Huihui-Step3-VL-10B-abliterated

huihui-ai/Huihui-Step3-VL-10B-abliterated

This is an uncensored version of stepfun-ai/Step3-VL-10B created with abliteration (see remove-refusals-with-transformers to know more about it).

It was only the text part that was processed, not the image part.

The abliterated model will no longer say "I can’t describe or analyze this image."

The model we saved uses the original mapping relationship(key_mapping) after conversion, so the file model.safetensors.index.json you see will be different. Of course, there is no need to remap it again.

Process Image

import torch
from tqdm import tqdm

from transformers import AutoModelForCausalLM, AutoProcessor, TextStreamer
import os
import sys

NEW_MODEL_ID = "huihui-ai/Huihui-Step3-VL-10B-abliterated"
sys.path.append(NEW_MODEL_ID)

processor = AutoProcessor.from_pretrained(NEW_MODEL_ID, trust_remote_code=True)

model = AutoModelForCausalLM.from_pretrained(
    NEW_MODEL_ID,
    trust_remote_code=True,
    device_map="auto",
    torch_dtype="auto",
).eval()

image_folder_path = "png"
image_files = [f for f in os.listdir(image_folder_path) if f.endswith(".png") or f.endswith(".jpg")]

for filename in tqdm(image_files, desc="Processing Images"):
    image_path = os.path.join(image_folder_path, filename)

    print(f"\nimage_path: {image_path}")
    messages = [
        {
            "role": "user",
            "content": [
                {"type": "image", "image": f"{image_path}"},
                {"type": "text", "text": "Describe this image."}
            ],
        },
    ]

    print("Response:")

    inputs = processor.apply_chat_template(
        messages,
        tokenize=True,
        add_generation_prompt=True,
        return_dict=True,
        return_tensors="pt"
    ).to(model.device)


    generate_ids = model.generate(
        **inputs,
        max_new_tokens=10240,
        do_sample=False,
        pad_token_id=processor.tokenizer.pad_token_id,
        eos_token_id=processor.tokenizer.eos_token_id,
    )
    output_text = processor.decode(generate_ids[0, inputs["input_ids"].shape[-1] :], skip_special_tokens=True)

    print(output_text)

    txt_filename = os.path.splitext(filename)[0] + ".txt"
    txt_filepath = os.path.join(image_folder_path, txt_filename)
    with open(txt_filepath, "w", encoding="utf-8") as txt_file:
        txt_file.write(output_text[0])

Chat

from transformers import AutoModelForCausalLM, AutoProcessor, TextStreamer
import torch
import os
import signal
import time
import sys

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 processor
NEW_MODEL_ID = "huihui-ai/Huihui-Step3-VL-10B-abliterated"

sys.path.append(NEW_MODEL_ID)

print(f"Load Model {NEW_MODEL_ID} ... ")

model = AutoModelForCausalLM.from_pretrained(
    NEW_MODEL_ID,
    trust_remote_code=True,
    device_map="auto",
    torch_dtype="auto",
).eval()

processor = AutoProcessor.from_pretrained(NEW_MODEL_ID, trust_remote_code=True)

messages = []
skip_prompt=True
skip_special_tokens=True

class CustomTextStreamer(TextStreamer):
    def __init__(self, processor, skip_prompt=True, skip_special_tokens=True):
        super().__init__(processor, 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.think_tokens_count = 0  # To track total think tokens
        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

        self.token_count += 1
        if self.think_tokens_count == 0 and "</think>" in self.generated_text:
        	  self.think_tokens_count = self.token_count
        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
            "think_tokens_count": self.think_tokens_count,
            "total_tokens": self.token_count,
            "tokens_per_second": tokens_per_second
        }
        return metrics
def generate_stream(model, processor, messages, skip_prompt, skip_special_tokens, max_new_tokens):
    toks = processor.apply_chat_template(
        messages,
        tokenize=True,
        add_generation_prompt=True,
        return_dict=True,
        return_tensors="pt",
    ).to(model.device)

    streamer = CustomTextStreamer(processor, 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)

    print("Response: ", end="", flush=True)
    try:
        generated_ids = model.generate(
            **toks,
            max_new_tokens=max_new_tokens,
            do_sample=False,
            pad_token_id=processor.tokenizer.pad_token_id,
            eos_token_id=processor.tokenizer.eos_token_id,
            streamer=streamer,
        )
        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 = [{"role": "user", "content": [{"type": "text", "text": user_input}]}]

    response, stop_flag, metrics = generate_stream(model, processor, messages, skip_prompt, skip_special_tokens, 65536)
    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.

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