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
```

Add community evaluation results for SWE-BENCH_VERIFIED, SWE-BENCH_PRO

#11

by nielsr HF Staff - opened 3 days ago

base: refs/heads/main

←

from: refs/pr/11

Discussion Files changed

+14

-0

nielsr

3 days ago

This PR adds community-provided evaluation results for the following benchmarks:

SWE-BENCH_VERIFIED
SWE-BENCH_PRO

These results were extracted from the model card. This is based on the new evaluation results feature.

Note: This is an automated PR. Please review the evaluation results before merging.

Add community evaluation results for SWE-BENCH_VERIFIED, SWE-BENCH_PRO062ce875

liushiyang

Kwaipilot org 2 days ago

Thanks for this automated PR.
After consideration, we prefer not to merge it for now. There exist significant discrepancies across different SWE-Bench evaluation setups, and merging results obtained under inconsistent evaluation standards will limit the comparability of our benchmark table.

nielsr

2 days ago

Hi, thanks.

Note that this feature is based on trust by the community, by extracting reported evals from papers, model cards, blog posts, Github READMEs and more to consolidate the numbers.

liushiyang

Kwaipilot org 2 days ago

@nielsr We recommend removing our model from the ranking list, as otherwise misunderstandings may arise where the outside world assumes our scores are relatively low. Thank you.

nielsr

2 days ago

Ok, I'll close this PR.

nielsr changed pull request status to closed 2 days ago

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