Instructions to use elozeiri/jais2-curriculum with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use elozeiri/jais2-curriculum with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("/lustre/scratch/users/mohamed.anwar/ONEDRIVE/Projects/jais_plus/checkpoints/20251130_8B_DPO") model = PeftModel.from_pretrained(base_model, "elozeiri/jais2-curriculum") - Transformers
How to use elozeiri/jais2-curriculum with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="elozeiri/jais2-curriculum") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("elozeiri/jais2-curriculum", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use elozeiri/jais2-curriculum with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "elozeiri/jais2-curriculum" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "elozeiri/jais2-curriculum", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/elozeiri/jais2-curriculum
- SGLang
How to use elozeiri/jais2-curriculum 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 "elozeiri/jais2-curriculum" \ --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": "elozeiri/jais2-curriculum", "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 "elozeiri/jais2-curriculum" \ --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": "elozeiri/jais2-curriculum", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use elozeiri/jais2-curriculum with Docker Model Runner:
docker model run hf.co/elozeiri/jais2-curriculum
- Xet hash:
- 575f94b0180d1d64c6f68fb24d5d77ecff2159180e5e89031e7ed11ff00aaa5a
- Size of remote file:
- 1.42 GB
- SHA256:
- 9a9a97e4fa302e22cf6cdf162d523e3e7ff6bed2b248db589fefb39dddee806c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.