Instructions to use prithivMLmods/Qwen3.6-35B-A3B-Uncensored-Aggressive with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/Qwen3.6-35B-A3B-Uncensored-Aggressive with Transformers:

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

pipe = pipeline("image-text-to-text", model="prithivMLmods/Qwen3.6-35B-A3B-Uncensored-Aggressive")
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/Qwen3.6-35B-A3B-Uncensored-Aggressive")
model = AutoModelForImageTextToText.from_pretrained("prithivMLmods/Qwen3.6-35B-A3B-Uncensored-Aggressive")
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/Qwen3.6-35B-A3B-Uncensored-Aggressive with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/Qwen3.6-35B-A3B-Uncensored-Aggressive"
# 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/Qwen3.6-35B-A3B-Uncensored-Aggressive",
		"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/Qwen3.6-35B-A3B-Uncensored-Aggressive

SGLang

How to use prithivMLmods/Qwen3.6-35B-A3B-Uncensored-Aggressive 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/Qwen3.6-35B-A3B-Uncensored-Aggressive" \
    --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/Qwen3.6-35B-A3B-Uncensored-Aggressive",
		"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/Qwen3.6-35B-A3B-Uncensored-Aggressive" \
        --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/Qwen3.6-35B-A3B-Uncensored-Aggressive",
		"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/Qwen3.6-35B-A3B-Uncensored-Aggressive with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/Qwen3.6-35B-A3B-Uncensored-Aggressive
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Qwen3.6-35B-A3B-Uncensored-Aggressive

Qwen3.6-35B-A3B-Uncensored-Aggressive is an optimized release built on top of huihui-ai/Huihui-Qwen3.6-35B-A3B-abliterated. This version focuses on updated shard sizing, repository optimization, and compatibility improvements for the latest Transformers releases, while preserving the MoE architecture and reasoning behavior of the original model. The result is a high-capacity 35B Mixture-of-Experts language model designed for efficient inference, stable deployment, and modern ecosystem integration.

GGUF: https://huggingface.co/prithivMLmods/Qwen3.6-35B-A3B-Uncensored-Aggressive-GGUF

This model is intended for research and learning purposes only. Any content generated by this model is used at the user's own risk. The authors and hosting page disclaim any liability for outputs produced by this model. Users are responsible for ensuring safe, ethical, and lawful usage.

Evaluation

Metric	Result
Refusal Rate	N/A
Test Setup	N/A
Inference Type	text-generation
Dataset	N/A

Note: This release does not introduce new benchmark evaluations and primarily focuses on repackaging, sharding updates, and Transformers compatibility improvements over the base model.

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-Qwen3.6-35B-A3B-abliterated

Key Highlights

Latest Transformers Compatibility Re-sharded and optimized for improved compatibility with recent Transformers releases.
Optimized Model Sharding Updated shard structure for improved download reliability, storage handling, and inference efficiency.
Stable Inference Pipeline Improved packaging for consistent loading and generation behavior.
35B MoE Architecture (A3B) Built on Qwen/Qwen3.6-35B-A3B, leveraging Mixture-of-Experts design for scalable reasoning capacity.
Improved Deployment Stability Designed for smoother inference across different hardware configurations.
Preserved Model Behavior No changes to weights or architecture; behavior remains consistent with the original model lineage.

Quick Start with Transformers

pip install transformers==5.2.0
# or
pip install git+https://github.com/huggingface/transformers.git

from transformers import Qwen3_5MoeForConditionalGeneration, AutoProcessor
import torch

model = Qwen3_5MoeForConditionalGeneration.from_pretrained(
    "prithivMLmods/Qwen3.6-35B-A3B-Uncensored-Aggressive",
    torch_dtype="auto",
    device_map="auto"
)

processor = AutoProcessor.from_pretrained(
    "prithivMLmods/Qwen3.6-35B-A3B-Uncensored-Aggressive"
)

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)

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

Multimodal and Language Research Studying large-scale MoE behavior and inference characteristics.
Red-Teaming & Evaluation Testing robustness across complex and adversarial prompts.
High-Performance Deployment Running large MoE models on optimized multi-GPU setups.
Research Prototyping Experimentation with scalable transformer architectures.

Limitations & Risks

Important Note: This model inherits the behavior and limitations of its base model.

Output Variability Responses may vary depending on sampling settings and prompt structure.
Resource Requirements A 35B MoE model requires significant GPU memory and optimized inference strategies such as quantization or tensor parallelism.
Deployment Constraints Performance depends heavily on hardware configuration and runtime optimization.
General Model Limitations May produce incorrect, incomplete, or inconsistent outputs in complex scenarios.