---
language:
- ar
- en
license: apache-2.0
tags:
- egyptian-arabic
- arabic
- causal-lm
- continual-pretraining
- instruction-tuning
- dialect
- qwen3
pipeline_tag: text-generation
base_model: Qwen/Qwen3-1.7B-Base
datasets:
- MBZUAI-Paris/Egyptian-SFT-Mixture
- UBC-NLP/nilechat-fw-edu-egy
model-index:
- name: Fattah-2.5B
results:
- task:
type: text-generation
dataset:
name: EgyptianMMLU
type: MBZUAI-Paris/EgyptianMMLU
metrics:
- type: accuracy
value: 38.4
name: EgyptianMMLU (acc)
- task:
type: text-generation
dataset:
name: EgyptianPIQA
type: MBZUAI-Paris/EgyptianPIQA
metrics:
- type: accuracy
value: 61.3
name: EgyptianPIQA (acc)
- task:
type: text-generation
dataset:
name: Belebele-Arz
type: facebook/belebele
metrics:
- type: accuracy
value: 40.78
name: Belebele-Arz (acc)
- task:
type: text-generation
dataset:
name: EgyptianHellaSwag
type: MBZUAI-Paris/EgyptianHellaSwag
metrics:
- type: accuracy
value: 24.0
name: EgyptianHellaSwag (acc_norm)
- task:
type: text-generation
dataset:
name: EgyptianWinoGrande
type: MBZUAI-Paris/EgyptianWinoGrande
metrics:
- type: accuracy
value: 49.4
name: EgyptianWinoGrande (acc)
- task:
type: text-generation
dataset:
name: EgyptianOpenBookQA
type: MBZUAI-Paris/EgyptianOpenBookQA
metrics:
- type: accuracy
value: 27.96
name: EgyptianOpenBookQA (acc)
---
# فتاح — Fattah-2.5B
### نموذج لغوي مصري مبني على Qwen3 بتقنية Depth-Up Scaling
**Egyptian Arabic LLM Built on Qwen3 with Depth-Up Scaling**
[](https://opensource.org/licenses/Apache-2.0)
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---
## Overview
**Fattah** (فتاح — meaning "the opener" or "the one who opens doors") is a 2.5B parameter Large Language Model specialized for **Egyptian Arabic**, the most widely spoken Arabic dialect with over 100 million native speakers.
Fattah is built through a novel three-stage pipeline:
1. **Depth-Up Scaling (DUS)** — expanding Qwen3-1.7B from 28 to 40 transformer layers
2. **Continual Pre-Training (CPT)** — trained on a ~8.59B token Egyptian Arabic corpus, processing 5.51B tokens (64.1% of the full dataset)
3. **Supervised Fine-Tuning (SFT)** — 400K Egyptian Arabic instruction-response pairs
> ⚠️ **Note:** This is the **pre-DPO** version (CPT + SFT only). A DPO-aligned version (`Fattah-2.5B-v2`) is coming soon with improved factual accuracy, reduced hallucination, and better instruction following.
---
## Model Details
| Property | Value |
|---|---|
| **Model Name** | Fattah-2.5B |
| **Base Model** | Qwen/Qwen3-1.7B-Base |
| **Architecture** | Qwen3 (expanded via DUS) |
| **Parameters** | 2,635,771,904 (~2.64B) |
| **Transformer Layers** | 40 (expanded from 28) |
| **Hidden Size** | 2048 |
| **Context Length** | 64K tokens (YaRN extended) |
| **Language** | Egyptian Arabic (primary), MSA, English |
| **License** | Apache 2.0 |
| **Training Compute** | 2× NVIDIA A6000 48GB |
---
## Training Pipeline
### Stage 1 — Depth-Up Scaling (DUS)
Starting from `Qwen/Qwen3-1.7B-Base`, we applied **Depth-Up Scaling** surgery — the same technique used in SOLAR-10.7B — to expand the model from 28 to 40 transformer layers, increasing parameter count from 1.7B to ~2.5B without any training.
