Update README.md

README.md

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-license: apache-2.0
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+---
+license: apache-2.0
+---
+<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.
+
+
+# Quickstart
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "KAT-Dev-72B-Exp"
+
+# load the tokenizer and the model
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+
+# prepare the model input
+prompt = "Give me a short introduction to large language model."
+messages = [
+    {"role": "user", "content": prompt}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True,
+)
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+# conduct text completion
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=65536
+)
+output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() 
+
+content = tokenizer.decode(output_ids, skip_special_tokens=True)
+
+print("content:", content)
+```
+
+# SWE agent Evaluation Parameters
+
+```
+temperature: 0.6
+max_turns: 150
+history_processors.n: 100
+```
+
+For full settings please refer to inference.yaml