Text Generation
Transformers
Safetensors
English
mistral
mergekit
Merge
uncensored
harmful
conversational
text-generation-inference
Instructions to use darkc0de/XortronCriminalComputingConfig with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
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pipeline_tag: text-generation
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new_version: darkc0de/XORTRON-XPRT-GGUF
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You can try this model now for free at [xortron.tech](https://xortron.tech/)
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State-of-the-art **Uncensored** performance.
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This model will help you do anything and everything you probably shouldn't be doing.
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As of this writing
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language:
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pipeline_tag: text-generation
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---
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You can try this model now for free at [xortron.tech](https://xortron.tech/)
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State-of-the-art **Uncensored** performance.
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This model will help you do anything and everything you probably shouldn't be doing.
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As of this writing, this model tops the **UGI Leaderboard** for models under 70 billion parameters in both the **UGI** and **W10** categories.
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