Instructions to use facebook/xglm-564M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/xglm-564M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="facebook/xglm-564M")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("facebook/xglm-564M") model = AutoModelForCausalLM.from_pretrained("facebook/xglm-564M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use facebook/xglm-564M with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "facebook/xglm-564M" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "facebook/xglm-564M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/facebook/xglm-564M
- SGLang
How to use facebook/xglm-564M 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 "facebook/xglm-564M" \ --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": "facebook/xglm-564M", "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 "facebook/xglm-564M" \ --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": "facebook/xglm-564M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use facebook/xglm-564M with Docker Model Runner:
docker model run hf.co/facebook/xglm-564M
Download tf_model.h5 from facebook/xglm-564M: direct link, hf CLI and curl.
- Browser
- Download file 3.32 GB
-
https://huggingface.co/facebook/xglm-564M/resolve/main/tf_model.h5
- Command line
-
hf download hf://facebook/xglm-564M/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/facebook/xglm-564M/resolve/main/tf_model.h5
3.32 GB
- Xet hash:
- ffb074468279df9a03d9ea16f169600689156c53f34a26fddd96908fc0a5b4f5
- Size of remote file:
- 3.32 GB
- SHA256:
- 76107e6b145fdeed0599d2a5770aebaef473815d09ffbe09d96275eba00df1c3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.