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 / README.md

m80hz

Update README.md

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	---
	language:
	- en
	license: apache-2.0
	library_name: transformers
	base_model: Qwen/Qwen2.5-VL-7B-Instruct
	tags:
	- robotics
	- failure-analysis
	- vision-language
	- qwen2.5-vl
	- lora
	- finetuned
	- video-text-to-text
	pipeline_tag: image-text-to-text
	---

	# KITE-7B-Instruct

	KITE-7B-Instruct is a fine-tuned version of [Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct) for VLM-based robot failure analysis, released as part of the [KITE](https://github.com/m80hz/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

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

	Then use the [KITE pipeline](https://github.com/m80hz/kite) to run failure analysis:
	```python
	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}
	}
	```