Allam-7B-idiolex

IdioleX-Allam-7B-AR — Dialectally Aligned Arabic LLM

IdioleX-Allam-7B-AR is a fine-tuned version of ALLaM-7B-Instruct-preview trained with an additional IdioleX embedding alignment objective to improve dialectal fidelity across Arabic varieties.

Kantharuban et al., IDIOLEX: Unified and Continuous Representations for Idiolectal and Stylistic Variation (preprint, under review). Code: github.com/AnjaliRuban/IdioleX

Training

Standard supervised fine-tuning (SFT) cross-entropy loss is augmented with a cosine similarity alignment loss between the LLM's pooled response hidden states (projected into IdioleX space) and the IdioleX embedding of the ground-truth response:

L = L_CE + α · (1 − cosine_sim(proj(h̄), e_idiolex))

The IdioleX encoder (AnjaliRuban/idiolex-arabertv2-ar) is kept frozen throughout training. A two-layer projection head maps from the LLM's hidden dimension (4096) to the IdioleX embedding dimension (768) and is also frozen after the first epoch.

Hyperparameters

Training data

Instruction–response pairs in dialectal Arabic constructed from publicly available corpora spanning Egyptian, Moroccan, Palestinian, Saudi, and Syrian Arabic, augmented into instruction-following format using GPT-5-mini. See the paper (§6.1, Appendix B.2.3) for full data details.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained(
    "your-username/idiolex-allam-7b-ar",
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained("your-username/idiolex-allam-7b-ar")

messages = [{"role": "user", "content": "ترجم للعربية المصرية: How are you doing today?"}]
inputs = tokenizer.apply_chat_template(
    messages, return_tensors="pt", add_generation_prompt=True
).to(model.device)

with torch.no_grad():
    output = model.generate(inputs, max_new_tokens=256, temperature=0.7, top_p=0.95, do_sample=True)

print(tokenizer.decode(output[0][inputs.shape[1]:], skip_special_tokens=True))

Citation

@article{kantharuban2025idiolex,
  title   = {IDIOLEX: Unified and Continuous Representations for Idiolectal and Stylistic Variation},
  author  = {Kantharuban, Anjali and Srivastava, Aarohi and Faisal, Fahim and Ahia, Orevaoghene
             and Anastasopoulos, Antonios and Chiang, David and Tsvetkov, Yulia and Neubig, Graham},
  year    = {2025},
  note    = {Preprint, under review}
}