Instructions to use LLM360/MegaMath-Llama-3.2-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LLM360/MegaMath-Llama-3.2-3B with Transformers:

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

pipe = pipeline("text-generation", model="LLM360/MegaMath-Llama-3.2-3B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LLM360/MegaMath-Llama-3.2-3B")
model = AutoModelForCausalLM.from_pretrained("LLM360/MegaMath-Llama-3.2-3B")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use LLM360/MegaMath-Llama-3.2-3B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LLM360/MegaMath-Llama-3.2-3B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LLM360/MegaMath-Llama-3.2-3B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/LLM360/MegaMath-Llama-3.2-3B

SGLang

How to use LLM360/MegaMath-Llama-3.2-3B 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 "LLM360/MegaMath-Llama-3.2-3B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LLM360/MegaMath-Llama-3.2-3B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "LLM360/MegaMath-Llama-3.2-3B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LLM360/MegaMath-Llama-3.2-3B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use LLM360/MegaMath-Llama-3.2-3B with Docker Model Runner:
```
docker model run hf.co/LLM360/MegaMath-Llama-3.2-3B
```

Add link to code

by nielsr HF Staff - opened Apr 9, 2025

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

-3

Files changed (1) hide show

README.md +5 -3

README.md CHANGED Viewed

@@ -1,11 +1,11 @@
 ---
-license: llama3.2
 datasets:
 - LLM360/MegaMath
 language:
 - en
-pipeline_tag: text-generation
 library_name: transformers
 tags:
 - math
 - code
@@ -13,9 +13,10 @@ tags:
 - pal
 ---
 # MegaMath-Llama-3.2-3B
-[Arxiv](arxiv.org/abs/2504.02807) | [Datasets](https://huggingface.co/datasets/LLM360/MegaMath)
 A proof-of-concept model train on [MegaMath](https://huggingface.co/datasets/LLM360/MegaMath) dataset, capable of both Chain-of-Thought and Program-Aided-Language problem solving.
@@ -36,4 +37,5 @@ If you find our work useful, please cite
   year      = {2025},
   note      = {Preprint}
 }
 ```

 ---
 datasets:
 - LLM360/MegaMath
 language:
 - en
 library_name: transformers
+license: llama3.2
+pipeline_tag: text-generation
 tags:
 - math
 - code
 - pal
 ---
+```markdown
 # MegaMath-Llama-3.2-3B
+[Arxiv](arxiv.org/abs/2504.02807) | [Datasets](https://huggingface.co/datasets/LLM360/MegaMath) | [Code](https://github.com/LLM360/MegaMath)
 A proof-of-concept model train on [MegaMath](https://huggingface.co/datasets/LLM360/MegaMath) dataset, capable of both Chain-of-Thought and Program-Aided-Language problem solving.
   year      = {2025},
   note      = {Preprint}
 }
+```
 ```