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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- 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
| Copied from https://huggingface.co/susnato/phi-2 commit@9070ddb4fce238899ddbd2aca1faf6a0aeb6e444. | |
| This model can be loaded using HuggingFace `transformers` [commit@4ab5fb8941a38d172b3883c152c34ae2a0b83a68](https://github.com/huggingface/transformers/tree/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](https://huggingface.co/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 | |
| ```python | |
| 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) | |
| ``` |