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)# pip install -U transformers accelerate # 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 adapter_model.bin from SJBaba/phi-1_5-finetuned-gsm8k: direct link, hf CLI and curl.
- Browser
- Download file 18.9 MB
-
https://huggingface.co/SJBaba/phi-1_5-finetuned-gsm8k/resolve/main/adapter_model.bin
- Command line
-
hf download hf://SJBaba/phi-1_5-finetuned-gsm8k/adapter_model.bin
-
curl -L -o adapter_model.bin https://huggingface.co/SJBaba/phi-1_5-finetuned-gsm8k/resolve/main/adapter_model.bin
18.9 MB
- Xet hash:
- 1c40191d15e092ec13c6de3512a8ccc08a5b5330c3a5d7bf10c1e773177b67d6
- Size of remote file:
- 18.9 MB
- SHA256:
- 5ffb41dc343d26e8dc2d4df77f68d991f4fbd7ba68524aa8068e88282284a1ec
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.