Evaluation [Self Reported]

Note: This model was tested on 250 randomly sampled harmful prompts based on the harm_bench dataset. The result shows 0 refusals out of 250. For more details, refer to the dataset page linked above.

Key Highlights

• Heretic Stable Training: Refined to reduce internal refusal behaviors while improving response stability and coherent long-form multimodal generation.
• 8B Multimodal Architecture: Based on Qwen3-VL-8B-Instruct, delivering strong vision-language understanding and detailed reasoning capabilities.
• Enhanced Visual Reasoning: Optimized for deep analysis of artistic, technical, forensic, abstract, and research-oriented visual content.
• High-Fidelity Captioning: Generates rich and descriptive captions suitable for metadata generation, accessibility pipelines, and dataset enrichment.
• Dynamic Resolution Handling: Maintains native Qwen3-VL support for multiple aspect ratios and high-resolution image processing.
• Stable Instruction Following: Tuned to preserve conversational coherence and reduce generation instability during extended reasoning tasks.

Quick Start with Transformers

bash
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pip install transformers==5.9.0
# or
pip install git+https://github.com/huggingface/transformers.git

python
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from transformers import Qwen3VLForConditionalGeneration, AutoProcessor
from qwen_vl_utils import process_vision_info
import torch

# Load the Heretic Stable model
model = Qwen3VLForConditionalGeneration.from_pretrained(
    "prithivMLmods/Qwen3-VL-8B-Heretic-Stable",
    torch_dtype="auto",
    device_map="auto"
)

processor = AutoProcessor.from_pretrained(
    "prithivMLmods/Qwen3-VL-8B-Heretic-Stable"
)

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "image",
                "image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg",
            },
            {
                "type": "text",
                "text": "Provide a detailed caption and reasoning for this image."
            },
        ],
    }
]

text = processor.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)

image_inputs, video_inputs = process_vision_info(messages)

inputs = processor(
    text=[text],
    images=image_inputs,
    videos=video_inputs,
    padding=True,
    return_tensors="pt",
).to("cuda")

generated_ids = model.generate(
    **inputs,
    max_new_tokens=256
)

generated_ids_trimmed = [
    out_ids[len(in_ids):]
    for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]

output_text = processor.batch_decode(
    generated_ids_trimmed,
    skip_special_tokens=True,
    clean_up_tokenization_spaces=False
)

print(output_text)

Intended Use

• Advanced Multimodal Research: Exploring reasoning behavior and multimodal robustness across diverse prompts.
• Visual Dataset Enrichment: Producing detailed captions for historical, artistic, scientific, or technical datasets.
• Behavioral Alignment Research: Studying the effects of refusal-reduction and abliteration-based fine-tuning strategies.
• Creative Vision-Language Applications: Supporting storytelling, world-building, visual narration, and scene interpretation workflows.

Limitations & Risks

Important Notice: This model intentionally minimizes conventional refusal mechanisms.

• Sensitive Output Generation: The model may produce explicit, controversial, or unrestricted outputs depending on prompts.
• User Responsibility: Outputs should be used responsibly and in accordance with applicable legal and ethical standards.
• Large Hardware Requirements: High-resolution multimodal inference may require substantial GPU memory and compute resources.

Model Lineage

• Base Model: Qwen/Qwen3-VL-8B-Instruct
• Intermediate Variant: prithivMLmods/Qwen3-VL-8B-Instruct-Unredacted-MAX
• Current Release: prithivMLmods/Qwen3-VL-8B-Heretic-Stable

Acknowledgements

I would like to thank the works of the following:

• Maxime Labonne — Uncensor any LLM with abliteration
• NVIDIA Transformer Engine Docs — Using FP8 and FP4 with Transformer Engine
• Remove Refusals with Transformers by Sumandora
• LLM Compressor by vLLM Project
• NVIDIA FP8 Introduction