Libraries Transformers How to use bs-modeling-metadata/website_metadata_exp_1_model_100k_checkpoint with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="bs-modeling-metadata/website_metadata_exp_1_model_100k_checkpoint", device_map="auto") # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("bs-modeling-metadata/website_metadata_exp_1_model_100k_checkpoint")
model = AutoModelForCausalLM.from_pretrained("bs-modeling-metadata/website_metadata_exp_1_model_100k_checkpoint", device_map="auto") Notebooks Google Colab Kaggle Local Apps Settings vLLM How to use bs-modeling-metadata/website_metadata_exp_1_model_100k_checkpoint with vLLM:
Install from pip and serve model # Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bs-modeling-metadata/website_metadata_exp_1_model_100k_checkpoint"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "bs-modeling-metadata/website_metadata_exp_1_model_100k_checkpoint",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}' Use Docker docker model run hf.co/bs-modeling-metadata/website_metadata_exp_1_model_100k_checkpoint SGLang How to use bs-modeling-metadata/website_metadata_exp_1_model_100k_checkpoint 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 "bs-modeling-metadata/website_metadata_exp_1_model_100k_checkpoint" \
--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": "bs-modeling-metadata/website_metadata_exp_1_model_100k_checkpoint",
"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 "bs-modeling-metadata/website_metadata_exp_1_model_100k_checkpoint" \
--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": "bs-modeling-metadata/website_metadata_exp_1_model_100k_checkpoint",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}' Docker Model Runner How to use bs-modeling-metadata/website_metadata_exp_1_model_100k_checkpoint with Docker Model Runner:
docker model run hf.co/bs-modeling-metadata/website_metadata_exp_1_model_100k_checkpoint 
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="bs-modeling-metadata/website_metadata_exp_1_model_100k_checkpoint", device_map="auto")