Instructions to use darkc0de/XORTRON.CriminalComputing.Config.LARGE.XPRT2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
gguf please?
Refreshed this page for 3 days now, wondering if get a gguf at Q4? I'm eager to try this.
Refreshed this page for 3 days now, wondering if get a gguf at Q4? I'm eager to try this.
Honest question, why dont you do it?
If you can't I'm willing to quantize it for you.
Or just wait until Darkc0de does it.
Cheers!
I've tried using the huggingface online spaces thing but it didnt work, I'm guessing the model is too big. I have no clue how to do it on my own pc. Gemini said i wouldnt have the ram to do it either.