Instructions to use cjvt/OPT_GaMS-1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cjvt/OPT_GaMS-1B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="cjvt/OPT_GaMS-1B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("cjvt/OPT_GaMS-1B") model = AutoModelForCausalLM.from_pretrained("cjvt/OPT_GaMS-1B") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use cjvt/OPT_GaMS-1B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "cjvt/OPT_GaMS-1B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "cjvt/OPT_GaMS-1B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/cjvt/OPT_GaMS-1B
- SGLang
How to use cjvt/OPT_GaMS-1B 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 "cjvt/OPT_GaMS-1B" \ --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": "cjvt/OPT_GaMS-1B", "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 "cjvt/OPT_GaMS-1B" \ --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": "cjvt/OPT_GaMS-1B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use cjvt/OPT_GaMS-1B with Docker Model Runner:
docker model run hf.co/cjvt/OPT_GaMS-1B
- Xet hash:
- 6bacd976d18995f77c3e61d6d55204f91c8f073d91b1c8073c0dc04d184a3aa8
- Size of remote file:
- 5.67 GB
- SHA256:
- 4d07d9e9da8fc12bcbb6ad282aa1846edb25a5b53defba5361d9ade3b6a79760
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.