Instructions to use nvidia/OpenMath-Nemotron-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nvidia/OpenMath-Nemotron-7B with Transformers:

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

pipe = pipeline("text-generation", model="nvidia/OpenMath-Nemotron-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("nvidia/OpenMath-Nemotron-7B")
model = AutoModelForCausalLM.from_pretrained("nvidia/OpenMath-Nemotron-7B")
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 nvidia/OpenMath-Nemotron-7B with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/nvidia/OpenMath-Nemotron-7B

SGLang

How to use nvidia/OpenMath-Nemotron-7B 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 "nvidia/OpenMath-Nemotron-7B" \
    --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": "nvidia/OpenMath-Nemotron-7B",
		"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 "nvidia/OpenMath-Nemotron-7B" \
        --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": "nvidia/OpenMath-Nemotron-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use nvidia/OpenMath-Nemotron-7B with Docker Model Runner:
```
docker model run hf.co/nvidia/OpenMath-Nemotron-7B
```

OpenMath-Nemotron-7B / tokenizer_config.json

igitman

Update tokenizer_config.json

c94eb10 verified about 1 year ago

raw

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5.27 kB

	{
	"add_bos_token": false,
	"add_prefix_space": false,
	"added_tokens_decoder": {
	"151643": {
	"content": "<\|endoftext\|>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false,
	"special": true
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	"151644": {
	"content": "<\|im_start\|>",
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	"additional_special_tokens": [
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	"<\|im_end\|>",
	"<\|object_ref_start\|>",
	"<\|object_ref_end\|>",
	"<\|box_start\|>",
	"<\|box_end\|>",
	"<\|quad_start\|>",
	"<\|quad_end\|>",
	"<\|vision_start\|>",
	"<\|vision_end\|>",
	"<\|vision_pad\|>",
	"<\|image_pad\|>",
	"<\|video_pad\|>"
	],
	"bos_token": null,
	"chat_template": "{%- if messages[0]['role'] == 'system' %}\n {{- '<\|im_start\|>system\n' + messages[0]['content'] + '<\|im_end\|>\n' }}\n{%- else %}\n {{- '<\|im_start\|>system\n<\|im_end\|>\n' }}\n{%- endif %}\n{%- for message in messages %}\n {%- if (message.role == 'user') or (message.role == 'system' and not loop.first) or (message.role == 'assistant') %}\n {{- '<\|im_start\|>' + message.role + '\n' + message.content + '<\|im_end\|>' + '\n' }}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<\|im_start\|>assistant\n' }}\n{%- endif %}",
	"clean_up_tokenization_spaces": false,
	"eos_token": "<\|im_end\|>",
	"errors": "replace",
	"extra_special_tokens": {},
	"model_max_length": 131072,
	"pad_token": "<\|endoftext\|>",
	"split_special_tokens": false,
	"tokenizer_class": "Qwen2Tokenizer",
	"unk_token": null
	}