Instructions to use Kwaipilot/KAT-Coder-V2.5-Dev with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kwaipilot/KAT-Coder-V2.5-Dev with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Kwaipilot/KAT-Coder-V2.5-Dev") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("Kwaipilot/KAT-Coder-V2.5-Dev") model = AutoModelForMultimodalLM.from_pretrained("Kwaipilot/KAT-Coder-V2.5-Dev", device_map="auto") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] inputs = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Kwaipilot/KAT-Coder-V2.5-Dev with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Kwaipilot/KAT-Coder-V2.5-Dev" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Kwaipilot/KAT-Coder-V2.5-Dev", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Kwaipilot/KAT-Coder-V2.5-Dev
- SGLang
How to use Kwaipilot/KAT-Coder-V2.5-Dev with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "Kwaipilot/KAT-Coder-V2.5-Dev" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Kwaipilot/KAT-Coder-V2.5-Dev", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "Kwaipilot/KAT-Coder-V2.5-Dev" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Kwaipilot/KAT-Coder-V2.5-Dev", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Kwaipilot/KAT-Coder-V2.5-Dev with Docker Model Runner:
docker model run hf.co/Kwaipilot/KAT-Coder-V2.5-Dev
Still Waiting for Vision 👀
KAT-Coder-V2.5-Dev has basically become my default coding model.
The only thing I still miss is vision support. Screenshots, browser renders, UI mockups, and visual debugging are a huge part of modern development, and that's probably the biggest gap KAT still has.
I know I'm not the first person to bring this up, but consider this another vote for vision. 😄
Thanks for building such a great open-weight model.
I was just wondering about this. I went searching for an mmproj file in vain. I would also love to have vision added!
try this out ?
beyoru/KAT-Coder-V2.5-Dev-VLbeyoru/KAT-Coder-V2.5-Dev-VL-Flash
It seems the vision component is subpar:
Vision capability — READ THIS
The vision tower was never co-trained with this checkpoint's language model, which was heavily RL-tuned for agentic coding. Measured on the bf16 parent (this build inherits the behaviour, and 4-bit experts can only make it worse):
probe bf16 parent
Dominant colour (4 solid-colour images) 4 / 4
Shape (circle / square / triangle) 3 / 3
Text/number reading (OCR) 0 / 4 — CAT→CCT, 42→48, HELLO→Hiro
✅ Coarse visual questions: colour, shape, layout, rough scene gist.
❌ Not usable for: reading code from screenshots, OCR, UI labels, document or chart understanding — anything where one wrong character changes the answer.
If your workload is text-only, the vision tower costs you 0.9 GB and nothing else; the language model is what this checkpoint is for.