nilq/babylm-10M
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How to use bbunzeck/gpt-wee-small with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="bbunzeck/gpt-wee-small") # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("bbunzeck/gpt-wee-small")
model = AutoModelForCausalLM.from_pretrained("bbunzeck/gpt-wee-small")How to use bbunzeck/gpt-wee-small with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bbunzeck/gpt-wee-small"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "bbunzeck/gpt-wee-small",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/bbunzeck/gpt-wee-small
How to use bbunzeck/gpt-wee-small with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "bbunzeck/gpt-wee-small" \
--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": "bbunzeck/gpt-wee-small",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'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 "bbunzeck/gpt-wee-small" \
--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": "bbunzeck/gpt-wee-small",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use bbunzeck/gpt-wee-small with Docker Model Runner:
docker model run hf.co/bbunzeck/gpt-wee-small
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("bbunzeck/gpt-wee-small")
model = AutoModelForCausalLM.from_pretrained("bbunzeck/gpt-wee-small")Citation:
@inproceedings{bunzeck-zarriess-2023-gpt,
title = "{GPT}-wee: How Small Can a Small Language Model Really Get?",
author = "Bunzeck, Bastian and
Zarrie{\ss}, Sina",
editor = "Warstadt, Alex and
Mueller, Aaron and
Choshen, Leshem and
Wilcox, Ethan and
Zhuang, Chengxu and
Ciro, Juan and
Mosquera, Rafael and
Paranjabe, Bhargavi and
Williams, Adina and
Linzen, Tal and
Cotterell, Ryan",
booktitle = "Proceedings of the BabyLM Challenge at the 27th Conference on Computational Natural Language Learning",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.conll-babylm.2",
doi = "10.18653/v1/2023.conll-babylm.2",
pages = "35--46",
}
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="bbunzeck/gpt-wee-small")