BananaMind Translate V3.4 is trained for direct, instruction-following translation between English and German. It is optimized to output only the translated text without explanations, formatting noise, or extra commentary.

Benchmark Results

BananaMind Translate Pro Benchmark

The Pro benchmark contains harder and more varied English → German sentences than the original benchmark. It is intended to be a stricter test of general translation quality.

On this harder 500-example benchmark:

BananaMind Translate V3.4 Qwen 4B: 77.3 chrF / 48.8 BLEU / 14.0% exact match
Google Translate: 84.5 chrF / 59.7 BLEU / 27.0% exact match
DeepSeek V4 Flash: 87.0 chrF / 68.7 BLEU / 35.2% exact match

Original BananaMind Translate Benchmark

The original benchmark showed stronger BananaMind scores, but those results should be interpreted with care. The normal benchmark was somewhat inflated because its examples had a similar style to the training data. The Pro benchmark above is a harder evaluation and better reflects generalization to less familiar sentence styles.

The test set has zero overlap with the training data.

Model Details

Base model: Qwen/Qwen3.5-4B
Fine-tuning method: QLoRA, merged into the base model
LoRA rank: r=32, alpha=64
Target modules: all-linear
Quantization during training: 4-bit NF4 + double quant, bfloat16 compute
Training epochs: 1
Learning rate: 1e-4 with cosine scheduler
Optimizer: paged_adamw_32bit
Training data: Cleaned English ↔ German parallel sentence pairs

Usage

python

from transformers import AutoProcessor, AutoModelForImageTextToText
import torch

processor = AutoProcessor.from_pretrained("Banaxi-Tech/BananaMind-Translate-V3.4")
model = AutoModelForImageTextToText.from_pretrained(
    "Banaxi-Tech/BananaMind-Translate-V3.4",
    dtype=torch.bfloat16,
    device_map="auto",
)

messages = [
    {
        "role": "system",
        "content": [{"type": "text", "text": "You are a translation engine. Translate English to German. Output only the German translation."}],
    },
    {
        "role": "user",
        "content": [{"type": "text", "text": "Translate to German: Hello, how are you?"}],
    },
]

inputs = processor.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_dict=True,
    return_tensors="pt",
).to(model.device)

with torch.no_grad():
    output_ids = model.generate(**inputs, max_new_tokens=512, do_sample=False)

new_tokens = output_ids[0][inputs["input_ids"].shape[-1]:]
print(processor.decode(new_tokens, skip_special_tokens=True))
# → Hallo, wie geht es dir?

Image Translation

Warning

This model was not trained specifically for image translation. It inherits Qwen3.5's vision capabilities, and early tests suggest it can translate visible text in images, but this mode is not as thoroughly evaluated as text translation. Use with care.

python

from PIL import Image

image = Image.open("your_image.png").convert("RGB")

messages = [
    {
        "role": "system",
        "content": [{"type": "text", "text": "You are a translation engine. Translate English to German. Output only the German translation."}],
    },
    {
        "role": "user",
        "content": [
            {"type": "image", "image": image},
            {"type": "text", "text": "Read all English text visible in this image and translate it to German."},
        ],
    },
]

Limitations

Optimized for English ↔ German translation; performance on other language pairs is untested.
Fine-tuned on a specific curated parallel corpus, so performance may vary on highly specialized legal, medical, literary, or technical text.
The original benchmark was easier and partly style-matched to the training data, so the Pro benchmark should be treated as the more realistic evaluation.
Vision-based translation uses the base model's unchanged vision encoder; OCR quality depends heavily on image clarity.

Training Details

The model was trained using QLoRA on a curated parallel corpus of English–German sentence pairs. The adapter was merged into the base model weights for easier deployment, so no separate adapter files are required.

License

This model is released under the Apache 2.0 License, the same license as the base model Qwen3.5-4B.

Third-Party Notices

This model is derived from Qwen3.5-4B by Alibaba Cloud (Qwen Team).

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