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
| """Shared parsing/masking helpers for the GSM8K reasoning dataset translation.""" | |
| import re | |
| RECORD_RE = re.compile( | |
| r"<question>(.*?)</question>\s*<thinking>(.*?)</thinking>\s*<answer>(.*?)</answer>", re.S | |
| ) | |
| NUM_RE = re.compile(r"\d+(?:\.\d+)?") | |
| PH_RE = re.compile(r"#(\d+)#") | |
| SRC_PROMPT = "Translate the following English sentence into Arabic:\n{text} <ar>" | |
| def parse(text): | |
| m = RECORD_RE.match(text.strip()) | |
| if not m: | |
| return None | |
| return m.group(1).strip(), m.group(2).strip(), m.group(3).strip() | |
| def mask_numbers(text): | |
| """'168 + 19 = 187' -> ('#0# + #1# = #2#', ['168', '19', '187'])""" | |
| nums = [] | |
| def repl(m): | |
| nums.append(m.group(0)) | |
| return f"#{len(nums) - 1}#" | |
| return NUM_RE.sub(repl, text), nums | |
| def unmask_numbers(text, nums): | |
| """Restore. Returns (text, ok) — ok is False if any placeholder was lost or duplicated.""" | |
| seen = [] | |
| def repl(m): | |
| i = int(m.group(1)) | |
| seen.append(i) | |
| return nums[i] if i < len(nums) else m.group(0) | |
| out = PH_RE.sub(repl, text) | |
| return out, sorted(seen) == list(range(len(nums))) | |
| def build_record(question, thinking, answer): | |
| return f"<question>{question}</question> <thinking>{thinking}</thinking> <answer>{answer}</answer>" | |