Instructions to use AI-MO/NuminaMath-72B-TIR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AI-MO/NuminaMath-72B-TIR with Transformers:

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

pipe = pipeline("text-generation", model="AI-MO/NuminaMath-72B-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-72B-TIR")
model = AutoModelForCausalLM.from_pretrained("AI-MO/NuminaMath-72B-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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use AI-MO/NuminaMath-72B-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-72B-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-72B-TIR",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AI-MO/NuminaMath-72B-TIR

SGLang

How to use AI-MO/NuminaMath-72B-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-72B-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-72B-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-72B-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-72B-TIR",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use AI-MO/NuminaMath-72B-TIR with Docker Model Runner:
```
docker model run hf.co/AI-MO/NuminaMath-72B-TIR
```

lewtun HF Staff commited on Jul 19, 2024

Commit

e07cd67

verified ·

1 Parent(s): 7682df3

Update README.md

Browse files

Files changed (1) hide show

README.md +8 -2

README.md CHANGED Viewed

@@ -3,11 +3,17 @@ base_model: AI-MO/qwen2-72b-sft
 tags:
 - alignment-handbook
 - generated_from_trainer
 datasets:
-- AI-MO/numina-dataset-tora-v1.0-release-candidate-1
 model-index:
 - name: qwen2-72b-sft-aimo_v03.00
   results: []
 ---
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -65,4 +71,4 @@ The following hyperparameters were used during training:
 - Transformers 4.42.3
 - Pytorch 2.3.0+cu121
 - Datasets 2.18.0
-- Tokenizers 0.19.1

 tags:
 - alignment-handbook
 - generated_from_trainer
+- math
+- aimo
 datasets:
+- AI-MO/NuminaMath-TIR
 model-index:
 - name: qwen2-72b-sft-aimo_v03.00
   results: []
+license: other
+license_name: tongyi-qianwen
+language:
+- en
 ---
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 - Transformers 4.42.3
 - Pytorch 2.3.0+cu121
 - Datasets 2.18.0
+- Tokenizers 0.19.1