Update README.md

README.md

@@ -29,9 +29,9 @@ State-of-the-art open-data 7B reasoning model. 🚀
 
 This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the 
 [OpenThoughts3-1.2M](https://huggingface.co/datasets/open-thoughts/OpenThoughts3-1.2M) dataset. 
-It represents a notable improvement over our previous models, [OpenThinker-7B](https://huggingface.co/open-thoughts/OpenThinker-7B) and [OpenThinker2-7B](https://huggingface.co/open-thoughts/OpenThinker2-7B), and it outperforms several other strong reasoning 7B models such as [DeepSeek-R1-Distill-Qwen-32B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B) and [Llama-3.1-Nemotron-Nano-8B-v1](https://huggingface.co/nvidia/Llama-3.1-Nemotron-Nano-8B-v1), despite being trained only with SFT, without any RL.
+It represents a notable improvement over our previous models, [OpenThinker-7B](https://huggingface.co/open-thoughts/OpenThinker-7B) and [OpenThinker2-7B](https://huggingface.co/open-thoughts/OpenThinker2-7B), and it outperforms several other strong reasoning 7B models such as [DeepSeek-R1-Distill-Qwen-7B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B) and [Llama-3.1-Nemotron-Nano-8B-v1](https://huggingface.co/nvidia/Llama-3.1-Nemotron-Nano-8B-v1), despite being trained only with SFT, without any RL.
 
-This time, we also release a paper! See our [paper](https://arxiv.org/abs/2506.04178) and [blog post](https://openthoughts.ai/blog/ot3) for more details. OpenThinker3-32B to follow! 👀
+This time, we also released a paper! See our [paper](https://arxiv.org/abs/2506.04178) and [blog post](https://openthoughts.ai/blog/ot3) for more details. OpenThinker3-32B to follow! 👀
 
 # Evaluation Results
 The numbers reported in the table below are evaluated with our open-source tool [Evalchemy](https://github.com/mlfoundations/Evalchemy). 
@@ -52,8 +52,8 @@ In the table below, we bold values in each column that are within 2 standard err
 
 This model was trained on the [OpenThoughts3-1.2M](https://huggingface.co/datasets/open-thoughts/OpenThoughts3-1.2M) dataset.
 
-The key behind the strong model performance is our comprehensive data pipeline and 1000+ ablation experiments.
-This led to the creation of [OpenThoughts3-1.2M](https://huggingface.co/datasets/open-thoughts/OpenThoughts3-1.2M), which consists of 850,000 math examples, 250,000 code examples, and 100,000 science examples.
+The key to the strong model performance is our comprehensive data pipeline and over 1,000+ ablation experiments.
+This led to the creation of [OpenThoughts3-1.2M](https://huggingface.co/datasets/open-thoughts/OpenThoughts3-1.2M), which consists of 850,000 math questions, 250,000 code questions, and 100,000 science questions.
 Reasoning traces are generated with QwQ-32B. 
 
 See the [OpenThoughts3-1.2M](https://huggingface.co/datasets/open-thoughts/OpenThoughts3-1.2M) dataset page or our [paper](https://arxiv.org/abs/2506.04178) for additional information.
@@ -81,6 +81,7 @@ The following hyperparameters were used during training:
 - lr_scheduler_type: cosine
 - lr_scheduler_warmup_ratio: 0.1
 - num_epochs: 5.0
+- weight_decay: 0.0
 
 ## Framework versions