Instructions to use m80hz/KITE-7B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use m80hz/KITE-7B-Instruct with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="m80hz/KITE-7B-Instruct")
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("m80hz/KITE-7B-Instruct")
model = AutoModelForMultimodalLM.from_pretrained("m80hz/KITE-7B-Instruct")
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 m80hz/KITE-7B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "m80hz/KITE-7B-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "m80hz/KITE-7B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/m80hz/KITE-7B-Instruct

SGLang

How to use m80hz/KITE-7B-Instruct 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 "m80hz/KITE-7B-Instruct" \
    --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": "m80hz/KITE-7B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "m80hz/KITE-7B-Instruct" \
        --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": "m80hz/KITE-7B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use m80hz/KITE-7B-Instruct with Docker Model Runner:
```
docker model run hf.co/m80hz/KITE-7B-Instruct
```

KITE-7B-Instruct

KITE-7B-Instruct is a fine-tuned version of Qwen2.5-VL-7B-Instruct for VLM-based robot failure analysis, released as part of the KITE paper (ICRA 2026).

This checkpoint contains the full merged weights (base + LoRA adapter), ready for direct inference with no additional merge step.

Model Details


Base model	Qwen/Qwen2.5-VL-7B-Instruct
Parameters	~7B
Fine-tuning	QLoRA (4-bit NF4) on RoboFAC textual + multimodal tasks
Architecture	Qwen2.5-VL (vision-language, conditional generation)
License	Apache 2.0 (same as base model)

Usage

from transformers import AutoProcessor, AutoModelForVision2Seq

model_id = "m80hz/KITE-7B-Instruct"
processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForVision2Seq.from_pretrained(model_id, device_map="auto", trust_remote_code=True)

Or serve it with vLLM for OpenAI-compatible inference:

python -m vllm.entrypoints.openai.api_server --model m80hz/KITE-7B-Instruct

Then use the KITE pipeline to run failure analysis:

python -m kite.cli \
  --model_name m80hz/KITE-7B-Instruct \
  --model_url http://127.0.0.1:8000/v1 \
  --dataset_folder ./datasets/robofac/simulation_data \
  --test_file ./datasets/robofac/test_qa_sim/test_detect_identify_locate.json \
  --out_dir ./outputs/kite_run

Usage

@inproceedings{hosseinzadeh2025kite,
  title     = {KITE: Keyframe-Indexed Tokenized Evidence for VLM-Based Robot Failure Analysis},
  author    = {Hosseinzadeh, Mehdi and Wong, King Hang and Dayoub, Feras},
  booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
  year      = {2026}
}

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Safetensors

Model size

8B params

Tensor type

F16

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