Add paper and code links to model card

Hi! I'm Niels from the Hugging Face community science team.

I'm opening this PR to improve your model card by adding links to the associated research paper, the official GitHub repository, and the project blog. Providing these links helps researchers and users easily find technical details and the implementation codebase.

README.md

@@ -1,44 +1,48 @@
----
-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
+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. It is the first continuous DLM to rival discrete diffusion models on standard language modeling benchmarks like LM1B and OpenWebText.
+
+- **Paper:** [LangFlow: Continuous Diffusion Rivals Discrete in Language Modeling](https://huggingface.co/papers/2604.11748)
+- **Code:** [GitHub Repository](https://github.com/nealchen2003/LangFlow)
+- **Project Blog:** [LangFlow Blog Post](https://caradryanl.github.io/blog/2026/langflow/)
+
+## 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)