Text Generation
Transformers
Safetensors
Arabic
llama
arabic
reasoning
chain-of-thought
math
gsm8k
small-language-model
slm
sft
conversational
text-generation-inference
Instructions to use oddadmix/Nawah-Math-Reasoning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
File size: 4,970 Bytes
867d0f3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 | """
Assemble the Arabic dataset from the translation cache and validate it.
Validation per row (a translation is only kept if it passes):
* both segments translated and non-empty
* the numbers in the Arabic text match the English exactly (order-insensitive multiset)
* no degenerate repetition loop from the translator
* not left largely untranslated (Latin-script residue)
Writes out_gsm/gsm8k_reasoning_ar.parquet plus a rejects file for inspection/retry.
"""
import collections
import json
import re
import sys
from pathlib import Path
import pyarrow as pa
import pyarrow.parquet as pq
sys.path.insert(0, ".")
from gsm_common import build_record
OUT = Path("out_gsm")
AR_DIGITS = str.maketrans("٠١٢٣٤٥٦٧٨٩", "0123456789")
NUM_RE = re.compile(r"\d+(?:\.\d+)?")
LATIN_RE = re.compile(r"[A-Za-z]")
ARABIC_RE = re.compile(r"[-ۿ]")
def numbers(text):
return NUM_RE.findall(text.translate(AR_DIGITS).replace(",", ""))
def has_repetition_loop(text, n=6, times=3):
"""Detect the translator getting stuck repeating an n-word window."""
words = text.split()
if len(words) < n * times:
return False
counts = collections.Counter(
" ".join(words[i : i + n]) for i in range(len(words) - n + 1)
)
return counts.most_common(1)[0][1] >= times
VERBALISABLE_MAX = 12 # Arabic writes small quantities as words ("ضعف" for 2, "الستة" for 6)
def check(en, ar, strict=True):
"""
strict=True (reasoning chains): every numeral must survive exactly — the arithmetic depends
on it. strict=False (questions): a small number may be verbalised, but a number the source
never contained is a translation error (observed: '$13751' -> '13571', '4 × 44' -> '4 × 46'),
which silently corrupts the math and is always rejected.
"""
if not ar or not ar.strip():
return "empty"
en_n, ar_n = collections.Counter(numbers(en)), collections.Counter(numbers(ar))
if ar_n - en_n:
return "invented_number"
missing = en_n - ar_n
if missing:
if strict:
return "number_dropped"
if any(float(v) > VERBALISABLE_MAX for v in missing):
return "number_dropped"
if has_repetition_loop(ar):
return "repetition"
if not ARABIC_RE.search(ar):
return "not_arabic"
latin = len(LATIN_RE.findall(ar))
if latin > 0.25 * len(ar.replace(" ", "")):
return "latin_residue"
return None
def main():
trans = {}
with open(OUT / "translations.jsonl", encoding="utf-8") as fh:
for line in fh:
try:
r = json.loads(line)
except json.JSONDecodeError:
continue
trans[r["src"]] = r["tgt"]
print(f"[*] {len(trans)} cached translations")
rows = [json.loads(l) for l in open(OUT / "selected_rows.jsonl", encoding="utf-8")]
print(f"[*] {len(rows)} selected rows")
kept, rejects = [], []
reasons = collections.Counter()
for r in rows:
q_ar, t_ar = trans.get(r["question"]), trans.get(r["thinking"])
if q_ar is None or t_ar is None:
reasons["missing"] += 1
rejects.append({**r, "reason": "missing"})
continue
why = check(r["question"], q_ar, strict=False) or check(r["thinking"], t_ar, strict=True)
if why:
reasons[why] += 1
rejects.append({**r, "question_ar": q_ar, "thinking_ar": t_ar, "reason": why})
continue
kept.append(
{
"text": build_record(q_ar, t_ar, r["answer"]),
"question": q_ar,
"thinking": t_ar,
"answer": r["answer"],
"question_en": r["question"],
"thinking_en": r["thinking"],
"source_index": r["idx"],
}
)
print(f"[*] kept {len(kept)}/{len(rows)} ({len(kept)/len(rows):.2%})")
print(f"[*] rejects: {dict(reasons)}")
table = pa.table({k: [row[k] for row in kept] for k in kept[0]})
pq.write_table(table, OUT / "gsm8k_reasoning_ar.parquet", compression="zstd")
print(f"[+] wrote {OUT / 'gsm8k_reasoning_ar.parquet'} ({table.num_rows} rows)")
with open(OUT / "rejects.jsonl", "w", encoding="utf-8") as fh:
for r in rejects:
fh.write(json.dumps(r, ensure_ascii=False) + "\n")
print(f"[+] wrote {OUT / 'rejects.jsonl'} ({len(rejects)} rows)")
stats = {
"selected": len(rows),
"kept": len(kept),
"kept_pct": 100 * len(kept) / len(rows),
"rejects": dict(reasons),
"unique_translations": len(trans),
}
(OUT / "build_stats.json").write_text(json.dumps(stats, ensure_ascii=False, indent=2), encoding="utf-8")
for row in kept[:3]:
print("-" * 70)
print("EN:", row["question_en"][:120])
print("AR:", row["question"][:120])
print("AR think:", row["thinking"][:160])
if __name__ == "__main__":
main()
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