model, tokenizer, messages, nothink, skip_prompt, skip_special_tokens, do_sample, max_new_tokens):
+    input_ids = tokenizer.apply_chat_template(
+        messages,
+        tokenize=True,
+        enable_thinking = not nothink,
+        add_generation_prompt=True,
+        return_tensors="pt"
+    )
+    attention_mask = torch.ones_like(input_ids, dtype=torch.long)
+    tokens = input_ids.to(model.device) 
+    attention_mask = attention_mask.to(model.device)
+
+    streamer = CustomTextStreamer(tokenizer, skip_prompt=skip_prompt, skip_special_tokens=skip_special_tokens)
+
+    def signal_handler(sig, frame):
+        streamer.stop_generation()
+        print("\n[Generation stopped by user with Ctrl+C]")
+
+    signal.signal(signal.SIGINT, signal_handler)
+
+    generate_kwargs = {}
+    if do_sample:
+        generate_kwargs = {
+              "do_sample": do_sample,
+              "max_length": max_new_tokens,
+              "temperature": 0.6,
+              "top_k": 20,
+              "top_p": 0.95,
+              "repetition_penalty": 1.2,
+              "no_repeat_ngram_size": 2
+        }
+    else:
+        generate_kwargs = {
+              "do_sample": do_sample,
+              "max_length": max_new_tokens,
+              "repetition_penalty": 1.2,
+              "no_repeat_ngram_size": 2
+        }
+  
+          
+    print("Response: ", end="", flush=True)
+    try:
+        generated_ids = model.generate(
+            tokens,
+            attention_mask=attention_mask,
+            #use_cache=False,
+            pad_token_id=tokenizer.pad_token_id,
+            streamer=streamer,
+            **generate_kwargs
+        )
+        del generated_ids
+    except StopIteration:
+        print("\n[Stopped by user]")
+
+    del input_ids, attention_mask
+    torch.cuda.empty_cache()
+    signal.signal(signal.SIGINT, signal.SIG_DFL)
+
+    return streamer.generated_text, streamer.stop_flag, streamer.get_metrics()
+
+init_seed = set_random_seed()
+  
+while True:
+    if same_seed:
+        set_random_seed(init_seed)
+    else:
+        init_seed = set_random_seed()
+        
+    print(f"\nnothink: {nothink}")
+    print(f"skip_prompt: {skip_prompt}")
+    print(f"skip_special_tokens: {skip_special_tokens}")
+    print(f"do_sample: {do_sample}")
+    print(f"same_seed: {same_seed}, {init_seed}\n")
+    
+    user_input = input("User: ").strip()
+    if user_input.lower() == "/exit":
+        print("Exiting chat.")
+        break
+    if user_input.lower() == "/clear":
+        messages = []
+        print("Chat history cleared. Starting a new conversation.")
+        continue
+    if user_input.lower() == "/nothink":
+        nothink = not nothink
+        continue
+    if user_input.lower() == "/skip_prompt":
+        skip_prompt = not skip_prompt
+        continue
+    if user_input.lower() == "/skip_special_tokens":
+        skip_special_tokens = not skip_special_tokens
+        continue
+    if user_input.lower().startswith("/same_seed"):
+        parts = user_input.split()
+        if len(parts) == 1:  # /same_seed (no number)
+            same_seed = not same_seed  # Toggle switch
+        elif len(parts) == 2:  # /same_seed <number>
+            try:
+                init_seed = int(parts[1])  # Extract and convert number to int
+                same_seed = True
+            except ValueError:
+                print("Error: Please provide a valid integer after /same_seed")       
+        continue
+    if user_input.lower() == "/do_sample":
+        do_sample = not do_sample
+        continue
+    if not user_input:
+        print("Input cannot be empty. Please enter something.")
+        continue
+    
+
+    messages.append({"role": "user", "content": user_input})
+    activated_experts = []
+    response, stop_flag, metrics = generate_stream(model, tokenizer, messages, nothink, skip_prompt, skip_special_tokens, do_sample, 40960)
+    print("\n\nMetrics:")
+    for key, value in metrics.items():
+        print(f"  {key}: {value}")
+        
+    print("", flush=True)
+    if stop_flag:
+        continue
+    messages.append({"role": "assistant", "content": response})
+
+# Remove all hooks after inference
+for h in hooks: h.remove()
+```
+
+### Usage Warnings
+
+
+ - **Risk of Sensitive or Controversial Outputs**: This model’s safety filtering has been significantly reduced, potentially generating sensitive, controversial, or inappropriate content. Users should exercise caution and rigorously review generated outputs.
+
+ - **Not Suitable for All Audiences**: Due to limited content filtering, the model’s outputs may be inappropriate for public settings, underage users, or applications requiring high security.
+
+ - **Legal and Ethical Responsibilities**: Users must ensure their usage complies with local laws and ethical standards. Generated content may carry legal or ethical risks, and users are solely responsible for any consequences.
+
+ - **Research and Experimental Use**: It is recommended to use this model for research, testing, or controlled environments, avoiding direct use in production or public-facing commercial applications.
+
+ - **Monitoring and Review Recommendations**: Users are strongly advised to monitor model outputs in real-time and conduct manual reviews when necessary to prevent the dissemination of inappropriate content.
+
+ - **No Default Safety Guarantees**: Unlike standard models, this model has not undergone rigorous safety optimization. huihui.ai bears no responsibility for any consequences arising from its use.
+
+
+### Donation
+
+If you like it, please click 'like' and follow us for more updates.  
+You can follow [x.com/support_huihui](https://x.com/support_huihui) to get the latest model information from huihui.ai.
+
+##### Your donation helps us continue our further development and improvement, a cup of coffee can do it.
+- bitcoin（BTC):
+```
+  bc1qqnkhuchxw0zqjh2ku3lu4hq45hc6gy84uk70ge
+```