bryanchrist/EDUMATH_annotations
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How to use bryanchrist/EDUMATH_12b with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("image-text-to-text", model="bryanchrist/EDUMATH_12b") # Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText
processor = AutoProcessor.from_pretrained("bryanchrist/EDUMATH_12b")
model = AutoModelForImageTextToText.from_pretrained("bryanchrist/EDUMATH_12b")How to use bryanchrist/EDUMATH_12b with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bryanchrist/EDUMATH_12b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "bryanchrist/EDUMATH_12b",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/bryanchrist/EDUMATH_12b
How to use bryanchrist/EDUMATH_12b with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "bryanchrist/EDUMATH_12b" \
--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": "bryanchrist/EDUMATH_12b",
"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 "bryanchrist/EDUMATH_12b" \
--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": "bryanchrist/EDUMATH_12b",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use bryanchrist/EDUMATH_12b with Docker Model Runner:
docker model run hf.co/bryanchrist/EDUMATH_12b
KTO-trained version of the Gemma 3 12B IT SFT model as introduced in EDUMATH: Generating Standards-aligned Educational Math Word Problems. See our project repo for usage and our paper for training details/metrics.
@misc{christ2026edumathgeneratingstandardsalignededucational,
title={EDUMATH: Generating Standards-aligned Educational Math Word Problems},
author={Bryan R. Christ and Penelope Molitz and Beau LeBlond and Zachary Gottesman and Jonathan Kropko and Thomas Hartvigsen},
year={2026},
eprint={2510.06965},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2510.06965},
}
Base model
google/gemma-3-12b-pt