Instructions to use prithivMLmods/Gliese-Qwen3.5-9B-Abliterated-Caption with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/Gliese-Qwen3.5-9B-Abliterated-Caption with Transformers:

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

pipe = pipeline("image-text-to-text", model="prithivMLmods/Gliese-Qwen3.5-9B-Abliterated-Caption")
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/Gliese-Qwen3.5-9B-Abliterated-Caption")
model = AutoModelForImageTextToText.from_pretrained("prithivMLmods/Gliese-Qwen3.5-9B-Abliterated-Caption")
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]:]))

llama-cpp-python

How to use prithivMLmods/Gliese-Qwen3.5-9B-Abliterated-Caption with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="prithivMLmods/Gliese-Qwen3.5-9B-Abliterated-Caption",
	filename="GGUF/Gliese-Qwen3.5-9B-Abliterated-Caption.BF16.gguf",
)

llm.create_chat_completion(
	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"
					}
				}
			]
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use prithivMLmods/Gliese-Qwen3.5-9B-Abliterated-Caption with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf prithivMLmods/Gliese-Qwen3.5-9B-Abliterated-Caption:BF16
# Run inference directly in the terminal:
llama-cli -hf prithivMLmods/Gliese-Qwen3.5-9B-Abliterated-Caption:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf prithivMLmods/Gliese-Qwen3.5-9B-Abliterated-Caption:BF16
# Run inference directly in the terminal:
llama-cli -hf prithivMLmods/Gliese-Qwen3.5-9B-Abliterated-Caption:BF16

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf prithivMLmods/Gliese-Qwen3.5-9B-Abliterated-Caption:BF16
# Run inference directly in the terminal:
./llama-cli -hf prithivMLmods/Gliese-Qwen3.5-9B-Abliterated-Caption:BF16

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf prithivMLmods/Gliese-Qwen3.5-9B-Abliterated-Caption:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf prithivMLmods/Gliese-Qwen3.5-9B-Abliterated-Caption:BF16

Use Docker

docker model run hf.co/prithivMLmods/Gliese-Qwen3.5-9B-Abliterated-Caption:BF16

LM Studio
Jan

vLLM

How to use prithivMLmods/Gliese-Qwen3.5-9B-Abliterated-Caption with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/Gliese-Qwen3.5-9B-Abliterated-Caption"
# 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/Gliese-Qwen3.5-9B-Abliterated-Caption",
		"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/Gliese-Qwen3.5-9B-Abliterated-Caption:BF16

SGLang

How to use prithivMLmods/Gliese-Qwen3.5-9B-Abliterated-Caption 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/Gliese-Qwen3.5-9B-Abliterated-Caption" \
    --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/Gliese-Qwen3.5-9B-Abliterated-Caption",
		"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/Gliese-Qwen3.5-9B-Abliterated-Caption" \
        --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/Gliese-Qwen3.5-9B-Abliterated-Caption",
		"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"
						}
					}
				]
			}
		]
	}'

Ollama
How to use prithivMLmods/Gliese-Qwen3.5-9B-Abliterated-Caption with Ollama:
```
ollama run hf.co/prithivMLmods/Gliese-Qwen3.5-9B-Abliterated-Caption:BF16
```

Unsloth Studio new

How to use prithivMLmods/Gliese-Qwen3.5-9B-Abliterated-Caption with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for prithivMLmods/Gliese-Qwen3.5-9B-Abliterated-Caption to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for prithivMLmods/Gliese-Qwen3.5-9B-Abliterated-Caption to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for prithivMLmods/Gliese-Qwen3.5-9B-Abliterated-Caption to start chatting

Pi new

How to use prithivMLmods/Gliese-Qwen3.5-9B-Abliterated-Caption with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf prithivMLmods/Gliese-Qwen3.5-9B-Abliterated-Caption:BF16

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "prithivMLmods/Gliese-Qwen3.5-9B-Abliterated-Caption:BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use prithivMLmods/Gliese-Qwen3.5-9B-Abliterated-Caption with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf prithivMLmods/Gliese-Qwen3.5-9B-Abliterated-Caption:BF16

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default prithivMLmods/Gliese-Qwen3.5-9B-Abliterated-Caption:BF16

Run Hermes

hermes

Docker Model Runner
How to use prithivMLmods/Gliese-Qwen3.5-9B-Abliterated-Caption with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/Gliese-Qwen3.5-9B-Abliterated-Caption:BF16
```

Lemonade

How to use prithivMLmods/Gliese-Qwen3.5-9B-Abliterated-Caption with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull prithivMLmods/Gliese-Qwen3.5-9B-Abliterated-Caption:BF16

Run and chat with the model

lemonade run user.Gliese-Qwen3.5-9B-Abliterated-Caption-BF16

List all available models

lemonade list

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

Gliese-Qwen3.5-9B-Abliterated-Caption is an abliterated evolution built on top of Qwen/Qwen3.5-9B, designed specifically for generalized and unfiltered image captioning. The model applies advanced refusal direction analysis and abliterated training strategies to minimize internal refusal behaviors while maximizing descriptive capability and visual understanding. The result is a powerful 9B parameter vision-language model optimized for highly detailed captions, deep scene understanding, and rich visual descriptions.

This model is materialized for research and learning purposes only. The model has reduced internal refusal behaviors, and any content generated by it is used at the user’s own risk. The authors and hosting page disclaim any liability for content generated by this model. Users are responsible for ensuring that the model is used in a a safe, ethical, and lawful manner.

Get GGUF

File Name	Quant Type	File Size	File Link
Gliese-Qwen3.5-9B-Abliterated-Caption.BF16.gguf	BF16	17.9 GB	Download
Gliese-Qwen3.5-9B-Abliterated-Caption.F16.gguf	F16	17.9 GB	Download
Gliese-Qwen3.5-9B-Abliterated-Caption.F32.gguf	F32	35.8 GB	Download
Gliese-Qwen3.5-9B-Abliterated-Caption.Q8_0.gguf	Q8_0	9.53 GB	Download
Gliese-Qwen3.5-9B-Abliterated-Caption.mmproj-bf16.gguf	mmproj-bf16	922 MB	Download
Gliese-Qwen3.5-9B-Abliterated-Caption.mmproj-f16.gguf	mmproj-f16	922 MB	Download
Gliese-Qwen3.5-9B-Abliterated-Caption.mmproj-f32.gguf	mmproj-f32	1.82 GB	Download
Gliese-Qwen3.5-9B-Abliterated-Caption.mmproj-q8_0.gguf	mmproj-q8_0	624 MB	Download

Expert Image Captioning System (chat_template.jinja) – https://huggingface.co/prithivMLmods/Gliese-Qwen3.5-9B-Abliterated-Caption/blob/main/chat_template.jinja [Recommended]

Standard or Default (chat_template.jinja) – https://huggingface.co/prithivMLmods/Gliese-Qwen3.5-9B-Abliterated-Caption/blob/main/standard-chat_template/chat_template.jinja

Download the model

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

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

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.