Improve model card: Add pipeline tag, update license and expand descriptions (#1)

- Improve model card: Add pipeline tag, update license and expand descriptions (eca3fb3fd02b680e4f43b3657e1a66eb2a646f75)


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

README.md

@@ -1,38 +1,43 @@
 ---
-library_name: transformers
-license: other
 base_model: QizhiPei/Qwen2.5-Math-7B-Instruct-RoPE-300k
+library_name: transformers
+license: apache-2.0
 tags:
 - llama-factory
 - full
 - generated_from_trainer
+pipeline_tag: text-generation
 model-index:
 - name: DiffScale-7B
   results: []
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
-
 Paper: [ScaleDiff: Scaling Difficult Problems for Advanced Mathematical Reasoning](https://arxiv.org/abs/2509.21070)
 
 Code: https://github.com/QizhiPei/ScaleDiff
 
 # DiffScale-7B
 
-This model is a fine-tuned version of [QizhiPei/Qwen2.5-Math-7B-Instruct-RoPE-300k](https://huggingface.co/QizhiPei/Qwen2.5-Math-7B-Instruct-RoPE-300k) on the DiffScale-Math dataset.
+This model is a fine-tuned version of [QizhiPei/Qwen2.5-Math-7B-Instruct-RoPE-300k](https://huggingface.co/QizhiPei/Qwen2.5-Math-7B-Instruct-RoPE-300k) on the ScaleDiff-Math dataset.
 
 ## Model description
 
-More information needed
+ScaleDiff-7B is a Large Reasoning Model (LRM) developed as part of the ScaleDiff pipeline, which is designed to scale the creation of challenging mathematical problems. This model, fine-tuned on the novel ScaleDiff-Math dataset, aims to enhance advanced mathematical reasoning capabilities by addressing the scarcity of high-quality, difficult training data. It leverages an adaptive thinking model for problem identification and a specialized generator (DiffGen-8B) for large-scale problem synthesis.
 
 ## Intended uses & limitations
 
-More information needed
+ScaleDiff-7B is intended for advanced mathematical reasoning tasks, offering significant improvements in complex problem-solving. It is particularly useful for researchers and practitioners looking to benchmark and develop LRMs on difficult mathematical challenges.
+
+**Limitations**: As a language model, its performance is dependent on the quality and scope of its training data. While designed for difficult problems, it may exhibit limitations in highly novel or out-of-distribution mathematical contexts. Further research is needed to fully understand its generalization capabilities beyond the specific benchmarks used in its evaluation.
 
 ## Training and evaluation data
 
-More information needed
+ScaleDiff-7B was fine-tuned on the custom-created [ScaleDiff-Math dataset](https://huggingface.co/datasets/QizhiPei/ScaleDiff-Math). This dataset is generated through a three-step pipeline:
+1.  **Problem Selection**: Difficult problems are identified from the [AM-Distilled-Dataset](https://huggingface.co/datasets/a-m-team/AM-Qwen3-Distilled) using AdaptThink, an adaptive thinking model.
+2.  **Problem Generation**: A dedicated problem generator, DiffGen-8B, is trained on these selected difficult problems to produce new, challenging problems.
+3.  **Solution Distillation and Filtration**: Long Chain-of-Thought (CoT) solutions for the newly generated problems are distilled using Qwen3-8B as a teacher model and then filtered for quality and relevance.
+
+The final ScaleDiff-Math dataset combines these new problem-solution pairs with an original dataset to provide a more effective training signal. Evaluation was conducted on a suite of difficult mathematical benchmarks including AIME'24, AIME'25, HMMT-Feb'25, BRUMO'25, and MATH500.
 
 ## Training procedure
 
@@ -61,5 +66,4 @@ The following hyperparameters were used during training:
 - Transformers 4.46.1
 - Pytorch 2.4.0+cu121
 - Datasets 3.1.0
-- Tokenizers 0.20.3
-
+- Tokenizers 0.20.3