Update README.md

README.md

@@ -17,7 +17,7 @@ This is a Speech Lanaguage Model trained for generating speech contiuations over
 ## Model Details
 
 ### Model Description
-This is a Speech Lanaguage Model, introduced in "_Slamming_: Training a Speech Language Model on One GPU in a Day", focusing on efficient training. 
+This is a Speech Lanaguage Model, introduced in "[_Slamming_: Training a Speech Language Model on One GPU in a Day](https://arxiv.org/abs/2502.15814)", focusing on efficient training. 
 It was fine-tuned from [Qwen/Qwen2.5-0.5B](https://huggingface.co/Qwen/Qwen2.5-0.5B) over a vocabulary of 500 speech tokens extracted from 
 the 11-th layer of [mhubert-25hz](https://huggingface.co/slprl/mhubert-base-25hz). For a stronger version of the model trained with 
 slightly more compute - 2*A100 for 2 days, see [slam_scaled](https://huggingface.co/slprl/slam_scaled).
@@ -34,7 +34,7 @@ The model was trained by next-token prediction over a subset of LibriSpeech, Lib
 ### Model Sources
 
 - **Repository:** [https://github.com/slp-rl/slamkit](https://github.com/slp-rl/slamkit)
-- **Paper:** [Soon!]
+- **Paper:** [https://arxiv.org/abs/2502.15814](https://arxiv.org/abs/2502.15814)
 - **Demo:** [Link](https://pages.cs.huji.ac.il/adiyoss-lab/slamming/)
 
 ## Uses
@@ -51,7 +51,7 @@ We refer users to the official repository for full usage explainations - [github
 
 
 ## Training Details
-We highly encourage users to read the full [paper](), for full training details, a brief overview is provided below.
+We highly encourage users to read the full [paper](https://arxiv.org/abs/2502.15814), for full training details, a brief overview is provided below.
 
 
 ### Training Data
@@ -86,7 +86,7 @@ The paper provides full results, we do give here some results and also refer to
 
 
 ### Compute Infrastructure
-This model was trained as part of ["*Slamming*: Training a Speech Language Model on One GPU in a Day"], focusing on efficient training.
+This model was trained as part of ["*Slamming*: Training a Speech Language Model on One GPU in a Day"](https://arxiv.org/abs/2502.15814), focusing on efficient training.
 
 #### Hardware
 This model was trained using **only a single Nvidia A5000 GPU**, 16 CPU cores and 24 GB of RAM for **24 hours**.
@@ -98,4 +98,14 @@ easy and efficent training of Speech Language Models.
 ## Citation
 
 **BibTeX:**
-Soon!
+```
+@misc{maimon2025slamming,
+      title={Slamming: Training a Speech Language Model on One GPU in a Day}, 
+      author={Gallil Maimon and Avishai Elmakies and Yossi Adi},
+      year={2025},
+      eprint={2502.15814},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2502.15814}, 
+}
+```