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

Libraries

How to use darkc0de/XortronCriminalComputingConfig with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("darkc0de/XortronCriminalComputingConfig")
model = AutoModelForCausalLM.from_pretrained("darkc0de/XortronCriminalComputingConfig")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use darkc0de/XortronCriminalComputingConfig with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/darkc0de/XortronCriminalComputingConfig

SGLang

How to use darkc0de/XortronCriminalComputingConfig 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/XortronCriminalComputingConfig" \
    --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/XortronCriminalComputingConfig",
		"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/XortronCriminalComputingConfig" \
        --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/XortronCriminalComputingConfig",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use darkc0de/XortronCriminalComputingConfig with Docker Model Runner:
```
docker model run hf.co/darkc0de/XortronCriminalComputingConfig
```

darkc0de/XortronCriminalComputingConfig

by libby055404 - opened Jul 5, 2025

base: refs/heads/main

←

from: refs/pr/2

Discussion Files changed

-5

This PR is in draft mode

libby055404

Jul 5, 2025

No description provided.

VaLtEc-BoY

Sep 18, 2025

•

edited Sep 18, 2025

Hello, is it possible to train embeddinggemma-300M_seq2048_mixed-precision Removing the censorship and putting it in .task, tflitelm and tflite... would be great,Qwen 2.5 0.5B and 1B too. To use on Android with Google's Edge Gallery app, thank you in advance.

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