```
Qwen3-1.7B-Base (28 layers)
↓ DUS Surgery
Fattah-DUS (40 layers, ~2.5B)
```
Layer expansion strategy: concatenate layers `[0-23]` + layers `[4-27]`, creating a deeper model that inherits the base model's knowledge while providing additional capacity for Egyptian Arabic adaptation.
### Stage 2 — Continual Pre-Training (CPT)
| Parameter | Value |
|---|---|
| Dataset | Custom Egyptian Arabic corpus (~8.59B tokens) |
| Total dataset tokens | **~8.59B tokens** |
| Tokens processed | **5.51B tokens** (64.1% of dataset) |
| Training steps | 42,000 |
| Learning rate | 1e-5 (cosine decay) |
| Sequence length | 4096 |
| Batch size | 2 per GPU × 8 grad accum × 2 GPUs = 131,072 tokens/step |
| Framework | ms-swift + DeepSpeed ZeRO-1 |
| Final loss | 1.824 |
**Dataset composition:**
- 51.7% Egyptian Arabic (web, subtitles, social media, educational)
- 22.1% Modern Standard Arabic (MSA)
- 13.8% English
- 12.4% Code
### Stage 3 — Supervised Fine-Tuning (SFT)
| Parameter | Value |
|---|---|
| Dataset | `MBZUAI-Paris/Egyptian-SFT-Mixture` (400K samples) |
| Epochs | 2 |
| Learning rate | 5e-6 (cosine decay) |
| Final eval loss | **1.668** |
| Final token accuracy | **67.01%** |
| Training time | ~19 hours |
### Context Extension — YaRN
After SFT, the context window was extended from 32K to **64K tokens** using YaRN (Yet another RoPE extensioN):
```json
"rope_scaling": {
"rope_type": "yarn",
"factor": 2.0,
"original_max_position_embeddings": 32768
}
```
---
## Evaluation Results
All evaluations use **zero-shot log-likelihood scoring** (same methodology as NileChat paper). HellaSwag uses length-normalized accuracy (`acc_norm`); all other benchmarks use unnormalized accuracy (`acc`).
### Arabic Script Benchmarks — Full Comparison
All evaluations use **zero-shot log-likelihood scoring**. HellaSwag uses `acc_norm` (length-normalized accuracy). All other benchmarks use `acc` (unnormalized accuracy).
Published baselines are from the **NileChat paper (Table 1)**. Fattah rows use our custom evaluation harness with identical zero-shot methodology.
## Arabic Script Benchmarks
| Model | Params | MMLU | Belebele | HellaSwag† | PIQA | WinoGrande | OpenBookQA | **Avg** |
|---|---|---:|---:|---:|---:|---:|---:|---:|
| Nile-Chat-12B | 12B | 62.59 | 70.69 | 64.04 | 63.53 | 42.06 | 53.13 | **59.34** |
| gemma-3-12b-it | 12B | 61.55 | 77.00 | 49.49 | 63.53 | 38.03 | 48.86 | **56.41** |
| Qwen2.5-14B-Instruct | 14B | 60.81 | 72.33 | 55.84 | 59.97 | 38.26 | 50.28 | **56.25** |
| Nile-Chat-3x4B-A6B | MoE | 52.13 | 75.44 | 59.30 | 57.91 | 41.16 | 48.39 | **55.72** |
| Nile-Chat-2x4B-A6B | MoE | 52.05 | 73.89 | 59.69 | 62.26 | 41.61 | 44.07 | **55.60** |
| AceGPT-v2-8b-chat | 8B | 55.25 | 73.33 | 53.14 | 58.39 | 39.82 | 47.16 | **54.52** |
| Nile-Chat-4B | 4B | 50.25 | 68.56 | 55.92 | 61.87 | 40.94 | 46.02 | **53.93** |
| c4ai-command-r7b | 7B | 70.67 | 61.84 | 50.39 | 57.20 | 36.91 | 46.02 | **53.84** |
| ALLaM-7B-Instruct | 7B | 67.67 | 66.10 | 57.29 | 62.18 | 40.04 | 67.10 | **60.06** |
| gemma-2-9b-it | 9B | 49.44 | 61.35 | 49.53 | 61.79 | 35.79 | 48.01 | **50.99** |
