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("text-generation", model="darkc0de/XORTRON-NXTXPRTXXL")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("darkc0de/XORTRON-NXTXPRTXXL")
model = AutoModelForMultimodalLM.from_pretrained("darkc0de/XORTRON-NXTXPRTXXL")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.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(tokenizer.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": "What is the capital of France?"
			}
		]
	}'

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": "What is the capital of France?"
			}
		]
	}'

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": "What is the capital of France?"
			}
		]
	}'

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

Downloads last month: 2

Safetensors

Model size

125B params

Tensor type

BF16

Model tree for darkc0de/XORTRON-NXTXPRTXXL

Base model

mistralai/Mistral-Medium-3.5-128B

Finetuned

(9)

this model

Quantizations

2 models