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 /chat.py

oddadmix

training code: data generation, SFT, eval, GRPO

867d0f3 verified 3 days ago

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1.01 kB

	"""Quick manual probe: python chat.py "سؤالك هنا" [model_dir]"""
	import sys, torch
	from transformers import AutoModelForCausalLM, AutoTokenizer

	question = sys.argv[1] if len(sys.argv) > 1 else "إذا كان لديك 1500 ريال وأنفقت 20% منها على الكتب، فكم تبقى معك؟"
	model_dir = sys.argv[2] if len(sys.argv) > 2 else "./Nawah-Reasoning-v1"

	tok = AutoTokenizer.from_pretrained(model_dir)
	model = AutoModelForCausalLM.from_pretrained(model_dir, dtype=torch.bfloat16).cuda().eval()

	prompt = tok.apply_chat_template([{"role": "user", "content": question}], tokenize=False, add_generation_prompt=True)
	ids = tok(prompt, return_tensors="pt").to("cuda")
	out = model.generate(**ids, max_new_tokens=512, do_sample=False,
	eos_token_id=[tok.eos_token_id, tok.convert_tokens_to_ids("<\|im_end\|>")],
	pad_token_id=tok.pad_token_id)
	print(tok.decode(out[0][ids["input_ids"].shape[1]:], skip_special_tokens=False).split("<\|im_end\|>")[0])