Instructions to use abideen/phi2-pro with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use abideen/phi2-pro with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="abideen/phi2-pro", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("abideen/phi2-pro", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("abideen/phi2-pro", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use abideen/phi2-pro with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "abideen/phi2-pro" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "abideen/phi2-pro", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/abideen/phi2-pro
- SGLang
How to use abideen/phi2-pro 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 "abideen/phi2-pro" \ --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": "abideen/phi2-pro", "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 "abideen/phi2-pro" \ --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": "abideen/phi2-pro", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use abideen/phi2-pro with Docker Model Runner:
docker model run hf.co/abideen/phi2-pro
| def default_args(parser): | |
| parser.add_argument("--cache_dir", default=None, type=str) | |
| parser.add_argument("--save_dir", default='./saved', type=str) | |
| parser.add_argument("--data_name", default='HuggingfaceH4/UltraFeedback', type=str) | |
| parser.add_argument("--model_name", default="gpt2", type=str) | |
| # Training Arguments | |
| parser.add_argument("--torch_compile", default=True, type=bool) | |
| parser.add_argument("--flash_attention_2", action='store_true') | |
| parser.add_argument("--lr_scheduler_type", default="cosine", type=str) | |
| parser.add_argument("--optim", default="paged_adamw_32bit", type=str) | |
| parser.add_argument("--overwrite_output_dir", default=True, type=bool) | |
| parser.add_argument("--lr", default=2e-5, type=float) | |
| parser.add_argument("--num_proc", default=1, type=int) | |
| parser.add_argument("--num_train_epochs", default=10, type=int) | |
| parser.add_argument("--per_device_train_batch_size", default=2, type=int) | |
| parser.add_argument("--per_device_eval_batch_size", default=2, type=int) | |
| parser.add_argument("--warmup_steps", default=5000, type=int) | |
| parser.add_argument("--evaluation_strategy", default='epoch', type=str) | |
| parser.add_argument("--do_eval", action='store_true') | |
| parser.add_argument("--gradient_accumulation_steps", default=1, type=int) | |
| parser.add_argument("--save_strategy", default='epoch', type=str) | |
| parser.add_argument("--prompt_max_length", default=256, type=int) | |
| parser.add_argument("--response_max_length", default=1024, type=int) | |
| parser.add_argument("--alpha", default=1.0, type=float) | |
| # Wandb Configurations | |
| parser.add_argument("--wandb_entity", default=None, type=str) | |
| parser.add_argument("--wandb_project_name", default=None, type=str) | |
| args = parser.parse_args() | |
| return args |