Instructions to use aaditya/phi_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aaditya/phi_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="aaditya/phi_2", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("aaditya/phi_2", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("aaditya/phi_2", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use aaditya/phi_2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "aaditya/phi_2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "aaditya/phi_2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/aaditya/phi_2
- SGLang
How to use aaditya/phi_2 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 "aaditya/phi_2" \ --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": "aaditya/phi_2", "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 "aaditya/phi_2" \ --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": "aaditya/phi_2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use aaditya/phi_2 with Docker Model Runner:
docker model run hf.co/aaditya/phi_2
| base_model: aaditya/phi_2 | |
| model_type: AutoModelForCausalLM | |
| tokenizer_type: AutoTokenizer | |
| load_in_8bit: false | |
| load_in_4bit: true | |
| strict: false | |
| datasets: | |
| - path: aaditya/alpaca_subset_1 | |
| type: alpaca | |
| val_set_size: 0 | |
| output_dir: . | |
| sequence_len: 2048 | |
| sample_packing: true | |
| pad_to_sequence_len: true | |
| adapter: qlora | |
| lora_model_dir: | |
| lora_r: 64 | |
| lora_alpha: 32 | |
| lora_dropout: 0.05 | |
| lora_target_linear: true | |
| lora_fan_in_fan_out: | |
| wandb_project: | |
| wandb_entity: | |
| wandb_watch: | |
| wandb_name: | |
| wandb_log_model: | |
| gradient_accumulation_steps: 2 | |
| micro_batch_size: 20 | |
| num_epochs: 1 | |
| optimizer: adamw_torch | |
| adam_beta2: 0.95 | |
| adam_epsilon: 0.00001 | |
| max_grad_norm: 1.0 | |
| lr_scheduler: cosine | |
| learning_rate: 0.000003 | |
| train_on_inputs: false | |
| group_by_length: false | |
| bf16: auto | |
| fp16: | |
| tf32: true | |
| gradient_checkpointing: true | |
| gradient_checkpointing_kwargs: | |
| use_reentrant: True | |
| early_stopping_patience: | |
| resume_from_checkpoint: | |
| local_rank: | |
| logging_steps: 1 | |
| xformers_attention: | |
| flash_attention: true | |
| warmup_steps: 100 | |
| evals_per_epoch: 4 | |
| saves_per_epoch: 1 | |
| debug: | |
| deepspeed: | |
| weight_decay: 0.1 | |
| fsdp: | |
| fsdp_config: | |
| resize_token_embeddings_to_32x: true | |
| special_tokens: | |
| pad_token: "<|endoftext|>" |