Text Generation
Transformers
PyTorch
Safetensors
phi-msft
axolotl
Generated from Trainer
custom_code
Instructions to use satpalsr/phi2-filter2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use satpalsr/phi2-filter2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="satpalsr/phi2-filter2", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("satpalsr/phi2-filter2", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use satpalsr/phi2-filter2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "satpalsr/phi2-filter2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "satpalsr/phi2-filter2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/satpalsr/phi2-filter2
- SGLang
How to use satpalsr/phi2-filter2 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 "satpalsr/phi2-filter2" \ --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": "satpalsr/phi2-filter2", "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 "satpalsr/phi2-filter2" \ --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": "satpalsr/phi2-filter2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use satpalsr/phi2-filter2 with Docker Model Runner:
docker model run hf.co/satpalsr/phi2-filter2
Download training_args.bin from satpalsr/phi2-filter2: direct link, hf CLI and curl.
- Browser
- Download file 5.24 kB
-
https://huggingface.co/satpalsr/phi2-filter2/resolve/main/training_args.bin
- Command line
-
hf download hf://satpalsr/phi2-filter2/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/satpalsr/phi2-filter2/resolve/main/training_args.bin
5.24 kB
- Xet hash:
- c759f0e7bcd2208927b98f35f80ec23aa7c0952109fdc6b0fe9f69d25a82cdd8
- Size of remote file:
- 5.24 kB
- SHA256:
- c67201b896b290970d7f4b444c7f49a41ad5190ca72e99ef204736c0d49d9279
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