Instructions to use BayesRL/Olmo3-B3PO_MC-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use BayesRL/Olmo3-B3PO_MC-7B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="BayesRL/Olmo3-B3PO_MC-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("BayesRL/Olmo3-B3PO_MC-7B")
model = AutoModelForMultimodalLM.from_pretrained("BayesRL/Olmo3-B3PO_MC-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use BayesRL/Olmo3-B3PO_MC-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "BayesRL/Olmo3-B3PO_MC-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BayesRL/Olmo3-B3PO_MC-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/BayesRL/Olmo3-B3PO_MC-7B

SGLang

How to use BayesRL/Olmo3-B3PO_MC-7B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "BayesRL/Olmo3-B3PO_MC-7B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BayesRL/Olmo3-B3PO_MC-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

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 "BayesRL/Olmo3-B3PO_MC-7B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BayesRL/Olmo3-B3PO_MC-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use BayesRL/Olmo3-B3PO_MC-7B with Docker Model Runner:
```
docker model run hf.co/BayesRL/Olmo3-B3PO_MC-7B
```

Olmo3-B3PO_MC-7B

📦 Code: insait-institute/c3po

Olmo-3 7B fine-tuned with the decoupled Monte-Carlo variant of B3PO, from the paper "Parameter Exploration for RLVR via Variational Learning".

3PO is a family of parameter-space exploration strategies for Reinforcement Learning with Verifiable Rewards (RLVR). Instead of relying only on action-space heuristics (temperature, clipping, entropy bonuses), 3PO samples model weights from an approximate posterior learned with the variational optimizer IVON; the amount of weight noise becomes an extra control lever for exploration.

B3PO_MC combines B3PO rollouts with the decoupled variance-reduction scheme described in Appendix C of the paper: the IVON gradient estimate uses several Monte-Carlo weight samples that are decoupled from the rollout perturbation, reducing gradient variance without changing the rollout budget.

If you intend to reproduce this run, this corresponds to setting DECOUPLED_MC_SAMPLES > 1 (ivon_config.decoupled_mc_samples) on top of the B3PO configuration.

Training


Base / warm-start	`BayesRL/Olmo3-IVON-SFT-7B`
Foundation model	`allenai/Olmo-3-1025-7B`
Algorithm	B3PO + decoupled MC variance reduction (Appendix C)
RL data	DAPO-Math-17k
Optimizer	IVON, lr `1.0`, ESS (λ) `1e9`
Hardware	8× NVIDIA H200 (144 GB)

Evaluation

Evaluated on AIME 2024–2026, MATH-500, AMC 2023, and Minerva. See the paper for full results.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("BayesRL/Olmo3-B3PO_MC-7B")
tok = AutoTokenizer.from_pretrained("BayesRL/Olmo3-B3PO_MC-7B")

Citation

@misc{venkatkrishna2026parameter,
      title={Parameter Exploration for RLVR via Variational Learning},
      author={Vatsal Venkatkrishna and Nico Daheim and Iryna Gurevych},
      year={2026},
}