Instructions to use SujiKim/learnweak-evocua-8b-lora-r32-vlc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SujiKim/learnweak-evocua-8b-lora-r32-vlc with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("meituan/EvoCUA-8B-20260105")
model = PeftModel.from_pretrained(base_model, "SujiKim/learnweak-evocua-8b-lora-r32-vlc")

Transformers

How to use SujiKim/learnweak-evocua-8b-lora-r32-vlc with Transformers:

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

pipe = pipeline("image-text-to-text", model="SujiKim/learnweak-evocua-8b-lora-r32-vlc")
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 AutoModel
model = AutoModel.from_pretrained("SujiKim/learnweak-evocua-8b-lora-r32-vlc", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use SujiKim/learnweak-evocua-8b-lora-r32-vlc with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SujiKim/learnweak-evocua-8b-lora-r32-vlc"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SujiKim/learnweak-evocua-8b-lora-r32-vlc",
		"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/SujiKim/learnweak-evocua-8b-lora-r32-vlc

SGLang

How to use SujiKim/learnweak-evocua-8b-lora-r32-vlc 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 "SujiKim/learnweak-evocua-8b-lora-r32-vlc" \
    --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": "SujiKim/learnweak-evocua-8b-lora-r32-vlc",
		"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 "SujiKim/learnweak-evocua-8b-lora-r32-vlc" \
        --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": "SujiKim/learnweak-evocua-8b-lora-r32-vlc",
		"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 SujiKim/learnweak-evocua-8b-lora-r32-vlc with Docker Model Runner:
```
docker model run hf.co/SujiKim/learnweak-evocua-8b-lora-r32-vlc
```

learnweak-evocua-8b-lora-r32-gimp

This repository contains a LoRA adapter for meituan/EvoCUA-8B-20260105, specialized for the GIMP desktop domain using the LearnWeak framework.

LearnWeak is an annotation-free specialization framework for small computer-use agents (CUAs). It uses a stronger reference agent to identify a student model's weaknesses in a target domain, synthesizes targeted tasks, and constructs supervision automatically to improve agent performance.

Project Page: https://learnweak.github.io/
Paper: Learn from Weaknesses: Automated Domain Specialization for Small Computer-Use Agents
Repository: https://github.com/sujiikim/LearnWeak

Usage

Serve with vLLM

You can serve the base model with this LoRA adapter using vLLM:

vllm serve meituan/EvoCUA-8B-20260105 \
  --enable-lora \
  --max-lora-rank 32 \
  --lora-modules learnweak-gimp=SujiKim/learnweak-evocua-8b-lora-r32-gimp

Use the LoRA module name learnweak-gimp when calling the served model.

Training Details

LearnWeak introduces an error-aware specialization objective that disentangles planning and execution errors, enabling more behaviorally precise updates than broad uniform supervision. This adapter was trained to overcome specific domain failures identified during the student-aware dataset generation process.

Citation

@article{kim2026learnweaknessesautomateddomain,
  title   = {Learn from Weaknesses: Automated Domain Specialization for Small Computer-Use Agents},
  author  = {Kim, Suji and Kim, Kangsa and Hwang, Sung Ju},
  journal = {arXiv preprint arXiv:2605.28775},
  year    = {2026}
}

Acknowledgments

This project builds on OSWorld, LlamaFactory, and EvoCUA.

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Paper for SujiKim/learnweak-evocua-8b-lora-r32-vlc

Learn from Weaknesses: Automated Domain Specialization for Small Computer-Use Agents

Paper • 2605.28775 • Published May 27 • 38