Instructions to use prithivMLmods/Qwen3.5-0.8B-Unredacted-MAX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/Qwen3.5-0.8B-Unredacted-MAX with Transformers:

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

pipe = pipeline("image-text-to-text", model="prithivMLmods/Qwen3.5-0.8B-Unredacted-MAX")
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/Qwen3.5-0.8B-Unredacted-MAX")
model = AutoModelForImageTextToText.from_pretrained("prithivMLmods/Qwen3.5-0.8B-Unredacted-MAX")
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 Settings

vLLM

How to use prithivMLmods/Qwen3.5-0.8B-Unredacted-MAX with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/Qwen3.5-0.8B-Unredacted-MAX"
# 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/Qwen3.5-0.8B-Unredacted-MAX",
		"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/Qwen3.5-0.8B-Unredacted-MAX

SGLang

How to use prithivMLmods/Qwen3.5-0.8B-Unredacted-MAX 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/Qwen3.5-0.8B-Unredacted-MAX" \
    --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/Qwen3.5-0.8B-Unredacted-MAX",
		"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/Qwen3.5-0.8B-Unredacted-MAX" \
        --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/Qwen3.5-0.8B-Unredacted-MAX",
		"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/Qwen3.5-0.8B-Unredacted-MAX with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/Qwen3.5-0.8B-Unredacted-MAX
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Qwen3.5-0.8B-Unredacted-MAX

Qwen3.5-0.8B-Unredacted-MAX is an optimized release built on top of huihui-ai/Huihui-Qwen3.5-0.8B-abliterated. This version focuses on improved packaging, updated repository structure, and compatibility with modern Transformers pipelines, while preserving the reasoning and instruction-following behavior of the base model. The result is a compact 0.8B parameter language model designed for lightweight deployment, efficient inference, and research-oriented experimentation.

This model is intended for research and learning purposes only. Any outputs generated by this model are the sole responsibility of the user. The authors and hosting platform disclaim all liability for generated content. Users must ensure safe, ethical, and lawful usage.

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-Qwen3.5-0.8B-abliterated

Evaluation Report (Self-Reported)

Model: Qwen3.5-0.8B-Unredacted-MAX

Abliteration Rate (Non-Refusal Rate): 94.500
Refusal Rate: 5.500

The evaluation was conducted using 2000 prompts across multiple runs to measure response behavior consistency. Results are averaged and may vary depending on sampling strategy, prompt distribution, and evaluation methodology.

Evaluation Summary (YAML)

evaluation:
  model_name: Qwen3.5-0.8B-Unredacted-MAX
  total_test_prompts: 2000
  evaluation_runs: 10
  prompts_per_run: 200
  evaluation_type: response_behavior_analysis

results:
  refusal_rate: 5.500
  non_refusal_rate: 94.500
  abliteration_rate: 94.500

Note: These results are self-reported and should be interpreted as approximate behavioral indicators rather than strict benchmark guarantees.

Key Highlights

Optimized Model Packaging Improved repository layout for smoother downloads and inference initialization.
Stable Transformers Compatibility Designed for modern Hugging Face Transformers versions and inference workflows.
0.8B Parameter Architecture Extremely lightweight model suitable for resource-constrained environments.
Efficient Instruction Handling Maintains consistent behavior across structured prompts.
Fast Local Inference Optimized for low-latency deployment on consumer hardware and compact systems.

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/Qwen3.5-0.8B-Unredacted-MAX",
    torch_dtype="auto",
    device_map="auto"
)

processor = AutoProcessor.from_pretrained(
    "prithivMLmods/Qwen3.5-0.8B-Unredacted-MAX"
)

messages = [
    {
        "role": "user",
        "content": [
            {"type": "text", "text": "Explain how transformer models work in simple terms."}
        ],
    }
]

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=256)

output_text = processor.batch_decode(
    [out[len(inp):] for inp, out in zip(inputs.input_ids, generated_ids)],
    skip_special_tokens=True,
    clean_up_tokenization_spaces=False
)

print(output_text)