Improve model card: remove incorrect project page, add usage and citation (#5)

- Improve model card: remove incorrect project page, add usage and citation (f29c4deed7775673a7918aa4a93941656092c17b)


Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>

README.md

@@ -2,7 +2,6 @@
 library_name: transformers
 license: apache-2.0
 pipeline_tag: text-generation
-project_page: https://sites.google.com/view/eagle-llm
 repo_url: https://github.com/recursal/RADLADS-paper
 ---
 
@@ -33,4 +32,56 @@ The file numbering is currently off by one from the step numbers shown in the pa
 |L28-D3584-qwerky7_qwen2-3-4k-ckpt5.pth|2|Qwen2.5-7B-Instruct|RAD-RWKV7|4k ctxlen training, early checkpoint|
 |L28-D3584-qwerky7_qwen2-3-4k.pth|2|Qwen2.5-7B-Instruct|RAD-RWKV7|4k ctxlen training|
 
-More information can be found at the Github repository: [https://github.com/recursal/RADLADS-paper](https://github.com/recursal/RADLADS-paper)
+## Usage
+
+This repository contains various PyTorch `.pth` checkpoints from the RADLADS paper, which are primarily intended for research, ablation studies, and conversion. To use these models with the Hugging Face `transformers` library, you will generally need to convert them to the Hugging Face format first.
+
+Please refer to the original GitHub repository for detailed instructions on how to convert these checkpoints to Hugging Face-compatible formats and for specific usage examples: [https://github.com/recursal/RADLADS-paper](https://github.com/recursal/RADLADS-paper)
+
+For models already converted to Hugging Face format and ready for direct use, please refer to the main [Recursal RADLADS collection](https://huggingface.co/collections/recursal/radlads-6818ee69e99e729ba8a87102) on the Hugging Face Hub.
+
+A conceptual example for loading a text generation model with `transformers` (after it has been converted to Hugging Face format, or if you are using a model from the main collection):
+
+```python
+from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
+import torch
+
+# Replace "recursal/RADLADS-RWKV7-Qwen2.5-7B" with the actual ID of a converted model
+# from the Recursal RADLADS collection, or your local path to a converted model.
+model_name = "recursal/RADLADS-RWKV7-Qwen2.5-7B" 
+
+try:
+    tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+    model = AutoModelForCausalLM.from_pretrained(
+        model_name,
+        torch_dtype=torch.bfloat16, # Adjust dtype based on model
+        device_map="auto",
+        trust_remote_code=True,
+    )
+    pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
+
+    prompt = "The key to life is"
+    print(pipe(prompt, max_new_tokens=20, do_sample=True)[0]["generated_text"])
+
+except Exception as e:
+    print(f"Could not load model directly with pipeline. This repository contains raw checkpoints that require conversion.")
+    print(f"Please refer to the original GitHub repository for detailed conversion and usage instructions: https://github.com/recursal/RADLADS-paper")
+    print(f"Or explore pre-converted models in the Recursal collection: https://huggingface.co/collections/recursal/radlads-6818ee69e99e729ba8a87102")
+
+```
+
+## Citation
+
+If you use this code or find our work valuable, please consider citing RADLADS:
+
+```bibtex
+@misc{goldstein2025radladsrapidattentiondistillation,
+      title={RADLADS: Rapid Attention Distillation to Linear Attention Decoders at Scale}, 
+      author={Daniel Goldstein and Eric Alcaide and Janna Lu and Eugene Cheah},
+      year={2025},
+      eprint={2505.03005},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2505.03005}, 
+}
+```