Instructions to use tngtech/OLMo-2-Instruct-Math-32B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tngtech/OLMo-2-Instruct-Math-32B with Transformers:

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

pipe = pipeline("text-generation", model="tngtech/OLMo-2-Instruct-Math-32B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tngtech/OLMo-2-Instruct-Math-32B")
model = AutoModelForCausalLM.from_pretrained("tngtech/OLMo-2-Instruct-Math-32B", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use tngtech/OLMo-2-Instruct-Math-32B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tngtech/OLMo-2-Instruct-Math-32B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tngtech/OLMo-2-Instruct-Math-32B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tngtech/OLMo-2-Instruct-Math-32B

SGLang

How to use tngtech/OLMo-2-Instruct-Math-32B 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 "tngtech/OLMo-2-Instruct-Math-32B" \
    --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": "tngtech/OLMo-2-Instruct-Math-32B",
		"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 "tngtech/OLMo-2-Instruct-Math-32B" \
        --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": "tngtech/OLMo-2-Instruct-Math-32B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tngtech/OLMo-2-Instruct-Math-32B with Docker Model Runner:
```
docker model run hf.co/tngtech/OLMo-2-Instruct-Math-32B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

TNG Technology Consulting fine-tuned the 32-billion-parameter OLMo-2 Large Language Model using AMD's MI300X GPUs and the Open R1 dataset, focusing on enhancing the model's reasoning capabilities. The MI300X accelerators, with their multi-chip module architecture and substantial memory bandwidth, facilitated efficient handling of the model's training requirements. The Open R1 dataset, curated by Hugging Face, provided a comprehensive collection of mathematical problems with detailed reasoning traces, serving as an ideal foundation for this fine-tuning endeavor. This collaborative effort underscores the potential of open-source initiatives and advanced hardware in advancing AI research.