Instructions to use Isaachhe/phi-2_dev with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Isaachhe/phi-2_dev with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Isaachhe/phi-2_dev")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Isaachhe/phi-2_dev") model = AutoModelForCausalLM.from_pretrained("Isaachhe/phi-2_dev") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Isaachhe/phi-2_dev with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Isaachhe/phi-2_dev" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Isaachhe/phi-2_dev", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Isaachhe/phi-2_dev
- SGLang
How to use Isaachhe/phi-2_dev 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 "Isaachhe/phi-2_dev" \ --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": "Isaachhe/phi-2_dev", "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 "Isaachhe/phi-2_dev" \ --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": "Isaachhe/phi-2_dev", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Isaachhe/phi-2_dev with Docker Model Runner:
docker model run hf.co/Isaachhe/phi-2_dev
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("Isaachhe/phi-2_dev")
model = AutoModelForCausalLM.from_pretrained("Isaachhe/phi-2_dev")YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
Copied from https://huggingface.co/susnato/phi-2 commit@9070ddb4fce238899ddbd2aca1faf6a0aeb6e444.
This model can be loaded using HuggingFace transformers commit@4ab5fb8941a38d172b3883c152c34ae2a0b83a68.
Below is the original introduction, which may be expired now.
DISCLAIMER: I don't own the weights to this model, this is a property of Microsoft and taken from their official repository : microsoft/phi-2.
The sole purpose of this repository is to use this model through the transformers API or to load and use the model using the HuggingFace transformers library.
Usage
First make sure you have the latest version of the transformers installed.
pip install -U transformers
Then use the transformers library to load the model from the library itself
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("susnato/phi-2")
tokenizer = AutoTokenizer.from_pretrained("susnato/phi-2")
inputs = tokenizer('''def print_prime(n):
"""
Print all primes between 1 and n
"""''', return_tensors="pt", return_attention_mask=False)
outputs = model.generate(**inputs, max_length=200)
text = tokenizer.batch_decode(outputs)[0]
print(text)
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Isaachhe/phi-2_dev")