Image-Text-to-Text
Transformers
Safetensors
Turkish
English
lora
vision-language
math
exam
yks
turkish
Instructions to use cgalabs/yks-vlm-lora-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cgalabs/yks-vlm-lora-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="cgalabs/yks-vlm-lora-v2")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("cgalabs/yks-vlm-lora-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use cgalabs/yks-vlm-lora-v2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "cgalabs/yks-vlm-lora-v2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "cgalabs/yks-vlm-lora-v2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/cgalabs/yks-vlm-lora-v2
- SGLang
How to use cgalabs/yks-vlm-lora-v2 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 "cgalabs/yks-vlm-lora-v2" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "cgalabs/yks-vlm-lora-v2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "cgalabs/yks-vlm-lora-v2" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "cgalabs/yks-vlm-lora-v2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use cgalabs/yks-vlm-lora-v2 with Docker Model Runner:
docker model run hf.co/cgalabs/yks-vlm-lora-v2
YKS-VLM-LoRA-v2
YKS-VLM-LoRA-v2 is a LoRA fine-tuned Vision-Language Model built on top of
Qwen2.5-VL-32B-Instruct, optimized for Turkish exam-style math questions (YKS).
This model is designed as a vision-to-structured-output component rather than a full end-to-end solver.
check us out: cga-labs.com
What this model is good at
- Reading math questions from images
- Understanding exam-style layouts (options, figures, tables)
- Producing stable, structured JSON outputs
- Acting as a preprocessing / parsing layer for downstream solvers
Typical output format:
{
"final_answer": "C"
}
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Model tree for cgalabs/yks-vlm-lora-v2
Base model
Qwen/Qwen2.5-VL-32B-Instruct