Instructions to use Aspik101/trurl-2-13b-pl-instruct_unload with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Aspik101/trurl-2-13b-pl-instruct_unload with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Aspik101/trurl-2-13b-pl-instruct_unload")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("Aspik101/trurl-2-13b-pl-instruct_unload") model = AutoModelForMultimodalLM.from_pretrained("Aspik101/trurl-2-13b-pl-instruct_unload") - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Aspik101/trurl-2-13b-pl-instruct_unload with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Aspik101/trurl-2-13b-pl-instruct_unload" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Aspik101/trurl-2-13b-pl-instruct_unload", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Aspik101/trurl-2-13b-pl-instruct_unload
- SGLang
How to use Aspik101/trurl-2-13b-pl-instruct_unload 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 "Aspik101/trurl-2-13b-pl-instruct_unload" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Aspik101/trurl-2-13b-pl-instruct_unload", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "Aspik101/trurl-2-13b-pl-instruct_unload" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Aspik101/trurl-2-13b-pl-instruct_unload", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Aspik101/trurl-2-13b-pl-instruct_unload with Docker Model Runner:
docker model run hf.co/Aspik101/trurl-2-13b-pl-instruct_unload
- Xet hash:
- f17bf5650d63f2d7bde367b6f68fc7d070ff7eeda20de6e5ec2c802a5a48d5f3
- Size of remote file:
- 6.18 GB
- SHA256:
- 0b66527e4a7bcc98580165cf07f2490da75fa9377eb10e9c0f70417e0ea91a51
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.