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youssefrekik/qwen3-8b-tunisian-insurance-task-adapter

sourceHugging Faceapache-2.0updated 10mo agoView on Hugging Face

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Model Card

Qwen3-8B Tunisian Insurance Assistant

Two-stage fine-tuned model for Tunisian insurance domain with dialect support.

Architecture

1.Dialect Adapter (Stage 1): Trained on Tunisian dialect corpus
2.Task Adapter (Stage 2): Fine-tuned on insurance FAQ data

Usage

python

from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel

# Load base model
model = AutoModelForCausalLM.from_pretrained(
    "unsloth/Qwen3-8B-unsloth-bnb-4bit",
    load_in_4bit=True,
    device_map="auto"
)

# Load tokenizer with custom tokens
tokenizer = AutoTokenizer.from_pretrained("youssefrekik/qwen3-8b-tunisian-dialect-adapter")
model.resize_token_embeddings(len(tokenizer))

# Load dialect adapter
model = PeftModel.from_pretrained(model, "youssefrekik/qwen3-8b-tunisian-dialect-adapter")

# Load task adapter
model.load_adapter("youssefrekik/qwen3-8b-tunisian-insurance-task-adapter", adapter_name="task")
model.set_adapter(["default", "task"])

# Generate
prompt = "<tunisian>Chnowa el fara9 bin RC w tous risques?</tunisian>"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Training Details

—Base Model: Qwen3-8B-4bit
—Stage 1: Dialect adaptation (r=8, 2000 steps)
—Stage 2: Task fine-tuning (r=8, 200 steps)
—Languages: Tunisian Arabic (Arabizi), Modern Standard Arabic, French, English
—Domain: Tunisian insurance

Custom Tokens

Added domain-specific tokens including:

—Language markers: <tunisian>, <arabizi>, <ar>, <fr>
—Insurance terms: <assurance>, <sinistre>, <prime>, etc.
—Tunisian expressions: <khouya>, <chnowa>, <kifach>, etc.

License

Apache 2.0