Instructions to use apipack/API-Pack-Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use apipack/API-Pack-Model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="apipack/API-Pack-Model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("apipack/API-Pack-Model") model = AutoModelForCausalLM.from_pretrained("apipack/API-Pack-Model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use apipack/API-Pack-Model with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "apipack/API-Pack-Model" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "apipack/API-Pack-Model", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/apipack/API-Pack-Model
- SGLang
How to use apipack/API-Pack-Model 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 "apipack/API-Pack-Model" \ --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": "apipack/API-Pack-Model", "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 "apipack/API-Pack-Model" \ --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": "apipack/API-Pack-Model", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use apipack/API-Pack-Model with Docker Model Runner:
docker model run hf.co/apipack/API-Pack-Model
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Download README.md from apipack/API-Pack-Model: direct link, hf CLI and curl.
- Browser
- Download file 497 Bytes
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https://huggingface.co/apipack/API-Pack-Model/resolve/2db0d88aaa8b8165c2c85e672e66c105ee78b5a1/README.md
- Command line
-
hf download hf://apipack/API-Pack-Model@2db0d88aaa8b8165c2c85e672e66c105ee78b5a1/README.md
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curl -L -o README.md https://huggingface.co/apipack/API-Pack-Model/resolve/2db0d88aaa8b8165c2c85e672e66c105ee78b5a1/README.md
497 Bytes
metadata
license: apache-2.0
library_name: transformers
This is the repository with model for the paper API Pack A Massive Multi-Programming Language Dataset for API Call Generation.
The model is fine-tuned with 1M instances from API-Pack dataset, and is based on CodeLlama-13b-hf, a custom commercial license for which is available.