---
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}
}
```