Gameplay Vision LLM Adapters

Trained adapters for multimodal gameplay video understanding, designed to work with Qwen3-VL-8B-Instruct.

Contents

Usage

from peft import PeftModel
from transformers import Qwen3VLForConditionalGeneration, AutoProcessor

# Load base model
model = Qwen3VLForConditionalGeneration.from_pretrained(
    "Qwen/Qwen3-VL-8B-Instruct",
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

# Apply LoRA adapter
model = PeftModel.from_pretrained(model, "YOUR_USERNAME/gameplay-vision-llm-adapters", subfolder="lora_adapter")

# Load projector weights
projector_weights = torch.load("projector_weights.pt")

Architecture

• Base Model: Qwen3-VL-8B-Instruct
• LoRA Config: r=16, alpha=32, target_modules=[q_proj, k_proj, v_proj, o_proj]
• Projectors: Linear → GELU → Linear (input_dim → 4096)
  • SigLIP: 1152 → 4096
  • VideoMAE: 768 → 4096
  • Wav2Vec2: 1024 → 4096

Training

• Trained on gameplay Q&A pairs from visual novel/RPG footage
• Projectors trained with generative alignment (frozen LLM)
• LoRA fine-tuned on instruction-following gameplay questions

GitHub Repository

Full codebase: https://github.com/chasemetoyer/gameplay-vision-llm

License

Apache 2.0