Instructions to use Continuous-Rivals-Discrete/langflow-owt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Continuous-Rivals-Discrete/langflow-owt with Transformers:

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

pipe = pipeline("text-generation", model="Continuous-Rivals-Discrete/langflow-owt", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForMaskedLM
model = AutoModelForMaskedLM.from_pretrained("Continuous-Rivals-Discrete/langflow-owt", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Continuous-Rivals-Discrete/langflow-owt with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Continuous-Rivals-Discrete/langflow-owt

SGLang

How to use Continuous-Rivals-Discrete/langflow-owt 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 "Continuous-Rivals-Discrete/langflow-owt" \
    --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": "Continuous-Rivals-Discrete/langflow-owt",
		"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 "Continuous-Rivals-Discrete/langflow-owt" \
        --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": "Continuous-Rivals-Discrete/langflow-owt",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Continuous-Rivals-Discrete/langflow-owt with Docker Model Runner:
```
docker model run hf.co/Continuous-Rivals-Discrete/langflow-owt
```

chumengl commited on Apr 16

Commit

a08f933

verified ·

1 Parent(s): d48a15b

Update README.md

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@@ -16,6 +16,9 @@ pipeline_tag: text-generation
 LangFlow is a continuous diffusion language model that operates in embedding space. Unlike discrete diffusion models (MDLM, SEDD, DUO), LangFlow performs diffusion directly on continuous token embeddings, enabling smoother denoising dynamics.
 ## Using LangFlow
 To use the pre-trained model for text generation, use the following snippet:
@@ -41,6 +44,18 @@ for text in texts:
 - **Training**: 1M steps on OpenWebText corpus
 - **Tokenizer**: GPT-2 tokenizer (50,257 vocab size)
 ## Model Card Contact
 Chumeng Liang (chumengl@illinois.edu)

 LangFlow is a continuous diffusion language model that operates in embedding space. Unlike discrete diffusion models (MDLM, SEDD, DUO), LangFlow performs diffusion directly on continuous token embeddings, enabling smoother denoising dynamics.
+For more details, please see our paper: [LangFlow: Continuous Diffusion Rivals Discrete in Language Modeling](https://arxiv.org/abs/2604.11748).
 ## Using LangFlow
 To use the pre-trained model for text generation, use the following snippet:
 - **Training**: 1M steps on OpenWebText corpus
 - **Tokenizer**: GPT-2 tokenizer (50,257 vocab size)
+## Citation
+```
+@article{chen2026langflow,
+  title={LangFlow: Continuous Diffusion Rivals Discrete in Language Modeling},
+  author={Chen, Yuxin and Liang, Chumeng and Sui, Hangke and Guo, Ruihan and Cheng, Chaoran and You, Jiaxuan and Liu, Ge},
+  journal={arXiv preprint arXiv:2604.11748},
+  year={2026}
+}
+```
 ## Model Card Contact
 Chumeng Liang (chumengl@illinois.edu)