Instructions to use Crimsoin/LORA-Student-Centered-Curriculum-Design with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Crimsoin/LORA-Student-Centered-Curriculum-Design with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Crimsoin/LORA-Student-Centered-Curriculum-Design")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Crimsoin/LORA-Student-Centered-Curriculum-Design") model = AutoModelForCausalLM.from_pretrained("Crimsoin/LORA-Student-Centered-Curriculum-Design", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Crimsoin/LORA-Student-Centered-Curriculum-Design with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Crimsoin/LORA-Student-Centered-Curriculum-Design" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Crimsoin/LORA-Student-Centered-Curriculum-Design", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Crimsoin/LORA-Student-Centered-Curriculum-Design
- SGLang
How to use Crimsoin/LORA-Student-Centered-Curriculum-Design 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 "Crimsoin/LORA-Student-Centered-Curriculum-Design" \ --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": "Crimsoin/LORA-Student-Centered-Curriculum-Design", "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 "Crimsoin/LORA-Student-Centered-Curriculum-Design" \ --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": "Crimsoin/LORA-Student-Centered-Curriculum-Design", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Unsloth Desktop
- Docker Model Runner
How to use Crimsoin/LORA-Student-Centered-Curriculum-Design with Docker Model Runner:
docker model run hf.co/Crimsoin/LORA-Student-Centered-Curriculum-Design
| { | |
| "bos_token_id": 199998, | |
| "do_sample": true, | |
| "eos_token_id": [ | |
| 200002, | |
| 199999 | |
| ], | |
| "max_length": 131072, | |
| "pad_token_id": 199999, | |
| "transformers_version": "4.56.2" | |
| } | |