Instructions to use oddadmix/Nawah-50M-Egyptian-18y-Persona with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use oddadmix/Nawah-50M-Egyptian-18y-Persona with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="oddadmix/Nawah-50M-Egyptian-18y-Persona")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("oddadmix/Nawah-50M-Egyptian-18y-Persona")
model = AutoModelForCausalLM.from_pretrained("oddadmix/Nawah-50M-Egyptian-18y-Persona", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use oddadmix/Nawah-50M-Egyptian-18y-Persona with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "oddadmix/Nawah-50M-Egyptian-18y-Persona"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "oddadmix/Nawah-50M-Egyptian-18y-Persona",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/oddadmix/Nawah-50M-Egyptian-18y-Persona

SGLang

How to use oddadmix/Nawah-50M-Egyptian-18y-Persona with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "oddadmix/Nawah-50M-Egyptian-18y-Persona" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "oddadmix/Nawah-50M-Egyptian-18y-Persona",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "oddadmix/Nawah-50M-Egyptian-18y-Persona" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "oddadmix/Nawah-50M-Egyptian-18y-Persona",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use oddadmix/Nawah-50M-Egyptian-18y-Persona with Docker Model Runner:
```
docker model run hf.co/oddadmix/Nawah-50M-Egyptian-18y-Persona
```

Nawah-50M — Egyptian 18-Year-Old Persona (نواة)

Nawah (نواة, "nucleus/seed") is a ~51.8M-parameter Arabic small language model fine-tuned to speak with the voice of an 18-year-old Egyptian persona. It is a proof-of-concept for training capable Arabic models from scratch at tiny scale — small enough to run on a CPU, yet coherent in Modern Standard Arabic with an Egyptian cultural frame.

Base model: oddadmix/50M-2048-Emhotob — a Llama-architecture model pre-trained from scratch on ~20B Arabic tokens with a 2048-token context window.
Fine-tuning data: oddadmix/4.1-mini-18-yearsold-10K — 9.8K persona-grounded Arabic examples (see Training data).
Architecture & recipe: derived from SupraLabs/Supra-50M-Base. This project is a POC for training Arabic tiny models from scratch.

الملخص بالعربية: «نواة» نموذج لغوي عربي صغير (~51.8 مليون معامل) مبني على معمارية Llama ومدرَّب من الصفر، ثم ضُبط ليتحدث بشخصية شاب مصري في الثامنة عشرة من عمره. نموذج تجريبي (Proof of Concept) لإثبات إمكانية تدريب نماذج عربية مفيدة على نطاق صغير جدًا يمكن تشغيلها حتى على المعالج (CPU). التفاصيل الكاملة في الأقسام أدناه.

Model details


Parameters	~51.8M
Architecture	Llama (`LlamaForCausalLM`)
Hidden size	512 · Layers 12 · Heads 8 (GQA, 4 KV) · head_dim 64
Vocab size	32002 (32000 base + 2 ChatML control tokens)
Context length	2048
Tied embeddings	yes
Chat format	ChatML (`<
Precision	bfloat16
License	Apache-2.0 (matches the base architecture)

Training data

Nawah is fine-tuned on oddadmix/4.1-mini-18-yearsold-10K — a synthetic, persona-grounded Arabic dataset of 9,800 examples covering the life of an 18-year-old Egyptian (family dynamics, street culture, transport, school/university, social expectations, and more). Each row carries rich conditioning metadata (life_stage, domain, persona_seed, socioeconomic_tilt) and multiple output formats (factual_qa, open_ended_qa, multi_turn), each with an LLM-as-a-judge quality_evaluation score.

Provenance of the data:

Distilled from Qwen/Qwen3.5-9B — the teacher model that generated the persona conversations and answers.
Generated with NVIDIA NeMo Data Designer — NVIDIA's framework for producing high-quality synthetic data from scratch or from seeds, with dependency-aware fields, statistical samplers, and built-in LLM-as-a-judge quality scoring. The dataset's schema (persona seeds, socioeconomic tilt, per-row quality reasoning + score) reflects a Data Designer configuration.

البيانات: ضُبط النموذج على مجموعة بيانات اصطناعية مبنية على شخصية مصرية عمرها 18 عامًا (9.8 ألف مثال). البيانات مُقطَّرة من نموذج Qwen 3.5 9B وتم توليدها باستخدام NVIDIA NeMo Data Designer.

Usage

The tokenizer uses the TokenizersBackend class, which requires transformers>=5.12. There is no packaged chat template, so build the ChatML prompt manually:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "oddadmix/Nawah-50M-Egyptian-18y-Persona"
tok = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16)

def chat(user_msg, system="أنت شاب مصري في الثامنة عشرة من عمرك."):
    prompt = (
        f"<|im_start|>system\n{system}<|im_end|>\n"
        f"<|im_start|>user\n{user_msg}<|im_end|>\n"
        f"<|im_start|>assistant\n"
    )
    ids = tok(prompt, return_tensors="pt").to(model.device)
    out = model.generate(
        **ids, max_new_tokens=256, do_sample=True,
        temperature=0.7, top_p=0.9, repetition_penalty=1.2,
        eos_token_id=tok.convert_tokens_to_ids("<|im_end|>"),
    )
    return tok.decode(out[0][ids.input_ids.shape[1]:], skip_special_tokens=True)

print(chat("احكيلي عن يومك في الجامعة."))

You can also try it in the demo Spaces: Nawah-50M-Demo · Nawah-50M-Demo0ts.

Intended use & limitations

Intended use. Research and demonstration of persona-conditioned Arabic generation at tiny scale; lightweight, CPU-friendly Arabic text generation; a baseline for from-scratch Arabic SLM experiments.

Limitations. At ~50M parameters this is a proof of concept, not a production assistant. Expect limited world knowledge, weak long-form reasoning, and occasional repetition or factual errors — use sampling with a repetition_penalty. The persona and its cultural framing come from synthetic data, so outputs reflect the teacher model's and the persona seed's assumptions rather than any real individual. Do not rely on it for factual, medical, legal, or financial advice.

Citation & credits

Base architecture & training scripts: SupraLabs/Supra-50M-Base (Apache-2.0).
Base Arabic model: oddadmix/50M-2048-Emhotob — from-scratch, ~20B Arabic tokens, 2048 ctx.
Fine-tuning data: oddadmix/4.1-mini-18-yearsold-10K — distilled from Qwen/Qwen3.5-9B, generated with NVIDIA NeMo Data Designer.

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Safetensors

Model size

51.8M params

Tensor type

BF16

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