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cjm249/
gameplay-vision-llm-adapters

Name: gameplay-vision-llm-adapters
Availability: InStock

Qwen/Qwen3-VL-8B-Instruct

adapter

apache-2.0

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Gameplay Vision LLM Adapters

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

File	Description	Size
`projector_weights.pt`	Multimodal projectors (SigLIP, VideoMAE, Wav2Vec2 → 4096-dim)	~120MB
`lora_adapter/`	LoRA adapter fine-tuned on gameplay Q&A	~50MB

Usage

python

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

cjm249/gameplay-vision-llm-adapters