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 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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