Instructions to use AI-MO/NuminaMath-7B-TIR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AI-MO/NuminaMath-7B-TIR with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="AI-MO/NuminaMath-7B-TIR")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("AI-MO/NuminaMath-7B-TIR")
model = AutoModelForCausalLM.from_pretrained("AI-MO/NuminaMath-7B-TIR")
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 AI-MO/NuminaMath-7B-TIR with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AI-MO/NuminaMath-7B-TIR"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AI-MO/NuminaMath-7B-TIR",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AI-MO/NuminaMath-7B-TIR

SGLang

How to use AI-MO/NuminaMath-7B-TIR 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 "AI-MO/NuminaMath-7B-TIR" \
    --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": "AI-MO/NuminaMath-7B-TIR",
		"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 "AI-MO/NuminaMath-7B-TIR" \
        --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": "AI-MO/NuminaMath-7B-TIR",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use AI-MO/NuminaMath-7B-TIR with Docker Model Runner:
```
docker model run hf.co/AI-MO/NuminaMath-7B-TIR
```

add AIBOM

#13

by RiccardoDav - opened Jul 21, 2025

base: refs/heads/main

←

from: refs/pr/13

Discussion Files changed

+71

-0

Files changed (1) hide show

AI-MO_NuminaMath-7B-TIR.json +71 -0

AI-MO_NuminaMath-7B-TIR.json ADDED Viewed

	@@ -0,0 +1,71 @@

+{
+    "bomFormat": "CycloneDX",
+    "specVersion": "1.6",
+    "serialNumber": "urn:uuid:6abaa880-fd87-409b-b3dd-f7017767e93d",
+    "version": 1,
+    "metadata": {
+        "timestamp": "2025-07-10T09:01:27.226304+00:00",
+        "component": {
+            "type": "machine-learning-model",
+            "bom-ref": "AI-MO/NuminaMath-7B-TIR-25bf135d-b699-508e-a81d-4ff4bb0efb1d",
+            "name": "AI-MO/NuminaMath-7B-TIR",
+            "externalReferences": [
+                {
+                    "url": "https://huggingface.co/AI-MO/NuminaMath-7B-TIR",
+                    "type": "documentation"
+                }
+            ],
+            "modelCard": {
+                "modelParameters": {
+                    "task": "text-generation",
+                    "architectureFamily": "llama",
+                    "modelArchitecture": "LlamaForCausalLM"
+                },
+                "properties": [
+                    {
+                        "name": "library_name",
+                        "value": "transformers"
+                    },
+                    {
+                        "name": "base_model",
+                        "value": "deepseek-ai/deepseek-math-7b-base"
+                    }
+                ],
+                "consideration": {
+                    "useCases": "Here's how you can run the model using the `pipeline()` function from \ud83e\udd17 Transformers:```pythonimport reimport torchfrom transformers import pipelinepipe = pipeline(\"text-generation\", model=\"AI-MO/NuminaMath-7B-TIR\", torch_dtype=torch.bfloat16, device_map=\"auto\")messages = [{\"role\": \"user\", \"content\": \"For how many values of the constant $k$ will the polynomial $x^{2}+kx+36$ have two distinct integer roots?\"},]prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)gen_config = {\"max_new_tokens\": 1024,\"do_sample\": False,\"stop_strings\": [\"```output\"], # Generate until Python code block is complete\"tokenizer\": pipe.tokenizer,}outputs = pipe(prompt, **gen_config)text = outputs[0][\"generated_text\"]print(text)"
+                }
+            },
+            "authors": [
+                {
+                    "name": "AI-MO"
+                }
+            ],
+            "licenses": [
+                {
+                    "license": {
+                        "id": "Apache-2.0",
+                        "url": "https://spdx.org/licenses/Apache-2.0.html"
+                    }
+                }
+            ],
+            "description": "- **Model type:** A 7B parameter math LLM fine-tuned in two stages of supervised fine-tuning, first on a dataset with math problem-solution pairs and then on a synthetic dataset with examples of multi-step generations using tool-integrated reasoning.- **Language(s) (NLP):** Primarily English- **License:** Apache 2.0- **Finetuned from model:** [deepseek-ai/deepseek-math-7b-base](https://huggingface.co/deepseek-ai/deepseek-math-7b-base)",
+            "tags": [
+                "transformers",
+                "safetensors",
+                "llama",
+                "text-generation",
+                "alignment-handbook",
+                "generated_from_trainer",
+                "conversational",
+                "arxiv:2309.17452",
+                "base_model:deepseek-ai/deepseek-math-7b-base",
+                "base_model:finetune:deepseek-ai/deepseek-math-7b-base",
+                "license:apache-2.0",
+                "autotrain_compatible",
+                "text-generation-inference",
+                "endpoints_compatible",
+                "region:us"
+            ]
+        }
+    }
+}