library_name: openvino base_model:

HauhauCS/Qwen3.5-9B-Uncensored-HauhauCS-Aggressive
GitMylo/Qwen3.5-9B-Uncensored-HauhauCS-Aggressive-safetensors tags:
openvino
intel-npu
int4
qwen3.5
uncensored
optimum pipeline_tag: image-text-to-text license: apache-2.0

Qwen3.5-9B-Uncensored-HauhauCS-Aggressive — OpenVINO INT4 (Intel NPU Optimized)

This repository contains the OpenVINO™ Intermediate Representation (IR) model for Qwen3.5-9B-Uncensored-HauhauCS-Aggressive, optimized for Intel NPU (Neural Processing Unit, such as Intel® Core™ Ultra processors) as well as Intel CPU and GPU.

The model weights are compressed to INT4 Symmetric format using NNCF (Neural Network Compression Framework) via Optimum Intel, meeting Intel NPU hardware execution specifications.

Model Highlights

Base Model: HauhauCS/Qwen3.5-9B-Uncensored-HauhauCS-Aggressive
Safetensors source: GitMylo/Qwen3.5-9B-Uncensored-HauhauCS-Aggressive-safetensors
Quantization: INT4 Symmetric (sym=True, group_size=128, ratio=1.0)
Target Hardware: Intel NPU (AI Boost), Intel CPU, Intel Arc / Iris Xe GPU

Requirements

pip install --upgrade openvino openvino-genai optimum-intel transformers

Inference on Intel NPU

1. Using OpenVINO GenAI (Recommended for low-latency NPU inference)

import openvino_genai as ov_genai

# Load model onto Intel NPU
pipe = ov_genai.VLMPipeline("JaelTmls/Qwen3.5-9B-Uncensored-HauhauCS-Aggressive-OpenVINO-INT4", device="NPU")

# For text-only generation:
prompt = "Explain quantum computing in simple terms."
response = pipe.generate(prompt, max_new_tokens=256)
print(response.texts[0])

(Note: If NPU device is not available, you can replace "NPU" with "GPU" or "CPU").

2. Using Optimum Intel

from optimum.intel.openvino import OVModelForVisualCausalLM
from transformers import AutoProcessor

model_id = "JaelTmls/Qwen3.5-9B-Uncensored-HauhauCS-Aggressive-OpenVINO-INT4"
processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
model = OVModelForVisualCausalLM.from_pretrained(model_id, device="NPU")

# Run generation
inputs = processor("Hello!", return_tensors="pt")
output = model.generate(**inputs, max_new_tokens=100)
print(processor.decode(output[0], skip_special_tokens=True))

Quantization Details

Method: NNCF Weight Compression
Weight format: INT4 Symmetric
Group size: 128
Ratio: 1.0 (all linear layers quantized to 4-bit)

Qwen3.5-9B-Uncensored-HauhauCS-Aggressive — OpenVINO INT4 (Intel NPU Optimized)

The model weights are compressed to INT4 Symmetric format using NNCF (Neural Network Compression Framework) via Optimum Intel, meeting Intel NPU hardware execution specifications.

Inference on Intel NPU

1. Using OpenVINO GenAI (Recommended for low-latency NPU inference)

import openvino_genai as ov_genai # Load model onto Intel NPU pipe = ov_genai.VLMPipeline("JaelTmls/Qwen3.5-9B-Uncensored-HauhauCS-Aggressive-OpenVINO-INT4", device="NPU") # For text-only generation: prompt = "Explain quantum computing in simple terms." response = pipe.generate(prompt, max_new_tokens=256) print(response.texts[0])

(Note: If NPU device is not available, you can replace "NPU" with "GPU" or "CPU").

2. Using Optimum Intel

from optimum.intel.openvino import OVModelForVisualCausalLM from transformers import AutoProcessor model_id = "JaelTmls/Qwen3.5-9B-Uncensored-HauhauCS-Aggressive-OpenVINO-INT4" processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True) model = OVModelForVisualCausalLM.from_pretrained(model_id, device="NPU") # Run generation inputs = processor("Hello!", return_tensors="pt") output = model.generate(**inputs, max_new_tokens=100) print(processor.decode(output[0], skip_special_tokens=True))