Instructions to use Kwaipilot/KAT-Coder-V2.5-Dev with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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
```

Ricardo-H commited on 2 days ago

Commit

3cf634e

verified ·

1 Parent(s): 3a7d874

docs(README): wrap custom-HTML title/benchmark table/footnotes in light cards for dark-mode readability

Browse files

Files changed (1) hide show

README.md +7 -1

README.md CHANGED Viewed

@@ -15,10 +15,12 @@ base_model:
 - Qwen3.6-35B-A3B
 ---
 <div style="display:flex;align-items:center;gap:20px;">
   <img src="kat_logo_hd.png" alt="KAT-Coder logo" width="120">
   <h1 style="margin:0;border:none;padding:0;font-size:52px;color:#1a4a25;">KAT-Coder-V2.5-Dev</h1>
 </div>
 <a href="https://huggingface.co/papers/2607.05471"><img src="https://img.shields.io/badge/Technical%20Report-1a4a25?style=for-the-badge&logo=arxiv&logoColor=white" alt="KAT-Coder Technical Report"></a>
@@ -45,6 +47,7 @@ Following the release of KAT-Coder-V2.5 in July, we are pleased to release the o
   </a>
 </p>
 <table style="border-collapse:collapse;width:100%;margin:0 auto;font-size:12.5px;line-height:1.4;white-space:nowrap;">
 <tr>
   <td style="padding:11px 8px;text-align:center;border-bottom:2px solid #1a4a25;"><span style="color:#1a4a25;font-weight:700;">Benchmark</span></td>
@@ -138,10 +141,12 @@ Following the release of KAT-Coder-V2.5 in July, we are pleased to release the o
   <td style="padding:9px 8px;text-align:center;color:#444444;">15.17</td>
 </tr>
 </table>
 <sub>For **Terminal-Bench 2.1**, the bold number is the average across two agent harnesses; the small numbers below are the per-harness scores (Terminus-2 / Claude Code).</sub>
-<div style="font-size:11.5px;line-height:1.5;color:#8a8a8a;margin-top:6px;">
 <p style="margin:0 0 3px;"><strong style="color:#555;">1. Evaluation method.</strong> All metrics presented in the table are reproduced in-house: we download the public model checkpoints, deploy them via vLLM or SGLang, and evaluate under a unified standardized pipeline. No officially reported results of the respective models are directly adopted in this table. Each model is tested only once on each evaluation set; retests are conducted only if obvious errors are found.</p>
@@ -157,6 +162,7 @@ Following the release of KAT-Coder-V2.5 in July, we are pleased to release the o
 <p style="margin:0 0 2px;">* Gemma4-26B-A4B-it: Two main factors degrade evaluation performance: context overflow (exceeding the 256k context limit) and hallucinated calls to the unsupported MultiEdit tool in this evaluation setup.</p>
 <p style="margin:0;">The above deviations arise from mismatches between model tool preference and the allowed toolset in the evaluation harness, rather than inherent capability limitations of the models.</p>
 </div>
 ## Post-training

 - Qwen3.6-35B-A3B
 ---
+<div style="background-color:#ffffff;border:1px solid #d7e4d2;border-radius:12px;padding:14px 22px;display:inline-block;">
 <div style="display:flex;align-items:center;gap:20px;">
   <img src="kat_logo_hd.png" alt="KAT-Coder logo" width="120">
   <h1 style="margin:0;border:none;padding:0;font-size:52px;color:#1a4a25;">KAT-Coder-V2.5-Dev</h1>
 </div>
+</div>
 <a href="https://huggingface.co/papers/2607.05471"><img src="https://img.shields.io/badge/Technical%20Report-1a4a25?style=for-the-badge&logo=arxiv&logoColor=white" alt="KAT-Coder Technical Report"></a>
   </a>
 </p>
+<div style="background-color:#ffffff;border:1px solid #d7e4d2;border-radius:12px;padding:16px;overflow-x:auto;">
 <table style="border-collapse:collapse;width:100%;margin:0 auto;font-size:12.5px;line-height:1.4;white-space:nowrap;">
 <tr>
   <td style="padding:11px 8px;text-align:center;border-bottom:2px solid #1a4a25;"><span style="color:#1a4a25;font-weight:700;">Benchmark</span></td>
   <td style="padding:9px 8px;text-align:center;color:#444444;">15.17</td>
 </tr>
 </table>
+</div>
 <sub>For **Terminal-Bench 2.1**, the bold number is the average across two agent harnesses; the small numbers below are the per-harness scores (Terminus-2 / Claude Code).</sub>
+<div style="background-color:#ffffff;border:1px solid #ececec;border-radius:12px;padding:14px 18px;margin-top:6px;">
+<div style="font-size:11.5px;line-height:1.5;color:#8a8a8a;">
 <p style="margin:0 0 3px;"><strong style="color:#555;">1. Evaluation method.</strong> All metrics presented in the table are reproduced in-house: we download the public model checkpoints, deploy them via vLLM or SGLang, and evaluate under a unified standardized pipeline. No officially reported results of the respective models are directly adopted in this table. Each model is tested only once on each evaluation set; retests are conducted only if obvious errors are found.</p>
 <p style="margin:0 0 2px;">* Gemma4-26B-A4B-it: Two main factors degrade evaluation performance: context overflow (exceeding the 256k context limit) and hallucinated calls to the unsupported MultiEdit tool in this evaluation setup.</p>
 <p style="margin:0;">The above deviations arise from mismatches between model tool preference and the allowed toolset in the evaluation harness, rather than inherent capability limitations of the models.</p>
+</div>
 </div>
 ## Post-training