Improve model card: Add paper abstract

This PR improves the model card by adding the paper's abstract. Including the abstract directly in the model card provides users with a quick overview of the model's capabilities and methodology without needing to click through to the full paper, enhancing the discoverability and utility of the artifact on the Hub.

README.md

@@ -17,6 +17,9 @@ pipeline_tag: text-generation
 
 K2-Think is a 32 billion parameter open-weights general reasoning model with strong performance in competitive mathematical problem solving. 
 
+## Abstract
+K2-Think is a reasoning system that achieves state-of-the-art performance with a 32B parameter model, matching or surpassing much larger models like GPT-OSS 120B and DeepSeek v3.1. Built on the Qwen2.5 base model, our system shows that smaller models can compete at the highest levels by combining advanced post-training and test-time computation techniques. The approach is based on six key technical pillars: Long Chain-of-thought Supervised Finetuning, Reinforcement Learning with Verifiable Rewards (RLVR), Agentic planning prior to reasoning, Test-time Scaling, Speculative Decoding, and Inference-optimized Hardware, all using publicly available open-source datasets. K2-Think excels in mathematical reasoning, achieving state-of-the-art scores on public benchmarks for open-source models, while also performing strongly in other areas such as Code and Science. Our results confirm that a more parameter-efficient model like K2-Think 32B can compete with state-of-the-art systems through an integrated post-training recipe that includes long chain-of-thought training and strategic inference-time enhancements, making open-source reasoning systems more accessible and affordable. K2-Think is freely available at this http URL , offering best-in-class inference speeds of over 2,000 tokens per second per request via the Cerebras Wafer-Scale Engine.
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 # Quickstart
 
 ### Transformers