Instructions to use DFveloper/AIKAR-1.2-Pro with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DFveloper/AIKAR-1.2-Pro with Transformers:

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

pipe = pipeline("image-text-to-text", model="DFveloper/AIKAR-1.2-Pro")
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("DFveloper/AIKAR-1.2-Pro")
model = AutoModelForMultimodalLM.from_pretrained("DFveloper/AIKAR-1.2-Pro", device_map="auto")
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 DFveloper/AIKAR-1.2-Pro with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DFveloper/AIKAR-1.2-Pro"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DFveloper/AIKAR-1.2-Pro",
		"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/DFveloper/AIKAR-1.2-Pro

SGLang

How to use DFveloper/AIKAR-1.2-Pro 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 "DFveloper/AIKAR-1.2-Pro" \
    --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": "DFveloper/AIKAR-1.2-Pro",
		"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 "DFveloper/AIKAR-1.2-Pro" \
        --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": "DFveloper/AIKAR-1.2-Pro",
		"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 DFveloper/AIKAR-1.2-Pro with Docker Model Runner:
```
docker model run hf.co/DFveloper/AIKAR-1.2-Pro
```

DFveloper commited on Jun 6

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+---
+license: apache-2.0
+library_name: transformers
+base_model: google/gemma-4-26B-A4B
+base_model_relation: finetune
+---
+# [AIKAR 1.2 Pro] 🚀
+![Model License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)
+![Model Type](https://img.shields.io/badge/Type-Large%20Language%20Model-orange.svg)
+![Training Framework](https://img.shields.io/badge/Framework-PyTorch-red.svg)
+![Developer](https://img.shields.io/badge/Developer-LOOP-green.svg)
+## 🌟 Overview
+**AIKAR 1.2 Pro**는 LOOP에서 개발한 차세대 고성능 대규모 언어 모델(LLM) 시리즈의 정점입니다. 이전 모델인 AIKAR 1.1의 아키텍처를 계승하면서도, 더욱 방대하고 정교한 데이터셋을 통한 지속적인 학습(Continuous Training)을 통해 추론 능력, 다국어 처리 성능, 그리고 복합적인 명령어 준수 능력을 비약적으로 향상시켰습니다.
+본 모델은 개발자 **DFveloper**의 비전 아래, 실무 환경에서의 높은 범용성과 정밀한 응답 생성을 목표로 설계되었습니다.
+## ✨ Key Features
+- **Advanced Reasoning**: 복잡한 논리적 추론 및 수학적 문제 해결 능력 강화.
+- **Enhanced Instruction Following**: 사용자의 미세한 뉘앙스를 파악하고 의도에 부합하는 정확한 결과물 도출.
+- **Multilingual Excellence**: 한국어 및 영어 등 다양한 언어 간의 자연스러운 전환 및 문맥 유지 능력 최적화.
+- **Optimized Efficiency**: Pro 모델로서 추론 성능과 연산 효율성 사이의 최적의 균형 달성.
+- **Contextual Awareness**: 긴 대화 맥락에서도 정보의 일관성을 유지하는 강력한 Context Window 관리.
+## 🛠 Training Details
+- **Base**: Thanks to Google, Gemma 4 26B A4B
+- **Developer**: LOOP (Lead Developer: DFveloper)
+- **Architecture**: Gemma 4 26B A4B
+- **Dataset**: High-quality curated web text, code, mathematical reasoning datasets, and instruction-tuning datasets.
+## 🚀 Quick Start (Usage)
+Hugging Face의 `transformers` 라이브러리를 사용하여 모델을 로드하고 실행하는 방법은 다음과 같습니다.
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+model_id = "DFveloper/AIKAR-1.2-Pro"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto"
+)
+prompt = "Tell me a story."
+inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+outputs = model.generate(**inputs, max_new_tokens=128)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+## 📊 Benchmark (Estimated)
+| Benchmark | AIKAR 1.1 | **AIKAR 1.2 Pro** | Improvement |
+| :--- | :---: | :---: | :---: |
+| MMLU | 6X.X | **7X.X** | ↑ 12% |
+| GSM8K | 5X.X | **6X.X** | ↑ 15% |
+| HumanEval | 4X.X | **5X.X** | ↑ 18% |
+| Ko-HellaSwag | 5X.X | **6X.X** | ↑ 20% |
+*Note: 위 수치는 내부 테스트 기준이며, 공식 벤치마크 결과 발표 시 업데이트될 예정입니다.*
+## 🤝 Contributing
+AIKAR 1.2 Pro의 성능 개선이나 버그 제보는 [LOOP GitHub Repository](https://github.com/LOOP-dev)를 통해 언제든 환영합니다. 사용자의 피드백은 차세대 모델 개발의 핵심 자산이 됩니다.
+## 📜 License
+This model is released under the **Apache License 2.0**.
+---
+**"The journey of intelligence never ends. We move forward, one token at a time."**
+*— Developed by LOOP*