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

Libraries

How to use SujiKim/learnweak-evocua-8b-lora-r32-libreoffice-writer 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-libreoffice-writer")

Transformers

How to use SujiKim/learnweak-evocua-8b-lora-r32-libreoffice-writer 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-libreoffice-writer")
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-libreoffice-writer", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use SujiKim/learnweak-evocua-8b-lora-r32-libreoffice-writer 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-libreoffice-writer"
# 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-libreoffice-writer",
		"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-libreoffice-writer

SGLang

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

LearnWeak: Automated Domain Specialization for Small Computer-Use Agents

This repository contains a domain-specialized LoRA adapter for EvoCUA-8B, developed as part of 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. This model focuses on specializing the agent for desktop software environments.

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

Model Description

Small open computer-use agents are practical specialization targets but often exhibit domain-specific failures. LearnWeak introduces an error-aware specialization objective that disentangles planning and execution errors, enabling more behaviorally precise updates. On OSWorld, LearnWeak achieves significant performance gains across various domains such as GIMP, LibreOffice, and VS Code.

How to Get Started with the Model

Serve with vLLM

You can serve the base model with this LoRA adapter enabled using vLLM. Replace the LoRA module name and path as appropriate for the specific domain:

vllm serve meituan/EvoCUA-8B-20260105 \
  --enable-lora \
  --max-lora-rank 32 \
  --lora-modules learnweak-adapter={MODEL_ID}

Use the LoRA module name (e.g., learnweak-adapter) when calling the served model's API.

Training Details

Base Model: meituan/EvoCUA-8B-20260105
Framework: PEFT (LoRA)
Rank: 32
Alpha: 64
Target Modules: q_proj, down_proj, k_proj, up_proj, o_proj, v_proj, gate_proj

Citation

If you find this work useful, please consider citing:

@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-libreoffice-writer

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

Paper • 2605.28775 • Published May 27 • 38