Gliese-Qwen3.5-9B-Abliterated-Caption

Base Model Signatures:

This model has been re-sharded and optimized for the latest Transformers version from the base model: https://huggingface.co/huihui-ai/Huihui-Qwen3.5-9B-abliterated.

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Download the model

bash
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hf auth login --token <YOUR_HF_TOKEN>

hf download prithivMLmods/Gliese-Qwen3.5-9B-Abliterated-Caption

Key Highlights

• Advanced Refusal Direction Analysis Uses targeted activation analysis to identify and mitigate refusal directions within the model’s latent space.

• Abliterated Caption Training Fine-tuned for unfiltered and detailed caption generation, enabling comprehensive visual descriptions without excessive refusal behaviors.

• Optimized Visual Understanding Enhanced to provide rich, context-aware descriptions of scenes, objects, people, and environments.

• 9B Parameter Architecture Built on Qwen3.5-9B, delivering strong multimodal reasoning and improved caption quality while remaining deployable on modern GPUs.

• High-Fidelity Caption Generation Designed to produce long-form, structured, and semantically detailed captions suitable for dataset generation, annotation, and research.

• Efficient Deployment Suitable for caption dataset creation, multimodal research, local inference pipelines, and AI development workflows.

Quick Start with Transformers

bash
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pip install transformers==5.3.0
# or
pip install git+https://github.com/huggingface/transformers.git

python
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from transformers import Qwen3_5ForConditionalGeneration, AutoProcessor
import torch

model = Qwen3_5ForConditionalGeneration.from_pretrained(
    "prithivMLmods/Gliese-Qwen3.5-9B-Abliterated-Caption",
    torch_dtype="auto",
    device_map="auto"
)

processor = AutoProcessor.from_pretrained(
    "prithivMLmods/Gliese-Qwen3.5-9B-Abliterated-Caption"
)

messages = [
    {
        "role": "user",
        "content": [
            {"type": "text", "text": "Describe this image in extreme detail."}
        ],
    }
]

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

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

generated_ids = model.generate(**inputs, max_new_tokens=512)

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

• High-Detail Image Captioning – Generating extremely descriptive captions for images.
• Dataset Generation – Creating large-scale caption datasets for multimodal training.
• Vision-Language Research – Studying multimodal reasoning and captioning behavior.
• Annotation Automation – Assisting in automatic labeling and visual description tasks.
• Local Multimodal AI Deployment – Running powerful captioning models on local GPUs.

Limitations & Risks

Important Note: This model intentionally reduces built-in refusal mechanisms.

• Unfiltered Outputs – The model may generate explicit or controversial captions depending on the input images.
• User Responsibility – Generated outputs should be handled responsibly and within legal and ethical boundaries.
• Model Size Constraints – While strong, a 9B model still has limitations compared to frontier-scale multimodal architectures.