Create README.md

README.md

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+---
+base_model:
+- ibm-granite/granite-4.0-micro
+license: apache-2.0
+library_name: transformers
+tags:
+- language
+- granite-4.0
+- abliterated
+- uncensored
+---
+
+# huihui-ai/Huihui-granite-4.0-micro-abliterated
+
+
+This is an uncensored version of [ibm-granite/granite-4.0-micro](https://huggingface.co/ibm-granite/granite-4.0-micro) created with abliteration (see [remove-refusals-with-transformers](https://github.com/Sumandora/remove-refusals-with-transformers) to know more about it).  
+
+
+## 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-granite-4.0-micro-abliterated"
+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,
+)
+
+NUM_TRANS_LAYERS = 94
+
+def create_device_map():
+    device_map = {
+        'model.embed_tokens': 0,
+        'model.norm': 0,
+        'lm_head': 0
+    }
+    for start, end, gpu_id in [(0, 4, 0), (4, 22, 1), (22, 40, 2), (40, 58, 3), (58, 76, 4), (76, 94, 5)]:
+        for i in range(start, end):
+            device_map[f'model.layers.{i}'] = gpu_id
+    
+    #for i in range(76, NUM_TRANS_LAYERS):
+    #    device_map[f'model.layers.{i}'] = "cpu"
+
+    return device_map
+
+#device_map = create_device_map()
+
+model = AutoModelForCausalLM.from_pretrained(
+    NEW_MODEL_ID, 
+    device_map="balanced", 
+    trust_remote_code=True,
+    #quantization_config=quant_config_4,
+    torch_dtype=torch.bfloat16,
+    low_cpu_mem_usage=True,
+)
+#print(model)
+#print(model.config)
+
+tokenizer = AutoTokenizer.from_pretrained(NEW_MODEL_ID, trust_remote_code=True)
+
+messages = []
+skip_prompt=True
+skip_special_tokens=True
+
+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, skip_prompt, skip_special_tokens, max_new_tokens):
+    formatted_prompt = tokenizer.apply_chat_template(
+        messages,
+        tokenize=False,
+        add_generation_prompt=True,
+    )
+
+    toks = tokenizer(
+        formatted_prompt,
+        return_tensors="pt",
+    ).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 = {}
+          
+    print("Response: ", end="", flush=True)
+    try:
+        generated_ids = model.generate(
+            **toks,
+            max_new_tokens=max_new_tokens,
+            streamer=streamer,
+            #**generate_kwargs
+        )
+        del generated_ids
+    except StopIteration:
+        print("\n[Stopped by user]")
+
+    del toks
+    torch.cuda.empty_cache()
+    signal.signal(signal.SIGINT, signal.SIG_DFL)
+
+    return streamer.generated_text, streamer.stop_flag, streamer.get_metrics()
+
+while True:
+    print(f"skip_prompt: {skip_prompt}")
+    print(f"skip_special_tokens: {skip_special_tokens}")
+    
+    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() == "/skip_prompt":
+        skip_prompt = not skip_prompt
+        continue
+    if user_input.lower() == "/skip_special_tokens":
+        skip_special_tokens = not skip_special_tokens
+        continue
+    if not user_input:
+        print("Input cannot be empty. Please enter something.")
+        continue
+    
+
+    messages.append({"role": "user", "content": user_input})
+
+    response, stop_flag, metrics = generate_stream(model, tokenizer, messages, skip_prompt, skip_special_tokens, 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})
+
+```
+
+## 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
+##### Your donation helps us continue our further development and improvement, a cup of coffee can do it.
+- bitcoin:
+```
+  bc1qqnkhuchxw0zqjh2ku3lu4hq45hc6gy84uk70ge
+```
+- Support our work on Ko-fi (https://ko-fi.com/huihuiai)!