Update README.md

README.md

@@ -7,24 +7,13 @@ library_name: transformers
 base_model: Qwen/Qwen2.5-32B
 ---
 
-<p align="center">
-  <img alt="k2-think-banner" src="https://github.com/MBZUAI-IFM/K2-Think-SFT/blob/main/assets/k2-think-banner.png">
-</p>
-
 <p align="center">
   <a href="https://k2think.ai"><strong>Try K2-Think</strong></a> ·
-  <a href="https://arxiv.org/abs/xxxxxxxx"><strong>Tech Report</strong></a> ·
-  <a href="https://github.com/LLM360/Reasoning360"><strong>Code </strong></a>
 </p>
 
 <br>
 
-K2-Think is a 32 billion parameter open-weights general reasoning model with strong performance in competitive mathematical problem solving. Built on a Qwen2.5-32B base, K2-Think combines long CoT SFT, RL with verifiable rewards, and a test-time scaling scaffold to match or exceed much larger models on public math benchmarks while keeping latency low.
-
-## **Highlights**
-- **Math specialist at 32B:** State-of-the-art results among open models on AIME-style olympiad math and other hard math sets.
-- **Fast generation:** **~2,000 tokens/sec** on our Cerebras WSE deployment; **~10×** faster than typical H100/H200 setups in our tests.
-- **Token-efficient reasoning:** Planning reduces average response length by **up to ~14%** at equal or higher accuracy.
+K2-Think is a 32 billion parameter open-weights general reasoning model with strong performance in competitive mathematical problem solving. 
 
 # Quickstart
 
@@ -82,24 +71,6 @@ We deploy K2-THINK on Cerebras Wafer-Scale Engine (WSE) systems, leveraging the
 | **Cerebras WSE (our deployment)** |             **\~2,000** |                         **\~16 s** |
 | Typical **H100/H200** GPU setup   |                   \~200 |                            \~160 s |
 
----
-
-## Token Efficiency
-
-K2-Think's **Plan-Before-You-Think** methodology combined with Best-of-N sampling produces more concise reasoning chains while maintaining or improving accuracy. Our test-time scaffold reduces average response length by up to **14%** across mathematical benchmarks.
-
-**Token reduction** per completed answer (SFT+RL checkpoint vs K2-Think):
-
-| Domain   | Benchmark      | SFT+RL Checkpoint |   K2-Think |          Δ |
-| -------- | -------------- | ----------------: | ---------: | ---------: |
-| **Math** | AIME24         |            23,324 | **20,058** | **−14.0%** |
-| **Math** | AIME25         |            25,869 | **24,218** | **−6.38%** |
-| **Math** | HMMT25         |            31,475 | **26,977** | **−14.3%** |
-| **Math** | OMNI-Math-HARD |            35,266 | **30,032** | **−14.0%** |
-| Code     | LiveCodeBench  |            13,552 | **12,166** | **−10.2%** |
-| Science  | GPQA-Diamond   |            15,271 | **14,661** | **−3.99%** |
-
-
 ---
 
 ## Safety Evaluation