Instructions to use EshAhm/phi-2-IA3_1-Python with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use EshAhm/phi-2-IA3_1-Python with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("microsoft/phi-2") model = PeftModel.from_pretrained(base_model, "EshAhm/phi-2-IA3_1-Python") - Transformers
How to use EshAhm/phi-2-IA3_1-Python with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="EshAhm/phi-2-IA3_1-Python")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("EshAhm/phi-2-IA3_1-Python", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use EshAhm/phi-2-IA3_1-Python with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "EshAhm/phi-2-IA3_1-Python" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "EshAhm/phi-2-IA3_1-Python", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/EshAhm/phi-2-IA3_1-Python
- SGLang
How to use EshAhm/phi-2-IA3_1-Python 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 "EshAhm/phi-2-IA3_1-Python" \ --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": "EshAhm/phi-2-IA3_1-Python", "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 "EshAhm/phi-2-IA3_1-Python" \ --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": "EshAhm/phi-2-IA3_1-Python", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use EshAhm/phi-2-IA3_1-Python with Docker Model Runner:
docker model run hf.co/EshAhm/phi-2-IA3_1-Python
Download training_args.bin from EshAhm/phi-2-IA3_1-Python: direct link, hf CLI and curl.
- Browser
- Download file 5.78 kB
-
https://huggingface.co/EshAhm/phi-2-IA3_1-Python/resolve/main/training_args.bin
- Command line
-
hf download hf://EshAhm/phi-2-IA3_1-Python/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/EshAhm/phi-2-IA3_1-Python/resolve/main/training_args.bin
5.78 kB
- Xet hash:
- eaf0883c3cbf465a083aa3c117c0eef0278373e71848e0f5d4beaa11acddb699
- Size of remote file:
- 5.78 kB
- SHA256:
- c7751546debff2a5dcbe01b4eb571ccad8628a5646775338ccac633642ebc6ab
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