Instructions to use pmahdavi/Olmo-3-7B-Think-Math-Code with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use pmahdavi/Olmo-3-7B-Think-Math-Code with Transformers:

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

pipe = pipeline("text-generation", model="pmahdavi/Olmo-3-7B-Think-Math-Code")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("pmahdavi/Olmo-3-7B-Think-Math-Code")
model = AutoModelForCausalLM.from_pretrained("pmahdavi/Olmo-3-7B-Think-Math-Code")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use pmahdavi/Olmo-3-7B-Think-Math-Code with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "pmahdavi/Olmo-3-7B-Think-Math-Code"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "pmahdavi/Olmo-3-7B-Think-Math-Code",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/pmahdavi/Olmo-3-7B-Think-Math-Code

SGLang

How to use pmahdavi/Olmo-3-7B-Think-Math-Code 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 "pmahdavi/Olmo-3-7B-Think-Math-Code" \
    --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": "pmahdavi/Olmo-3-7B-Think-Math-Code",
		"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 "pmahdavi/Olmo-3-7B-Think-Math-Code" \
        --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": "pmahdavi/Olmo-3-7B-Think-Math-Code",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use pmahdavi/Olmo-3-7B-Think-Math-Code with Docker Model Runner:
```
docker model run hf.co/pmahdavi/Olmo-3-7B-Think-Math-Code
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

merged-model

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the Task Arithmetic merge method using allenai/Olmo-3-1025-7B as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

# Task arithmetic merge: Apply Math+Code task vectors to Think-SFT
#
# Mathematical formulation:
#   output = Think-SFT + 0.5*(RL-Zero-Math - base) + 0.5*(RL-Zero-Code - base)
#
# This is achieved by treating Think-SFT as a model with weight=1.0:
#   output = base + 1.0*(Think-SFT - base) + 0.5*(Math - base) + 0.5*(Code - base)
#
# Usage:
#   modal run modal_merge.py --config examples/olmo-think-math-code.yaml --hf-repo pmahdavi/Olmo-3-7B-Think-Math-Code

merge_method: task_arithmetic
base_model: allenai/Olmo-3-1025-7B
models:
  - model: allenai/Olmo-3-7B-Think-SFT
    parameters:
      weight: 1.0
  - model: allenai/Olmo-3-7B-RL-Zero-Math
    parameters:
      weight: 0.5
  - model: allenai/Olmo-3-7B-RL-Zero-Code
    parameters:
      weight: 0.5
dtype: bfloat16