Text Generation
Transformers
PyTorch
TensorBoard
opt
Generated from Trainer
text-generation-inference
Instructions to use Aalaa/opt-125m-custom-data with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Aalaa/opt-125m-custom-data with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Aalaa/opt-125m-custom-data")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Aalaa/opt-125m-custom-data") model = AutoModelForCausalLM.from_pretrained("Aalaa/opt-125m-custom-data", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Aalaa/opt-125m-custom-data with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Aalaa/opt-125m-custom-data" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Aalaa/opt-125m-custom-data", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Aalaa/opt-125m-custom-data
- SGLang
How to use Aalaa/opt-125m-custom-data 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 "Aalaa/opt-125m-custom-data" \ --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": "Aalaa/opt-125m-custom-data", "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 "Aalaa/opt-125m-custom-data" \ --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": "Aalaa/opt-125m-custom-data", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Aalaa/opt-125m-custom-data with Docker Model Runner:
docker model run hf.co/Aalaa/opt-125m-custom-data
Download pytorch_model.bin from Aalaa/opt-125m-custom-data: direct link, hf CLI and curl.
- Browser
- Download file 501 MB
-
https://huggingface.co/Aalaa/opt-125m-custom-data/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://Aalaa/opt-125m-custom-data@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Aalaa/opt-125m-custom-data/resolve/refs%2Fpr%2F1/pytorch_model.bin
501 MB
- Xet hash:
- 6133953844a77c176d7be9da470df6bf34885c9674490848ae6ffd43b8f35fe1
- Size of remote file:
- 501 MB
- SHA256:
- b830bb4979a05762142b5715c49af044dbae76b6518e52c76146af7edc8b6a7b
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