Update README.md

README.md

@@ -1,44 +1,46 @@
----
-datasets:
-- Skylion007/openwebtext
-language:
-- en
-library_name: transformers
-license: apache-2.0
-metrics:
-- perplexity
-pipeline_tag: text-generation
----
-
-# LangFlow
-
-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:
-
-```python
-from transformers import AutoModelForMaskedLM, AutoTokenizer
-
-tokenizer = AutoTokenizer.from_pretrained('gpt2')
-model = AutoModelForMaskedLM.from_pretrained('chumengl/langflow-owt', trust_remote_code=True)
-
-# Generate samples
-samples = model.generate_samples(num_samples=5, num_steps=128)
-texts = tokenizer.batch_decode(samples, skip_special_tokens=True)
-for text in texts:
-    print(text)
-```
-
-## Model Details
-
-- **Architecture**: DiT (Diffusion Transformer) backbone with adaptive layer normalization
-- **Context Length**: 1024 tokens
-- **Parameters**: ~130M non-embedding parameters (similar to GPT-2 medium)
-- **Training**: 1M steps on OpenWebText corpus
-- **Tokenizer**: GPT-2 tokenizer (50,257 vocab size)
-
-## Model Card Contact
-
-Chumeng Liang (chumengl@illinois.edu)
+---
+datasets:
+- Skylion007/openwebtext
+papers:
+- arxiv: 2604.11748
+language:
+- en
+library_name: transformers
+license: apache-2.0
+metrics:
+- perplexity
+pipeline_tag: text-generation
+---
+
+# LangFlow
+
+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:
+
+```python
+from transformers import AutoModelForMaskedLM, AutoTokenizer
+
+tokenizer = AutoTokenizer.from_pretrained('gpt2')
+model = AutoModelForMaskedLM.from_pretrained('chumengl/langflow-owt', trust_remote_code=True)
+
+# Generate samples
+samples = model.generate_samples(num_samples=5, num_steps=128)
+texts = tokenizer.batch_decode(samples, skip_special_tokens=True)
+for text in texts:
+    print(text)
+```
+
+## Model Details
+
+- **Architecture**: DiT (Diffusion Transformer) backbone with adaptive layer normalization
+- **Context Length**: 1024 tokens
+- **Parameters**: ~130M non-embedding parameters (similar to GPT-2 medium)
+- **Training**: 1M steps on OpenWebText corpus
+- **Tokenizer**: GPT-2 tokenizer (50,257 vocab size)
+
+## Model Card Contact
+
+Chumeng Liang (chumengl@illinois.edu)