Create README.md

README.md

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+---
+license: apache-2.0
+base_model:
+- Kwaipilot/KAT-Dev-72B-Exp
+pipeline_tag: text-generation
+library_name: transformers
+---
+
+GGUF quants of [Kwaipilot/KAT-Dev-72B-Exp](https://huggingface.co/Kwaipilot/KAT-Dev-72B-Exp)
+
+Using llama.cpp b6730 (commit [e60f01d941bc5b7fae62dd57fee4cec76ec0ea6e](https://github.com/ggml-org/llama.cpp/commit/e60f01d941bc5b7fae62dd57fee4cec76ec0ea6e))
+
+The importance matrix was generated with calibration_datav3.txt.
+
+All quants were generated/calibrated with the imatrix, including the K quants.
+
+---
+
+<div align="center">
+  <img src="https://cdn-uploads.huggingface.co/production/uploads/61ee40a269351366e29972ad/KIYEa1c_WJEWPpeS0L_k1.png" width="100%" alt="Kwaipilot" />
+</div>
+
+<hr>
+
+
+# News
+
+🔥 We’re thrilled to announce the release of **KAT-Dev-72B-Exp**, our latest and most powerful model yet!
+
+🔥 You can now try our **strongest** proprietary coder model **KAT-Coder** directly on the [**StreamLake**](https://www.streamlake.ai/product/kat-coder) platform **for free**.
+
+# Highlights
+**KAT-Dev-72B-Exp**  is an open-source 72B-parameter model for software engineering tasks.
+
+On SWE-Bench Verified, **KAT-Dev-72B-Exp** achieves **74.6%** accuracy ⚡ — **when evaluated strictly with the SWE-agent scaffold**.
+
+**KAT-Dev-72B-Exp** is the experimental reinforcement-learning version of the KAT-Coder model. Through this open-source release, we aim to reveal the technical innovations behind KAT-Coder’s large-scale RL to developers and researchers.
+
+
+![Kim 2025-10-10 165138](https://cdn-uploads.huggingface.co/production/uploads/61ee40a269351366e29972ad/-1nx5HYc-wTjUFNbf-GfO.png)
+
+# Introduction
+ 
+We rewrote the attention kernel and redesigned the training engine for shared prefix trajectories to achieve highly efficient RL training, especially for scaffolds leveraging context management. 
+
+Furthermore, to prevent exploration collapse observed in RL training, we reshaped advantage distribution based on pass rates: amplifying the advantage scale of highly exploratory groups while reducing that of low-exploration ones.
+
+
+# SWE agent Evaluation Parameters
+
+```
+temperature: 0.6
+max_turns: 150
+history_processors.n: 100
+```
+
+For full settings please refer to inference.yaml