Synthetic Pre-pretraining Survives Scale, but Not as a Grammatical Prior
Paper โข 2609.39827 โข Published โข 11
How to use verify-ppt/marin_100b_7b with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="verify-ppt/marin_100b_7b") # pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("verify-ppt/marin_100b_7b", device_map="auto")How to use verify-ppt/marin_100b_7b with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "verify-ppt/marin_100b_7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "verify-ppt/marin_100b_7b",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/verify-ppt/marin_100b_7b
How to use verify-ppt/marin_100b_7b with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "verify-ppt/marin_100b_7b" \
--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": "verify-ppt/marin_100b_7b",
"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 "verify-ppt/marin_100b_7b" \
--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": "verify-ppt/marin_100b_7b",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use verify-ppt/marin_100b_7b with Docker Model Runner:
docker model run hf.co/verify-ppt/marin_100b_7b
This model is part of the study in the paper Synthetic Pre-pretraining Survives Scale, but Not as a Grammatical Prior. It is a SmolLM3-based causal language model trained with a pre-pretraining and pre-training pipeline as described in the paper.
For code and usage instructions, please refer to the GitHub repository. Additional project resources are available on the Hugging Face organization page.
docker model run hf.co/verify-ppt/marin_100b_7b