Aunsiels/InfantBooks
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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.