Base Model: unsloth/Qwen3-VL-8B-Instruct-unsloth-bnb-4bit Training Method: GRPO (Group Relative Policy Optimization) Task: Grid-based component localization (20 epochs, 460 steps)

Training Details

Training Framework

Method: GRPO with Unsloth
LoRA Configuration:
- Rank (r): 16
- Alpha: 16
- Dropout: 0
- Target Modules: Attention and MLP layers

Training Data

Dataset: SLD component detection dataset
Format: Component bounding boxes with metadata
Components: Electrical panels (TSS, PSU-P, PP-series) with voltage and ampere ratings

Training Parameters

Group Size: 8 trajectories per example
Batch Size: 2
Learning Rate: 5e-6
Temperature: 0.7
Reward Functions:
- IoU with ground truth
- Efficiency (fewer steps)
- Centering (component in crop center)

Usage

python

from transformers import AutoModelForCausalLM, AutoProcessor
import torch

# Load model
model = AutoModelForCausalLM.from_pretrained(
    "qwen3_vl_8b_grpo_agent_e20",
    device_map="auto",
    torch_dtype=torch.float16,
    trust_remote_code=True
)

processor = AutoProcessor.from_pretrained("qwen3_vl_8b_grpo_agent_e20", trust_remote_code=True)

# Prepare inputs
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "image": "path/to/sld_diagram.png"},
            {"type": "text", "text": "Locate the component TSS in this diagram"}
        ]
    }
]

text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = processor(text=text, images=image, return_tensors="pt").to(model.device)

# Generate
outputs = model.generate(**inputs, max_new_tokens=512)
result = processor.decode(outputs[0], skip_special_tokens=True)
print(result)

Model Performance

This model was trained using GRPO to optimize for:

Accurate bounding box prediction (IoU score)
Efficient component search (minimal steps)
Centered component detection (component in crop center)

Limitations

Trained specifically on electrical SLD diagrams
Best performance on components similar to training data
Requires high-resolution input images for accurate detection

Citation

bibtex

@misc{qwen3_vl_grpo_sld,
  title = {qwen3_vl_8b_grpo_agent_e20},
  author = {SLD Training Team},
  year = {2024},
  note = {GRPO-trained Qwen3-VL-8B for SLD component detection}
}

License

Apache 2.0

pavan01729/qwen3_vl_8b_grpo_agent_e20

qwen3_vl_8b_grpo_agent_e20

Model Description