Update README.md

README.md

@@ -1,58 +1,35 @@
 ---
 base_model: Qwen/Qwen2.5-32B-Instruct
 library_name: transformers
-model_name: Qwen2.5-32B-Instruct-20250119_185226
+model_name: step-conditional-control
 tags:
 - generated_from_trainer
 - trl
 - sft
-licence: license
+license: apache-2.0
 ---
 
-# Model Card for Qwen2.5-32B-Instruct-20250119_185226
+# Model Summary
 
-This model is a fine-tuned version of [Qwen/Qwen2.5-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct).
-It has been trained using [TRL](https://github.com/huggingface/trl).
+- **Repository:** [simplescaling/s1](https://github.com/simplescaling/s1)
+- **Paper:** TODO
 
-## Quick start
+# Use
 
-```python
-from transformers import pipeline
+This is the token-conditional control model for our paper. You can evaluate using the information [here](https://github.com/simplescaling/s1?tab=readme-ov-file#evaluation).
 
-question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
-generator = pipeline("text-generation", model="qfq/Qwen2.5-32B-Instruct-20250119_185226", device="cuda")
-output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
-print(output["generated_text"])
-```
-
-## Training procedure
+# Training information
 
 [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/hashimoto-group/o1/runs/i3e03g4y) 
 
-
-This model was trained with SFT.
-
-### Framework versions
-
 - TRL: 0.13.0
 - Transformers: 4.48.0
 - Pytorch: 2.3.1
 - Datasets: 3.0.1
 - Tokenizers: 0.21.0
 
-## Citations
-
-
+# Citation
 
-Cite TRL as:
-    
 ```bibtex
-@misc{vonwerra2022trl,
-	title        = {{TRL: Transformer Reinforcement Learning}},
-	author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
-	year         = 2020,
-	journal      = {GitHub repository},
-	publisher    = {GitHub},
-	howpublished = {\url{https://github.com/huggingface/trl}}
-}
+TODO
 ```