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

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 results table

by benlipkin - opened Jul 20, 2024

base: refs/heads/main

←

from: refs/pr/7

Discussion Files changed

+15

-0

Files changed (1) hide show

README.md +15 -0

README.md CHANGED Viewed

@@ -109,6 +109,21 @@ This model is a fine-tuned version of [deepseek-ai/deepseek-math-7b-base](https:
 - **License:** Apache 2.0
 - **Finetuned from model:** [deepseek-ai/deepseek-math-7b-base](https://huggingface.co/deepseek-ai/deepseek-math-7b-base)
 ### Model Sources
 <!-- Provide the basic links for the model. -->

 - **License:** Apache 2.0
 - **Finetuned from model:** [deepseek-ai/deepseek-math-7b-base](https://huggingface.co/deepseek-ai/deepseek-math-7b-base)
+## Model performance
+| | | NuminaMath-7B-CoT | NuminaMath-7B-TIR | Qwen2-7B-Instruct | Llama3-8B-Instruct | DeepSeekMath-7B-Instruct | DeepSeekMath-7B-RL | DART-Math-7B-CoT |
+| --- | --- | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
+| **GSM8k** | 0-shot | 76.3% | 84.6% | 82.3% | 79.6% | 82.8% | **88.2%** | 86.6% |
+| Grade school math |
+| **MATH** | 0-shot | 55.8% | **68.1%** | 49.6% | 30.0% | 46.8% | 51.7% | 53.6% |
+| Math problem-solving |
+| **AMC 2023** | 0-shot | 11/40 | **20/40** | 10/40 | 2/40 | 7/40 | 9/40 | 11/40 |
+| Competition-level math | maj@64 | 18/40 | **31/40** | 13/40 | 9/40 | 13/40 | 14/40 | 16/40 |
+| **AIME 2024** | 0-shot | 0/30 | **5/30** | 1/30 | 0/30 | 1/30 | 1/30 | 1/30 |
+| Competition-level math | maj@64 | 1/30 | **10/30** | 4/30 | 2/30 | 1/30 | 1/30 | 1/30 |
+*Table: Comparison of various 7B and 8B parameter language models on different math benchmarks. All scores except those for NuminaMath-7B-TIR are reported without tool-integrated reasoning.*
 ### Model Sources
 <!-- Provide the basic links for the model. -->