Model Details

• Base Model: google/gemma-3-4b-it
• Architecture: Standalone (Weights fully merged)
• Language(s): Pashto (Primary), English
• Capabilities: Text Generation, Vision-Language (Image Analysis, OCR)

How to Load and Use

Because this model is a fully merged standalone architecture, you can load it directly using standard Hugging Face transformers libraries without needing any separate adapter configurations.

python
Copy code
import torch
from transformers import AutoProcessor, AutoModelForImageTextToText, BitsAndBytesConfig

# Direct repository ID
model_id = "uzairkhn/Almas-Pashto-AI"

print("Loading processor...")
processor = AutoProcessor.from_pretrained(model_id)

print("Configuring 4-bit quantization...")
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_compute_dtype=torch.bfloat16
)

print("Loading Almas Pashto AI...")
model = AutoModelForImageTextToText.from_pretrained(
    model_id,
    quantization_config=bnb_config,
    device_map="auto"
)

# Example Inference
test_prompt = "مصنوعي استخبارات څه شی دی؟"

messages = [
    {"role": "user", "content": [{"type": "text", "text": test_prompt}]}
]

text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = processor(text=[text], return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=512,
        temperature=0.7,
        do_sample=True,
        repetition_penalty=1.1
    )

generated_text = processor.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True)
print(generated_text)