Instructions to use oddadmix/Nawah-Math-Reasoning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use oddadmix/Nawah-Math-Reasoning with Transformers:

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

pipe = pipeline("text-generation", model="oddadmix/Nawah-Math-Reasoning")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("oddadmix/Nawah-Math-Reasoning")
model = AutoModelForCausalLM.from_pretrained("oddadmix/Nawah-Math-Reasoning", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use oddadmix/Nawah-Math-Reasoning with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "oddadmix/Nawah-Math-Reasoning"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "oddadmix/Nawah-Math-Reasoning",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/oddadmix/Nawah-Math-Reasoning

SGLang

How to use oddadmix/Nawah-Math-Reasoning 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-Math-Reasoning" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "oddadmix/Nawah-Math-Reasoning",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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-Math-Reasoning" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "oddadmix/Nawah-Math-Reasoning",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use oddadmix/Nawah-Math-Reasoning with Docker Model Runner:
```
docker model run hf.co/oddadmix/Nawah-Math-Reasoning
```

Nawah-Math-Reasoning

File size: 3,556 Bytes

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---
title: Nawah Math Reasoning
emoji: 🧠
colorFrom: indigo
colorTo: purple
sdk: gradio
sdk_version: 6.26.0
python_version: '3.12'
app_file: app.py
pinned: false
models:
- oddadmix/Nawah-Math-Reasoning
datasets:
- oddadmix/arabic-math-reasoning-synth
- oddadmix/gsm8k-reasoning-ar
short_description: نموذج استدلال رياضي عربي (52M) يفكّر خطوة بخطوة قبل الإجابة
---

# Nawah-Math-Reasoning — Demo

A **51.8M-parameter** Arabic math reasoning model. It writes its derivation inside
`<think>…</think>` and then gives the final answer; the demo splits the two live as it streams —
the reasoning trace in one panel, the answer in the other.

Fine-tuned from [`oddadmix/50M-2048-Emhotob`](https://huggingface.co/oddadmix/50M-2048-Emhotob),
a Llama-architecture base pre-trained from scratch on ~20B Arabic tokens (12 layers, hidden 512,
2048 context).

> **بالعربية:** نموذج عربي صغير (~52 مليون معامل) يكتب خطوات تفكيره داخل وسم `<think>` ثم يعطي
> الإجابة النهائية. الديمو بيفصل الاتنين وانت بتتفرج على النموذج وهو بيكتب.

## Everything is open — Apache 2.0

| | |
|---|---|
| 🧠 **Model** | [`oddadmix/Nawah-Math-Reasoning`](https://huggingface.co/oddadmix/Nawah-Math-Reasoning) |
| 🛠️ **Training code** | [`code/`](https://huggingface.co/oddadmix/Nawah-Math-Reasoning/tree/main/code) — data generation, translation, SFT, eval, GRPO |
| 📚 **Synthetic corpus** | [`oddadmix/arabic-math-reasoning-synth`](https://huggingface.co/datasets/oddadmix/arabic-math-reasoning-synth) — 120,462 arithmetically verified rows |
| 📚 **Translated corpus** | [`oddadmix/gsm8k-reasoning-ar`](https://huggingface.co/datasets/oddadmix/gsm8k-reasoning-ar) — 142,969 rows |

## Results

Number agreement, greedy decoding, on held-out splits. Every version of the model was scored on
identical rows, so the numbers are comparable across the whole development ladder.

| eval set | n | score |
|---|---:|---:|
| GSM8K-ar | 600 | **79.0%** |
| Arabic_Reasoning | 400 | **73.0%** |
| synthetic math | 1000 | **40.4%** |
| synthetic relational | 400 | **52.2%** |

The last row is what this release adds: problems where the difficulty is the *relation* between
quantities (`ضعف`, `نصف`, `أكثر بـ…`) rather than the arithmetic. The previous version scored
34.0% there — the relation appeared in barely 1.3% of the training corpus, so 20,139 rows were
generated specifically to fill the gap.

## Limitations

A 52M proof of concept. It reliably produces the *shape* of Arabic step-by-step reasoning, but
**arithmetic errors are the dominant failure mode** — the derivation is usually structurally
right, one computation is wrong, and the model then stays faithful to its own bad number. The
40.4% and 52.2% above are the honest ceiling on multi-step problems. Single-turn only; open-ended
and non-mathematical questions are out of distribution.

نموذج تجريبي: بيعرف يمشي خطوة خطوة بالعربي، بس بيغلط في الحساب كتير.

Runs on **ZeroGPU**. The model is small enough for CPU too — switch the Space to `cpu-basic` and
it still works, just slower.

## Configuration

| Variable | Purpose |
|---|---|
| `MODEL_ID` | Model repo to load (default `oddadmix/Nawah-Math-Reasoning`) |
| `MODEL_HF_TOKEN` | Only needed if `MODEL_ID` points at a **private** repo. (`HF_TOKEN` is reserved by Spaces and does not reach the container.) |