license, datasets, language, base_model, pipeline_tag, tags

license	datasets	language	base_model	pipeline_tag	tags
apache-2.0	24-mohamedyehia/gloss2text-Ar-sft	ar	google/gemma-3-270m-it	text-generation	sign-language arabic ArSL gloss-to-text gemma-3 accessibility

Gloss2Text-V1-Gemma3-270M (Merged)

Overview

Gloss2Text-V1-Gemma3-270M is a fine-tuned version of Google's Gemma-3-270m-it, optimized for Arabic Sign Language (ArSL) gloss-to-text translation.

This release is merged, which means the LoRA adapters have already been fused into the base model weights for faster inference and easier plug-and-play usage.

Model Description

• Task: Converts a sequence of Arabic glosses into a natural, grammatically correct Modern Standard Arabic (MSA) sentence.
• Input format: Arabic gloss text, for example: "أنا شرب ماء الآن"
• Output format: Clean MSA text, for example: "أنا أشرب الماء الآن."
• Architecture: Gemma-3, 270M parameters
• Training method: Supervised fine-tuning with LoRA (rank 64), then merged

Training Highlights

The model was trained on a specialized dataset containing diverse ArSL gloss-sentence pairs.

• Final eval loss: ~0.34
• Precision: Trained with bf16 for improved numerical stability

How to Use

Because this is a merged model, you can load it directly with the transformers library without needing peft:

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "24-mohamedyehia/Gloss2Text-V1-Gemma3-270M"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

def translate_gloss(gloss_text):
    prompt = f"Translate ArSL gloss to an MSA sentence.\nGloss: {gloss_text}\nOutput: "
    inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
    outputs = model.generate(**inputs, max_new_tokens=50)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)


# Example
print(translate_gloss("أنا ذهاب صيدلية"))

Developer

Developed by Mohamed Yehia.

• LinkedIn: Mohamed Yehia