Instructions to use prithivMLmods/Qwen3-VL-8B-Abliterated-Caption-it-FP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/Qwen3-VL-8B-Abliterated-Caption-it-FP8 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="prithivMLmods/Qwen3-VL-8B-Abliterated-Caption-it-FP8")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("prithivMLmods/Qwen3-VL-8B-Abliterated-Caption-it-FP8")
model = AutoModelForImageTextToText.from_pretrained("prithivMLmods/Qwen3-VL-8B-Abliterated-Caption-it-FP8")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use prithivMLmods/Qwen3-VL-8B-Abliterated-Caption-it-FP8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/Qwen3-VL-8B-Abliterated-Caption-it-FP8"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Qwen3-VL-8B-Abliterated-Caption-it-FP8",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/prithivMLmods/Qwen3-VL-8B-Abliterated-Caption-it-FP8

SGLang

How to use prithivMLmods/Qwen3-VL-8B-Abliterated-Caption-it-FP8 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "prithivMLmods/Qwen3-VL-8B-Abliterated-Caption-it-FP8" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Qwen3-VL-8B-Abliterated-Caption-it-FP8",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "prithivMLmods/Qwen3-VL-8B-Abliterated-Caption-it-FP8" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Qwen3-VL-8B-Abliterated-Caption-it-FP8",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use prithivMLmods/Qwen3-VL-8B-Abliterated-Caption-it-FP8 with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/Qwen3-VL-8B-Abliterated-Caption-it-FP8
```

Qwen3-VL-8B-Abliterated-Caption-it-FP8

Qwen3-VL-8B-Abliterated-Caption-it-FP8 is an FP8-compressed variant built on top of prithivMLmods/Qwen3-VL-8B-Abliterated-Caption-it. This edition applies BF16 · FP8 (F8_E4M3) precision formats to significantly reduce memory usage and improve inference throughput while preserving the dense captioning strength and abliterated behavioral characteristics of the original 8B architecture. The base Qwen3-VL-8B-Abliterated-Caption-it model is a fine-tuned version of Qwen3-VL-8B-Instruct, tailored for Abliterated Captioning and uncensored image description. It is designed to generate highly detailed, descriptive captions across a broad range of visual categories, including complex, sensitive, or nuanced content, while supporting varying aspect ratios and resolutions.

FP8 (8-bit floating point) weight and activation quantization using hardware acceleration on GPUs – FP8 W8A8. Quantization W8A8 FP8-dynamic recipe – examples.

Key Highlights

BF16 · FP8 (F8_E4M3) Compression: Transformer Engine based FP8 quantization reduces VRAM footprint and improves generation speed while maintaining caption quality.
Abliterated Caption Fine-Tuning: Optimized for highly descriptive, dense caption generation with minimized refusal behavior.
8B Vision-Language Architecture: Balanced performance and deployment efficiency compared to larger parameter scales.
High-Density Descriptions: Produces richly detailed captions suitable for dataset generation, metadata enrichment, archival systems, and accessibility pipelines.
Dynamic Resolution Support: Handles diverse image sizes and aspect ratios effectively.
Optimized Deployment: FP8 compression enables smoother deployment on Hopper and other compatible GPU architectures.

Quick Start with Transformers

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

# Load the 8B Abliterated Caption FP8 model
model = Qwen3VLForConditionalGeneration.from_pretrained(
    "prithivMLmods/Qwen3-VL-8B-Abliterated-Caption-it-FP8",
    torch_dtype="auto",
    device_map="auto"
)

processor = AutoProcessor.from_pretrained(
    "prithivMLmods/Qwen3-VL-8B-Abliterated-Caption-it-FP8"
)

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

Dataset Caption Generation: Creating high-density captions for training and archival datasets.
Metadata Enrichment: Enhancing searchability and indexing for large image collections.
Visual Documentation Research: Studying descriptive robustness across complex or sensitive imagery.
Creative and Narrative Projects: Producing rich descriptive text for storytelling and world-building.
Behavioral Analysis Research: Evaluating the impact of abliterated fine-tuning on captioning behavior.

Limitations & Risks

Critical Note: This model minimizes built-in refusal behaviors.

Sensitive Content Exposure: The model may generate explicit or controversial descriptions depending on the input image.
User Responsibility: Outputs must be handled responsibly and used within ethical and legal boundaries.
Hardware Requirements: FP8 requires compatible GPU hardware support for optimal performance and efficiency.