Aunsiels/InfantBooks
Viewer • Updated • 496 • 96 • 7
How to use Aunsiels/ChildGPT with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="Aunsiels/ChildGPT") # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("Aunsiels/ChildGPT")
model = AutoModelForCausalLM.from_pretrained("Aunsiels/ChildGPT")How to use Aunsiels/ChildGPT with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Aunsiels/ChildGPT"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "Aunsiels/ChildGPT",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/Aunsiels/ChildGPT
How to use Aunsiels/ChildGPT with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "Aunsiels/ChildGPT" \
--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": "Aunsiels/ChildGPT",
"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 "Aunsiels/ChildGPT" \
--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": "Aunsiels/ChildGPT",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use Aunsiels/ChildGPT with Docker Model Runner:
docker model run hf.co/Aunsiels/ChildGPT
A GPT2-model finetuned on children's books.
Romero, J., & Razniewski, S. (2022).
Do Children Texts Hold The Key To Commonsense Knowledge?
In Proceedings of the 2022 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning.
docker model run hf.co/Aunsiels/ChildGPT