Instructions to use darkc0de/XORTRON-NXTXPRTXXL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use darkc0de/XORTRON-NXTXPRTXXL with Transformers:

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

pipe = pipeline("image-text-to-text", model="darkc0de/XORTRON-NXTXPRTXXL")
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, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("darkc0de/XORTRON-NXTXPRTXXL")
model = AutoModelForMultimodalLM.from_pretrained("darkc0de/XORTRON-NXTXPRTXXL")
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 darkc0de/XORTRON-NXTXPRTXXL with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "darkc0de/XORTRON-NXTXPRTXXL"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "darkc0de/XORTRON-NXTXPRTXXL",
		"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/darkc0de/XORTRON-NXTXPRTXXL

SGLang

How to use darkc0de/XORTRON-NXTXPRTXXL 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 "darkc0de/XORTRON-NXTXPRTXXL" \
    --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": "darkc0de/XORTRON-NXTXPRTXXL",
		"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 "darkc0de/XORTRON-NXTXPRTXXL" \
        --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": "darkc0de/XORTRON-NXTXPRTXXL",
		"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 darkc0de/XORTRON-NXTXPRTXXL with Docker Model Runner:
```
docker model run hf.co/darkc0de/XORTRON-NXTXPRTXXL
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

This is a decensored version of Darkhn/Mistral-Medium-3.5-128B-BF16-Text-Only, made using Heretic v1.3.0

This model is reproducible!

See the README in the reproduce directory for more information.

Abliteration parameters

Parameter	Value
direction_index	40.13
attn.o_proj.max_weight	1.47
attn.o_proj.max_weight_position	52.50
attn.o_proj.min_weight	1.39
attn.o_proj.min_weight_distance	49.92
mlp.down_proj.max_weight	1.21
mlp.down_proj.max_weight_position	54.38
mlp.down_proj.min_weight	0.76
mlp.down_proj.min_weight_distance	50.68

Performance

Metric	This model	Original model (Darkhn/Mistral-Medium-3.5-128B-BF16-Text-Only)
KL divergence	0.0329	0 (by definition)
Refusals	8/100	93/100

Multimodal reconstruction

This release preserves XORTRON's modified language-model tensors while restoring the untouched Pixtral vision tower and multimodal projector from mistralai/Mistral-Medium-3.5-128B.

The finished checkpoint uses the native Mistral3ForConditionalGeneration architecture. All floating tensors are stored as full FP16 Safetensors, and the repository includes the official processor, tokenizer, image-token configuration, and multimodal chat template.

The imported vision and projector components were not modified by Heretic. The language model remains the XORTRON derivative.

Featherless compatibility

This repository contains the complete multimodal model in full FP16 Safetensors format using the native Mistral3ForConditionalGeneration architecture. It includes XORTRON's modified language model together with the official Pixtral vision tower, multimodal projector, processor configuration, image token configuration, tokenizer, and multimodal chat template.