Instructions to use ModalityDance/slpo-codi-gpt2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ModalityDance/slpo-codi-gpt2 with Transformers:

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

pipe = pipeline("text-generation", model="ModalityDance/slpo-codi-gpt2")

# Load model directly
from transformers import AutoTokenizer, LatentCODIGPT2

tokenizer = AutoTokenizer.from_pretrained("ModalityDance/slpo-codi-gpt2")
model = LatentCODIGPT2.from_pretrained("ModalityDance/slpo-codi-gpt2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ModalityDance/slpo-codi-gpt2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ModalityDance/slpo-codi-gpt2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ModalityDance/slpo-codi-gpt2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ModalityDance/slpo-codi-gpt2

SGLang

How to use ModalityDance/slpo-codi-gpt2 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 "ModalityDance/slpo-codi-gpt2" \
    --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": "ModalityDance/slpo-codi-gpt2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "ModalityDance/slpo-codi-gpt2" \
        --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": "ModalityDance/slpo-codi-gpt2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ModalityDance/slpo-codi-gpt2 with Docker Model Runner:
```
docker model run hf.co/ModalityDance/slpo-codi-gpt2
```

slpo-codi-gpt2 / README.md

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Update model card with arXiv 2607.19691

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metadata

license: mit
library_name: transformers
pipeline_tag: text-generation
tags:
  - latent-reasoning
  - codi
  - slpo
  - gpt2
  - reinforcement-learning
base_model: ModalityDance/latent-tts-codi

CODI + SLPO (GPT-2)

Surrogate Latent Policy Optimization (SLPO) checkpoint on top of CODI (GPT-2 124M). This is the CODI+SLPO model reported in the paper SLPO: Scaling Latent Reasoning via a Surrogate Policy.

Model Details

Backbone: CODI / GPT-2 (ModalityDance/latent-tts-codi)
Method: stopping-gate cold start → SLPO (RLOO) with adaptive latent stopping
Special tokens: <|latent|>, <|start-latent|>, <|end-latent|>
Recommended gate threshold: 0.7
Max latent length: 12

Results (paper main table, Acc)

Deterministic accuracy with dropout disabled and learned stop gate:

Benchmark	Acc	Mean latent length
GSM8K	42.76	11.83
GSM-Hard	9.71	11.94
MultiArith	90.52	11.44

Paper (arXiv): 2607.19691
Hugging Face Paper: 2607.19691
Code: ModalityDance/SLPO
Project page: modalitydance.github.io/SLPO
Base model: ModalityDance/latent-tts-codi
Sibling: ModalityDance/slpo-coconut-gpt2
Collection: ModalityDance/SLPO

Installation

git clone https://github.com/ModalityDance/SLPO.git
cd SLPO
pip install -r requirements.txt   # plus a CUDA PyTorch build
hf download ModalityDance/slpo-codi-gpt2 --local-dir checkpoints/slpo-codi-gpt2

Quick Start

Batched eval (paper Acc settings):

CKPT=checkpoints/slpo-codi-gpt2 \
MODEL_TYPE=codi STOP_POLICY=gate \
STOP_GATE_THRESHOLD=0.7 MAX_LATENT_LENGTH=12 \
DATA=data/gsm_test.json \
bash scripts/eval.sh

Minimal Python (from the repo root; needs the SLPO latent generation stack):

import torch
from transformers import AutoTokenizer

from src.models.generation import LatentGenerationMixin, LatentGenerationConfig
from src.paths import get_model_class

model_id = "ModalityDance/slpo-codi-gpt2"
backbone_cls = get_model_class("codi")

class LatentModel(backbone_cls, LatentGenerationMixin):
    pass

tokenizer = AutoTokenizer.from_pretrained(model_id)
if tokenizer.pad_token is None:
    tokenizer.pad_token = tokenizer.eos_token

model = LatentModel.from_pretrained(model_id)
model.eval()

question = (
    "Janet's ducks lay 16 eggs per day. She eats three for breakfast every morning "
    "and bakes muffins for her friends every day with four. She sells the remainder "
    "at the farmers' market daily for $2 per fresh duck egg. "
    "How much in dollars does she make every day at the farmers' market?"
)
prompt = question + "<|start-latent|>"
inputs = tokenizer(prompt, return_tensors="pt")

gen_cfg = LatentGenerationConfig(
    stop_policy="gate",
    max_latent_length=12,
    stop_gate_threshold=0.7,
    max_new_tokens=128,
    pad_token_id=tokenizer.pad_token_id,
    eos_token_id=tokenizer.eos_token_id,
    bos_token_id=tokenizer.bos_token_id,
)

with torch.no_grad():
    output = model.generate(**inputs, generation_config=gen_cfg)
sequences = output.sequences if hasattr(output, "sequences") else output
print(tokenizer.decode(sequences[0], skip_special_tokens=True))

Citation

@misc{you2026slpo,
  title   = {SLPO: Scaling Latent Reasoning via a Surrogate Policy},
  author  = {You, Runyang and Liu, Zhiyuan and Li, Yongqi and Li, Wenjie},
  year    = {2026},
  eprint  = {2607.19691},
  archivePrefix = {arXiv},
  primaryClass = {cs.CL},
  url     = {https://arxiv.org/abs/2607.19691}
}

ModalityDance
/

slpo-codi-gpt2