gemma-3-0.7b-vlm-custom

Gemma3-270m-VLM (Pi0.6)

A Vision-Language Model combining:

• Vision Tower: SigLIP from google/gemma-3-4b-pt (417M params)
• Multi-modal Projector: Randomly initialized (739K params)
• Language Model: google/gemma-3-270m (268M params)

Total: 686M parameters

Architecture

• Vision hidden size: 1152
• LLM hidden size: 640
• Vocab size: 262,208 (includes 64 image tokens)
• Image token index: 262,144

Usage

With LLaMAFactory

llamafactory-cli train \
    --stage sft \
    --model_name_or_path models/gemma3-270m-vlm-with-weights \
    --template gemma3 \
    --dataset mllm_demo \
    --freeze_vision_tower True \
    --freeze_multi_modal_projector True \
    --bf16 True \
    ...

With Transformers

from transformers import AutoModelForImageTextToText, AutoProcessor

model = AutoModelForImageTextToText.from_pretrained(
    "models/gemma3-270m-vlm-with-weights",
    torch_dtype="bfloat16"
)
processor = AutoProcessor.from_pretrained("models/gemma3-270m-vlm-with-weights")

Training Recommendations

1. Freeze vision tower and projector initially to train only the LLM
2. Use small learning rate (e.g., 5e-5 or 1e-4)
3. Gradually unfreeze projector after LLM converges
4. Vision tower can remain frozen if using pretrained vision encoder

Notes

• Multi-modal projector is randomly initialized and needs training
• The model uses Gemma3 tokenizer with 262,144 base tokens + 64 image tokens
• Compatible with all Gemma3 features (sliding window attention, etc.)