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
```

oddadmix commited on 6 days ago

Commit

4cdd298

verified ·

1 Parent(s): 089d47f

training code: data generation, SFT, eval, GRPO

Browse files

Files changed (1) hide show

code/push_release.py +3 -1

code/push_release.py CHANGED Viewed

@@ -303,7 +303,9 @@ def main():
     # Weights first, then the card, so the repo is never public-and-uncarded for long.
     api.upload_folder(
         folder_path=str(MODEL_DIR), repo_id=args.repo,
-        ignore_patterns=["checkpoint-*/*", "checkpoint-*", "eval_on_*.json", "README.md"],
         commit_message="Nawah-Math-Reasoning: weights, tokenizer, eval + training metrics")
     api.upload_file(path_or_fileobj=str(out), path_in_repo="README.md", repo_id=args.repo,
                     commit_message="model card")

     # Weights first, then the card, so the repo is never public-and-uncarded for long.
     api.upload_folder(
         folder_path=str(MODEL_DIR), repo_id=args.repo,
+        # eval_on_synth.json ships: it is the 1,000-row cross-model synth cell the card quotes.
+        # eval_on_v6mix.json does not — it is byte-identical to eval_reasoning.json.
+        ignore_patterns=["checkpoint-*/*", "checkpoint-*", "eval_on_v6mix.json", "README.md"],
         commit_message="Nawah-Math-Reasoning: weights, tokenizer, eval + training metrics")
     api.upload_file(path_or_fileobj=str(out), path_in_repo="README.md", repo_id=args.repo,
                     commit_message="model card")