Instructions to use darkc0de/XORTRON.CriminalComputing.Config.LARGE.XPRT2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use darkc0de/XORTRON.CriminalComputing.Config.LARGE.XPRT2 with Transformers:

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

pipe = pipeline("text-generation", model="darkc0de/XORTRON.CriminalComputing.Config.LARGE.XPRT2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("darkc0de/XORTRON.CriminalComputing.Config.LARGE.XPRT2")
model = AutoModelForCausalLM.from_pretrained("darkc0de/XORTRON.CriminalComputing.Config.LARGE.XPRT2")
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

vLLM

How to use darkc0de/XORTRON.CriminalComputing.Config.LARGE.XPRT2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "darkc0de/XORTRON.CriminalComputing.Config.LARGE.XPRT2"
# 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.CriminalComputing.Config.LARGE.XPRT2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/darkc0de/XORTRON.CriminalComputing.Config.LARGE.XPRT2

SGLang

How to use darkc0de/XORTRON.CriminalComputing.Config.LARGE.XPRT2 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.CriminalComputing.Config.LARGE.XPRT2" \
    --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.CriminalComputing.Config.LARGE.XPRT2",
		"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.CriminalComputing.Config.LARGE.XPRT2" \
        --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.CriminalComputing.Config.LARGE.XPRT2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use darkc0de/XORTRON.CriminalComputing.Config.LARGE.XPRT2 with Docker Model Runner:
```
docker model run hf.co/darkc0de/XORTRON.CriminalComputing.Config.LARGE.XPRT2
```

XORTRON.CriminalComputing.Config.LARGE.XPRT2 / README.md

darkc0de

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1b2a681 verified 12 days ago

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metadata

license: apache-2.0
base_model: mistralai/Mistral-Medium-3.5-128B
tags:
  - mistral
  - mistral-3.5
  - text-only
  - bf16
  - 128b
  - heretic
  - uncensored
  - decensored
  - abliterated
library_name: transformers

This is a decensored version of mistralai/Mistral-Medium-3.5-128B, made using Heretic v1.2.0

Abliteration parameters

Parameter	Value
direction_index	43.15
attn.o_proj.max_weight	1.48
attn.o_proj.max_weight_position	59.65
attn.o_proj.min_weight	1.44
attn.o_proj.min_weight_distance	48.02
mlp.down_proj.max_weight	1.21
mlp.down_proj.max_weight_position	54.75
mlp.down_proj.min_weight	0.30
mlp.down_proj.min_weight_distance	50.44

Performance

Metric	This model	Original model (mistralai/Mistral-Medium-3.5-128B)
KL divergence	0.0220	0 (by definition)
Refusals	9/100	98/100

Thanks to Darkhn for providing the Text Only BF16 base for this tune