SmolVLM2-500M-vi-stage1

SmolVLM2-500M-vi-stage1

Vietnamese stage-1 adaptation of SmolVLM2-500M-Video-Instruct. The tokenizer was extended with 8,064 Vietnamese sub-word tokens (vocab 49,280 → 57,344) and the model was trained on a Vietnamese image–text mixture. This is a standalone merged model — the LoRA adapter and the newly-trained token embeddings have been folded into the base weights, so it loads directly with AutoModelForImageTextToText.from_pretrained with no adapter step.

What was trained

• Tokenizer expansion: 8,064 new Vietnamese BPE tokens (IDs 49,280–57,343), mean-initialized, cutting Vietnamese fertility (tokens/word) substantially.
• Recipe: LoRA (r=32, α=64) on attention + MLP projections, plus only the 8,064 new token rows of embed_tokens and lm_head trained via PEFT trainable_token_indices (base vocabulary frozen). This avoids the base-vocab forgetting / mode collapse that fully training the embeddings caused in an earlier run.
• Schedule: 2 epochs, effective batch 48 (4 × 3 GPU × 4 grad-accum), LR 1e-4 cosine, bf16, 1536px images.

Training data

Vietnamese multimodal + a slice of English, roughly:

Evaluation

Base vs. this model, 200 samples/task (exact_match/token_f1 for VQA; bleu/rougeL for captioning):

The main, genuine gains are in Vietnamese image captioning (UIT-ViIC rougeL roughly doubles) and modest OCR-VQA improvement. The OpenViVQA token_f1 number drops, but note the base score there is inflated by the base model echoing the question back (which overlaps reference tokens); both models actually struggle with this open-ended scene-text VQA task at 500M scale.

Usage

import torch
from transformers import AutoModelForImageTextToText, AutoProcessor
from PIL import Image

repo = "thuanan/SmolVLM2-500M-vi-stage1"
processor = AutoProcessor.from_pretrained(repo)
model = AutoModelForImageTextToText.from_pretrained(
    repo, torch_dtype=torch.bfloat16
).to("cuda").eval()

image = Image.open("your_image.jpg").convert("RGB")
messages = [{"role": "user", "content": [
    {"type": "image"},
    {"type": "text", "text": "Mô tả bức ảnh này."},
]}]
prompt = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
inputs = processor(text=prompt, images=[image], return_tensors="pt").to("cuda", torch.bfloat16)
out = model.generate(**inputs, max_new_tokens=64)
print(processor.batch_decode(out, skip_special_tokens=True)[0])

Limitations

• Stage-1 only: best at captioning; open-ended Vietnamese VQA (esp. scene-text / OCR reasoning) remains weak — expected for a 500M model.
• Occasional spelling artifacts on rare Vietnamese tokens (under-trained embeddings), most visible on OpenViVQA-style questions.
• English capability is partially preserved (anti-forgetting data) but not benchmarked here.

License

Apache-2.0, inherited from the base SmolVLM2 model.