Instructions to use SJBaba/phi-1_5-finetuned-gsm8k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SJBaba/phi-1_5-finetuned-gsm8k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="SJBaba/phi-1_5-finetuned-gsm8k", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("SJBaba/phi-1_5-finetuned-gsm8k", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use SJBaba/phi-1_5-finetuned-gsm8k with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "SJBaba/phi-1_5-finetuned-gsm8k" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "SJBaba/phi-1_5-finetuned-gsm8k", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/SJBaba/phi-1_5-finetuned-gsm8k
- SGLang
How to use SJBaba/phi-1_5-finetuned-gsm8k 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 "SJBaba/phi-1_5-finetuned-gsm8k" \ --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": "SJBaba/phi-1_5-finetuned-gsm8k", "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 "SJBaba/phi-1_5-finetuned-gsm8k" \ --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": "SJBaba/phi-1_5-finetuned-gsm8k", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use SJBaba/phi-1_5-finetuned-gsm8k with Docker Model Runner:
docker model run hf.co/SJBaba/phi-1_5-finetuned-gsm8k
Download training_args.bin from SJBaba/phi-1_5-finetuned-gsm8k: direct link, hf CLI and curl.
- Browser
- Download file 4.54 kB
-
https://huggingface.co/SJBaba/phi-1_5-finetuned-gsm8k/resolve/main/training_args.bin
- Command line
-
hf download hf://SJBaba/phi-1_5-finetuned-gsm8k/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/SJBaba/phi-1_5-finetuned-gsm8k/resolve/main/training_args.bin
4.54 kB
- Xet hash:
- 5f0992470b97d7c67da08b0357979f6c7ab1a3f9c66ece24053ea4ef9bb74319
- Size of remote file:
- 4.54 kB
- SHA256:
- c6b086353e59f9ffc00f8ce79de6f8152ccbbde37382150a13049580f749fb5b
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