adaption_multilingual_vqa_test

adaption_multilingual_vqa_test

Model Training

A LORA adapter for google/gemma-4-31B-it-VLM. This model was trained with SFT using Adaption's AutoScientist on the multilingual_vqa_test dataset.

AutoScientist Config

{
  "job_id": "c41cc068-7da6-494c-820d-f2ee12be08e6",
  "training_experiment_id": "6f6ddf31-899a-48f3-8bfd-abd7590148ab",
  "original_model_name": "google/gemma-4-31B-it-VLM",
  "trained_model_name": "adaption_multilingual_vqa_test",
  "training_method": "sft",
  "training_type": "lora",
  "data_format": "chat",
  "hyperparams": {
    "lora": "true",
    "lora_r": 8,
    "n_evals": 5,
    "n_epochs": 1,
    "batch_size": "max",
    "lora_alpha": 8,
    "lora_dropout": 0,
    "min_lr_ratio": 0.1,
    "warmup_ratio": 0.1,
    "weight_decay": 0,
    "learning_rate": 0.00005,
    "max_grad_norm": 2,
    "base_model_size": "31B",
    "train_on_inputs": "false",
    "training_method": "sft",
    "lr_scheduler_type": "cosine",
    "scheduler_num_cycles": 0.5,
    "lora_trainable_modules": "q_proj,v_proj"
  }
}

Training Data

The model was trained on 819 rows of adapted data with the following domain distribution: math (32%), data-analysis-visualization (15%), science (6%), language (5%), other (5%), architecture-design (5%), corporate-business (5%), fitness-sports (4%), transportation (4%), animal-nature (2%), sports (2%), cooking (2%), geography (2%), art (2%), fashion-beauty (1%), academic-education (1%), culture (1%), product-advice (1%), market-analysis (1%), music (1%), personal-growth (1%), code (0%), entertainment (0%), technology (0%), marketing (0%), governance (0%).

Model Evaluation

The model was evaluated on an in-distribution held-out test set as well as a broader domain-specific test set to measure generalization.

How to use

pip install torch transformers peft

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

BASE = "google/gemma-4-31B-it-VLM"
ADAPTER = "<this-repo-id>"

device = "cuda" if torch.cuda.is_available() else "cpu"
dtype = torch.float32 if device == "cpu" else torch.bfloat16

base = AutoModelForCausalLM.from_pretrained(BASE, dtype=dtype).to(device)
model = PeftModel.from_pretrained(base, ADAPTER)
# Optional: merge the LoRA weights into the base for faster inference
model = model.merge_and_unload()
model.eval()

tokenizer = AutoTokenizer.from_pretrained(BASE)
messages = [{"role": "user", "content": "Hello!"}]
text = tokenizer.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(device)

with torch.inference_mode():
    out = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))