🇱🇧 Lebanese-Llama-3.1-8B

Lebanese-Llama-3.1-8B is a high-performance LLM fine-tuned specifically for the Lebanese dialect (Ammiya). It bridges the gap between Modern Standard Arabic (MSA) and the multi-modal nature of Lebanese communication, seamlessly blending Arabic script, French/English influences, and Arabizi (Romanized Arabic with numbers).

This model was trained and validated on the NVIDIA DGX Spark, the world’s first personal AI supercomputer powered by the Grace Blackwell (GB10) architecture.

🎮 Live Demo

Try the model instantly in your browser without any setup: 👉 Lebanese Llama Chat Demo

🎭 The Lebanese Persona

To get the most authentic "Ammiya" experience, use this system prompt. It activates the model's specialized cultural knowledge and linguistic patterns.

System Prompt:

"You are an authentic Lebanese AI assistant. Speak strictly in the Lebanese dialect (Ammiya). Match the script the user writes in: if they write Arabizi (Latin letters with digits like 3, 7, 2), answer in Arabizi; if they write Arabic script, answer in Arabic script. Use expressions like 'Yalla', 'Walaw', 'Mni7', and 'Ya zalame'. Example: 'Kifak ya m3allem? Shou el akhbar?'"

Example Comparison

• User: "Kifak? Khabbirni kif l wade3 bi Lebnen l yom bi kel sra7a."
• Lebanese-Llama: "Ya zalame, l wade3 de7ek mtl kel marra. Kelshi mni7, hamdellah."

🚀 Model Features

• Dialectal Authenticity: Fine-tuned to recognize and generate Lebanese syntax, specifically the "B" prefix for verbs and regional idioms (e.g., mni7, shou fi ma fi).
• Arabizi Mastery: Expertly handles Romanized Lebanese using numbers (e.g., 3 for 'ayn, 7 for ha, 2 for hamza) — both directions, Arabizi ↔ Arabic script.
• Blackwell Optimized: Merged into 16-bit (Bfloat16) to leverage the 5th Gen Tensor Cores and 128GB Unified Memory of the DGX Spark.
• Cultural Nuance: Enhanced understanding of Lebanese culinary, geographic, and social context compared to base Llama-3.1.

🛠 Training Specifications

• Infrastructure: NVIDIA DGX Spark (Grace Blackwell Superchip)
• Architecture: Llama-3.1-8B-Instruct (4-bit QLoRA base)
• Method: QLoRA (Unsloth), LoRA r=128 / alpha=128, LR 5e-5, cosine schedule, 3 epochs (1,281 steps)
• Checkpoint selection: best eval loss on a held-out set (1.453 at step 800) — best, not last, weights shipped

Training data (6,825 examples, validated)

Validation: empty outputs dropped, SHA1 dedup, instruction templates rotated per source, French/English code-switching preserved.

📊 Evaluation

Held-out set: 371 Alexandria LB conversations never seen in training (50 sampled for generation checks).

Version history. v1 trained 300 steps (~4,800 sequences) over ~5.7K examples, 11% of which had empty targets from a data-merge keying bug. v2 fixed the merge and doubled the data — but its 4,110 FineWeb2 rows were sampled with zero dialect filtering and turned out to be dominated by spam and vulgar web junk, which poisoned open-ended generation. v3 drops FineWeb2 entirely and retrains on the remaining 6,825 validated Lebanese/Levantine examples; it matches v2 on held-out metrics while producing genuinely Lebanese output in open chat.

💻 Usage & Implementation

Because this model was developed on the sm_121a architecture, it is best loaded using the "Stable Path" to avoid Triton compiler conflicts.

Standard Inference (Hugging Face Transformers)

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "assix-research/lebanese-llama-3.1-8b"
tokenizer = AutoTokenizer.from_pretrained(model_id)

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

# Recommended System Prompt for a Native Persona
system_prompt = (
    "You are an authentic Lebanese AI assistant. Speak strictly in the Lebanese dialect (Ammiya). "
    "Match the script the user writes in: Arabizi in, Arabizi out; Arabic script in, Arabic script out."
)

messages = [
    {"role": "system", "content": system_prompt},
    {"role": "user", "content": "Marhaba! Kifak el yom? Khabbirni shway shou fi ma fi."}
]

inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to("cuda")
outputs = model.generate(inputs, max_new_tokens=150, temperature=0.7)
# Slice the output to remove the prompt and only show the assistant's reply
print(tokenizer.decode(outputs[0][inputs.shape[1]:], skip_special_tokens=True))

💡 Prompting Tips

To get the most out of Lebanese-Llama-3.1-8B, you can "steer" the model's output script and tone by adjusting your system prompt.

🔠 Script Control

🎭 Tone & Slang

Because the model was trained on the DGX Spark with a focus on dialectal authenticity, it responds well to specific slang triggers:

• Casual: Add "Use slang like 'Ya zalame' or 'ya m3allem'."
• Helpful: Add "You are a friendly Lebanese cousin helping a relative."
• Direct: Add "Be short and snappy, like a WhatsApp message."

🛠 Troubleshooting for Developers

Handling "Echoing"

When using the transformers library, the model may return your prompt along with its answer. Always slice your output tensor to get the clean Lebanese response:

# Use the input length to slice the output
response = tokenizer.decode(outputs[0][inputs.shape[1]:], skip_special_tokens=True)

⚠️ Known Limitations & Blackwell Optimization

Triton Compatibility: If running on Blackwell hardware (sm_121a), you may encounter a ptxas fatal: Value 'sm_121a' is not defined error when using custom kernels. To fix this, use the standard PyTorch RMS Norm fallback:

import unsloth.kernels.rms_layernorm
unsloth.kernels.rms_layernorm.fast_rms_layernorm = torch.nn.functional.rms_norm

🤝 Contribution & Acknowledgements

Developed by assix on the DGX Spark infrastructure. Special thanks to the creators of AladdinBench, the Alexandria corpus (UBC-NLP), and ArabicDialectCorpus.

License: MIT (note: the Alexandria corpus is CC-BY-NC-4.0)