OLMo-150M and OLMo-1B Pretrained Models
Collection
Pretrained models from scratch used in "Echo Chamber: RL Post-training Amplifies Behaviors Learned in Pretraining". • 12 items • Updated • 4
How to use rosieyzh/OLMo-150M-4xtg with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="rosieyzh/OLMo-150M-4xtg") # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("rosieyzh/OLMo-150M-4xtg")
model = AutoModelForCausalLM.from_pretrained("rosieyzh/OLMo-150M-4xtg", device_map="auto")How to use rosieyzh/OLMo-150M-4xtg with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rosieyzh/OLMo-150M-4xtg"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "rosieyzh/OLMo-150M-4xtg",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/rosieyzh/OLMo-150M-4xtg
How to use rosieyzh/OLMo-150M-4xtg with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "rosieyzh/OLMo-150M-4xtg" \
--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": "rosieyzh/OLMo-150M-4xtg",
"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 "rosieyzh/OLMo-150M-4xtg" \
--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": "rosieyzh/OLMo-150M-4xtg",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use rosieyzh/OLMo-150M-4xtg with Docker Model Runner:
docker model run hf.co/rosieyzh/OLMo-150M-4xtg
150M OLMo model pretrained on 4 passes of the TinyGSM dataset.
Model names contain the contents of the pretraining dataset, delimited by underscores.
If there is a {n}x in front of the dataset abbreviation, that means it was repeated n times during pretraining. For instance, 2xtg refers to two passes over the TinyGSM dataset.