| jais-adapted-13b-chat | 13B | 50.03 | 65.33 | 47.53 | 56.72 | 37.14 | 41.76 | **49.75** |
| jais-family-13b-chat | 13B | 44.85 | 66.33 | 52.99 | 57.91 | 36.91 | 38.64 | **49.61** |
| jais-family-6p7b-chat | 7B | 42.60 | 57.33 | 49.18 | 62.23 | 33.33 | 37.50 | **47.03** |
| gemma-3-4b-it | 4B | 38.56 | 60.32 | 42.56 | 56.49 | 35.79 | 46.73 | **46.74** |
| Qwen2.5-7B-Instruct | 7B | 64.22 | 58.02 | 45.47 | 56.41 | 38.70 | 11.34 | **45.69** |
| jais-adapted-7b-chat | 7B | 40.96 | 55.67 | 40.85 | 56.50 | 32.89 | 42.33 | **44.87** |
| Llama-3.1-8B-Instruct | 8B | 55.89 | 57.97 | 43.10 | 54.27 | 35.57 | 9.06 | **42.64** |
| | | | | | | | | |
| **Fattah-2.5B (post-SFT)** ⭐ | **2.5B** | **38.40** | **40.78** | **24.00** | **61.30** | **49.40** | **27.96** | **40.31** |
† HellaSwag uses `acc_norm` (length-normalized accuracy). All other benchmarks use `acc`.
‡ Published baselines are from the NileChat paper (Table 1) — these are instruction-tuned + RLHF-aligned models.
⭐ Best Fattah checkpoint (pre-DPO).
## Key Highlights
- **PIQA (61.3%)** — Fattah outperforms Qwen2.5-7B (56.4%), gemma-3-4b (56.5%), Llama-3.1-8B (54.3%), and all jais models despite being 2.5B
- **WinoGrande (49.4%)** — Fattah scores higher than every published baseline in the table, including models 3–5× larger
- **Average gap** — Fattah post-SFT (40.31%) is behind Nile-Chat-4B (53.93%) by 13.6 points; DPO alignment is expected to close this gap significantly
- **Comparable baselines** — most fair comparison is with gemma-3-4b-it (4B, 46.74%) — Fattah is 2.5B and pre-DPO, 6.4 points behind a fully aligned 4B model
### Full Training Journey (Base → DUS → CPT → SFT)
| Benchmark | Base 1.7B | DUS 2.5B | Post-CPT | **Post-SFT** | Net (Base→SFT) |
|---|---|---|---|---|---|
| EgyptianMMLU | 34.07% | 29.20% | 37.07% | **38.40%** | **+4.33%** ✅ |
| EgyptianPIQA | 54.80% | 51.90% | 61.10% | **61.30%** | **+6.50%** ✅ |
| Belebele-Arz | 37.00% | 32.78% | 41.56% | **40.78%** | **+3.78%** ✅ |
| EgyHellaSwag | 25.00% | 23.60% | 21.40% | **24.00%** | **−1.00%** ⚠️ |
| WinoGrande | 49.40% | 49.40% | 49.40% | **49.40%** | **0.00%** ➡️ |
| OpenBookQA | 21.03% | 17.67% | 27.74% | **27.96%** | **+6.93%** ✅ |
| **Average** | **36.88%** | **34.09%** | **39.71%** | **40.31%** | **+3.43%** ✅ |
| EGY Perplexity | 18.84 | 46.31 | **6.69** | — | **−12.15** ✅ |
Key observations:
- DUS surgery caused an expected temporary regression (34.09%) as the new layers were randomly initialized
- CPT recovered and surpassed the base (39.71%), acquiring strong Egyptian Arabic dialect knowledge
- SFT further improved average to **40.31%**, with MMLU +1.33% and HellaSwag recovering from 21.4% → 24.0%
- EGY Perplexity improvement of ×2.8 (18.84 → 6.69) confirms deep dialect acquisition during CPT
---
## Usage
### Installation
```bash
pip install transformers>=4.51.0 torch accelerate
```
### Basic Chat
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_name = "belal212/Fattah-2.5B-preview"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.bfloat16,
device_map="auto"
)
messages = [
{
"role": "system",
"content": "أنت فتاح، مساعد ذكي ومفيد بتتكلم العربي المصري."
