Instructions to use edougawa/Nex-N2-mini-Abliterated-NVFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use edougawa/Nex-N2-mini-Abliterated-NVFP4 with Transformers:

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

pipe = pipeline("image-text-to-text", model="edougawa/Nex-N2-mini-Abliterated-NVFP4")
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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("edougawa/Nex-N2-mini-Abliterated-NVFP4")
model = AutoModelForMultimodalLM.from_pretrained("edougawa/Nex-N2-mini-Abliterated-NVFP4", device_map="auto")
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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use edougawa/Nex-N2-mini-Abliterated-NVFP4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "edougawa/Nex-N2-mini-Abliterated-NVFP4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "edougawa/Nex-N2-mini-Abliterated-NVFP4",
		"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/edougawa/Nex-N2-mini-Abliterated-NVFP4

SGLang

How to use edougawa/Nex-N2-mini-Abliterated-NVFP4 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 "edougawa/Nex-N2-mini-Abliterated-NVFP4" \
    --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": "edougawa/Nex-N2-mini-Abliterated-NVFP4",
		"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 "edougawa/Nex-N2-mini-Abliterated-NVFP4" \
        --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": "edougawa/Nex-N2-mini-Abliterated-NVFP4",
		"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 edougawa/Nex-N2-mini-Abliterated-NVFP4 with Docker Model Runner:
```
docker model run hf.co/edougawa/Nex-N2-mini-Abliterated-NVFP4
```

Nex-N2-mini-Abliterated-NVFP4

NVIDIA ModelOpt NVFP4 unified Hugging Face checkpoint produced from edougawa/Nex-N2-mini-Abliterated.

This is a decensored (abliterated) version of nex-agi/Nex-N2-mini, produced with Abliterix v1.8.0.

Abliteration orthogonalizes the model weights against the measured "refusal" direction, reducing refusals while keeping the base model's capabilities as intact as possible (low KL divergence). It does not add any new knowledge or capability — all credit for the underlying model belongs to Nex-AGI. The original model card is reproduced in full below.

Base model: nex-agi/Nex-N2-mini (Apache-2.0, by Nex-AGI)

⚠️ Safety disclaimer

This model has had its built-in refusal behavior deliberately reduced. As a result it may produce unexpected, offensive, inaccurate, or otherwise harmful output, and may comply with requests that the original model would have refused.

It is provided by the publisher, edougawa, "as is" and without warranty of any kind, express or implied. Use at your own risk.
You are solely responsible for how you use this model and for ensuring your use — and any generated output — complies with all applicable laws, regulations, and the terms of the base model's license.
To the maximum extent permitted by law, the publisher (edougawa), the base-model authors (Nex-AGI), and the Abliterix authors accept no liability for any claim, damages, or other consequences arising from the use of this model or its outputs.
Outputs do not reflect the views of the publisher (edougawa), the base-model authors (Nex-AGI), or the Abliterix authors. Apply your own safety filtering, human review, and guardrails before any production or user-facing use.

Quantization

ModelOpt: 0.44.0
PyTorch: 2.11.0+cu130
Transformers: 5.12.0
Format: nvfp4_experts_only
Calibration samples: 256,128,128
Calibration sequence length: 2048
KV cache in checkpoint: unquantized
Target hardware: NVIDIA GB10 / Blackwell SM121
Runtime target: vLLM ModelOpt FP4 loader

The expert-only preset quantizes the dominant MoE expert weights to NVFP4 while retaining attention, embeddings, LM head, vision encoder, and MTP-sensitive weights at their exported higher precision. This choice prioritizes accuracy.

Features

Text generation
Image understanding architecture/config preserved
Video token/config preserved

vLLM

Use only on NVIDIA Blackwell hardware with an NVFP4-capable vLLM build. Review the source model card for its intended use, limitations, and safety notes.

Downloads last month: 19

Safetensors

Model size

19B params

Tensor type

BF16

F8_E4M3

Model tree for edougawa/Nex-N2-mini-Abliterated-NVFP4

Base model

nex-agi/Nex-N2-mini

Finetuned

edougawa/Nex-N2-mini-Abliterated

Quantized

(1)

this model