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oddadmix
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Model Details
Model Provider
oddadmix
Model Tree
oddadmix/50M-2048-Emhotob
Input Modalities
Output Modalities
Supported Functionality
| 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) |
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:
Qwen/Qwen3.5-9B — the
teacher model that generated the persona conversations and answers.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.
The tokenizer uses the TokenizersBackend class, which requires transformers>=5.12.
There is no packaged chat template, so build the ChatML prompt manually:
python
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizermodel_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. 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.
SupraLabs/Supra-50M-Base (Apache-2.0).oddadmix/50M-2048-Emhotob — from-scratch, ~20B Arabic tokens, 2048 ctx.oddadmix/4.1-mini-18-yearsold-10K — distilled from Qwen/Qwen3.5-9B, generated with NVIDIA NeMo Data Designer.