Base model: google/gemma-4-31B-it (31B parameters)
Adapter: LoRA rank 16, alpha 32, targeting q_proj and v_proj
Training data: 20,000 parallel translation pairs from OPUS (100 languages, primarily paired with English)
Training: 3 epochs, 81 steps
Languages: Arabic-English, French-English, Chinese-English, and 97 more

Training Results

Metric	Before	After
Quality	2.0	5.9 (+195.0%)
Grade	E	C
Percentile	0.1	7.2
Win Rate	46%	54%

How to Use

python

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base_model = AutoModelForCausalLM.from_pretrained(
    "google/gemma-4-31B-it",
    torch_dtype="bfloat16",
    device_map="auto"
)
model = PeftModel.from_pretrained(base_model, "morningstarxcdcode/adaption-opus-100-translation-model")
tokenizer = AutoTokenizer.from_pretrained("morningstarxcdcode/adaption-opus-100-translation-model")

inputs = tokenizer("Translate to French: The weather is nice today.", return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Team

Sourav Rajak, Priyanshu Tomar, Roshan G, Vivek Rajput

Part of the AutoScientist Challenge — Healthcare, Finance, Language, Legal, and Marketing tracks.