🔥 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 platform for free.

Highlights

KAT-Dev-32B is an open-source 32B-parameter model for software engineering tasks.

On SWE-Bench Verified, KAT-Dev-32B achieves comparable performance with 62.4% resolved and ranks 5th among all open-source models with different scales.

Introduction

KAT-Dev-32B is optimized via several stages of training, including a mid-training stage, supervised fine-tuning (SFT) & reinforcement fine-tuning (RFT) stage and an large-scale agentic reinforcement learning (RL) stage. In summary, our contributions include:

For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our blog.

Quickstart

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "Kwaipilot/KAT-Dev"

# 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)

Claude Code

vllm server

MODEL_PATH="Kwaipilot/KAT-Dev"

vllm serve $MODEL_PATH \
      --enable-prefix-caching \
      --tensor-parallel-size 8 \
      --tool-parser-plugin $MODEL_PATH/qwen3coder_tool_parser.py \
      --chat-template $MODEL_PATH/chat_template.jinja \
      --enable-auto-tool-choice --tool-call-parser qwen3_coder

claude-code-router is a third-party routing utility that allows Claude Code to flexibly switch between different backend APIs. On the dashScope platform, you can install the claude-code-config extension package, which automatically generates a default configuration for claude-code-router with built-in dashScope support.

Once the configuration files and plugin directory are generated, the environment required by ccr will be ready. If needed, you can still manually edit ~/.claude-code-router/config.json and the files under ~/.claude-code-router/plugins/ to customize the setup.

Finally, simply start ccr to run Claude Code and seamlessly connect it with the powerful coding capabilities of KAT-Dev-32B. Happy coding!

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