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 / code /finish_merge_push.sh

oddadmix

training code: data generation, SFT, eval, GRPO

867d0f3 verified 6 days ago

Raw

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1.78 kB

	#!/usr/bin/env bash
	# Merge the primary cache with every node shard, rebuild the corpus, push to the Hub.
	#
	# Run this after the primary generator exits. Idempotent: it re-merges from the two
	# caches every time, so re-running after a resumed generation just produces a larger
	# corpus. Neither node's live cache is touched — the merge lands in out_merged/.
	#
	# ./finish_merge_push.sh # build + card, no push (review, then re-run with push)
	# ./finish_merge_push.sh --push # build + card + upload
	set -euo pipefail
	cd /notebooks/50M/reasonsing
	P=/notebooks/50M/.venv-lfm2/bin/python
	REPO=oddadmix/arabic-math-reasoning-synth
	SHARD_DIR=shards_dl/shards

	# 1. merge — primary cache first, then every shard pulled from the Hub
	mkdir -p out_merged
	cat out_synth/generations.jsonl > out_merged/generations.jsonl
	for gz in "$SHARD_DIR"/*.jsonl.gz; do
	echo "[*] merging shard $(basename "$gz")"
	gunzip -c "$gz" >> out_merged/generations.jsonl
	done
	echo "[+] merged cache: $(wc -l < out_merged/generations.jsonl) tasks"

	# 2. authoritative re-parse / re-validate / dedup over the whole merged cache
	OUT_DIR=out_merged SFT_DIR=data_synth_sft $P build_synth_dataset.py > /tmp/build.log 2>&1
	$P - <<'PY'
	import json
	d = json.load(open("out_merged/build_stats.json")); s = d["stats"]
	print(f"[+] kept {s['kept']:,} rows \| templates {d['unique_templates']:,} \| "
	f"accept {d['accept_rate']:.1%} \| dup drops {s.get('dropped_duplicate_template',0):,}")
	for k in sorted(s):
	if k.startswith("model_"):
	print(f" {k[6:]:36s} {s[k]:,}")
	PY

	# 3. card + upload
	if [[ "${1:-}" == "--push" ]]; then
	OUT_DIR=out_merged $P push_synth_dataset.py --repo "$REPO"
	else
	OUT_DIR=out_merged $P push_synth_dataset.py --repo "$REPO" --dry-run
	fi