},
{
"role": "user",
"content": "كلمني عن القاهرة"
}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=False # disable thinking mode for conversational use
)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
with torch.no_grad():
outputs = model.generate(
**inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9,
repetition_penalty=1.1,
pad_token_id=tokenizer.eos_token_id
)
response = tokenizer.decode(
outputs[0][inputs["input_ids"].shape[1]:],
skip_special_tokens=True
)
print(response)
```
### With Thinking Mode (for complex reasoning)
```python
messages = [
{
"role": "system",
"content": "أنت فتاح، مساعد ذكي بتفكر خطوة بخطوة قبل ما تجاوب."
},
{
"role": "user",
"content": "ازاي أحسن خوارزمية للـ sorting في Python؟"
}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=True # activate mode
)
```
---
## Intended Use
Fattah is designed for:
- ✅ Egyptian Arabic conversational AI
- ✅ Question answering in Egyptian dialect
- ✅ Text generation and creative writing in Egyptian Arabic
- ✅ RAG-based knowledge retrieval systems
- ✅ Foundation for Fattah-Coding (Python + React/TS specialist — coming soon)
- ✅ Agent systems requiring Egyptian Arabic understanding
---
## Limitations
- **Factual hallucination**: As a 2.5B model without DPO alignment, Fattah may confidently generate incorrect facts. A DPO-aligned version is in development.
- **Knowledge cutoff**: Training data has a knowledge cutoff. Recent events are not known.
- **Dialect coverage**: Optimized for Egyptian Arabic. Performance on other Arabic dialects is not guaranteed.
- **Model size**: At 2.5B parameters, Fattah cannot match the factual depth of larger models. Use RAG for knowledge-intensive applications.
- **Pre-DPO**: This version has not undergone preference optimization. Responses may occasionally be over-cautious or inconsistent in style.
---
## Roadmap
| Version | Status | Description |
|---|---|---|
| Fattah-2.5B | ✅ Released | CPT + SFT, Egyptian Arabic assistant |
| Fattah-2.5B-v2 | 🔄 In progress | + DPO alignment (Egyptian-DPO-Mixture) |
| Fattah-Python-2.5B | ⏳ Planned | Fattah + Python/AI coding specialization |
| Fattah-React-2.5B | ⏳ Planned | Fattah + React/TypeScript specialization |
| Fattah-Coding-MoE | ⏳ Planned | MoE with LLM-gated routing between Python + React experts |
---
## Training Infrastructure
- **GPUs**: 2× NVIDIA A6000 48GB
- **Framework**: [ms-swift](https://github.com/modelscope/ms-swift) 4.0.2
- **Distributed**: DeepSpeed ZeRO Stage 1
- **Attention**: Flash Attention 2.3.6
- **Mixed precision**: bfloat16
- **Total compute**: ~60 GPU-hours (CPT) + ~19 GPU-hours (SFT)
---
## Citation
If you use Fattah in your research, please cite:
```bibtex
@misc{fattah2026,
title = {Fattah: Egyptian Arabic LLM via Depth-Up Scaling and Continual Pre-Training},
author = {Belal},
year = {2026},
publisher = {HuggingFace},
howpublished = {\url{https://huggingface.co/belal212/Fattah-2.5B-preview}},
note = {Pre-DPO version}
}
```
---
## Acknowledgements
- [Qwen Team](https://huggingface.co/Qwen) for the Qwen3-1.7B-Base model
- [MBZUAI-Paris](https://huggingface.co/MBZUAI-Paris) for the Egyptian-SFT-Mixture dataset and NileChat benchmarks
- [UBC-NLP](https://huggingface.co/UBC-NLP) for the NileChat pre-training corpus
- [ms-swift](https://github.com/modelscope/ms-swift) for the training framework
---
فتاح — بيفتح أبواب الذكاء الاصطناعي للعربي المصري
Fattah — Opening the doors of AI for Egyptian Arabic speakers