Instructions to use pmahdavi/Olmo-3.1-7B-Math-Code with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use pmahdavi/Olmo-3.1-7B-Math-Code with Transformers:

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

pipe = pipeline("text-generation", model="pmahdavi/Olmo-3.1-7B-Math-Code")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("pmahdavi/Olmo-3.1-7B-Math-Code")
model = AutoModelForCausalLM.from_pretrained("pmahdavi/Olmo-3.1-7B-Math-Code")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use pmahdavi/Olmo-3.1-7B-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-3.1-7B-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-3.1-7B-Math-Code",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/pmahdavi/Olmo-3.1-7B-Math-Code

SGLang

How to use pmahdavi/Olmo-3.1-7B-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-3.1-7B-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-3.1-7B-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-3.1-7B-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-3.1-7B-Math-Code",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use pmahdavi/Olmo-3.1-7B-Math-Code with Docker Model Runner:
```
docker model run hf.co/pmahdavi/Olmo-3.1-7B-Math-Code
```

merged-model

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

Merge Details

Merge Method

This model was merged using the Linear merge method.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

# Linear merge of OLMo-3.1 Math and Code RL models
# Output = 0.5 * Math + 0.5 * Code
#
# Usage:
#   modal run modal_merge.py --config examples/olmo3.1-linear-merge.yaml --hf-repo pmahdavi/Olmo-3.1-7B-Math-Code

models:
  - model: allenai/Olmo-3.1-7B-RL-Zero-Math
    parameters:
      weight: 0.5
  - model: allenai/Olmo-3.1-7B-RL-Zero-Code
    parameters:
      weight: 0.5

merge_method: linear
dtype: bfloat16

Downloads last month: 4

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for pmahdavi/Olmo-3.1-7B-Math-Code

allenai/Olmo-3.1-7B-RL-Zero-Code

allenai/Olmo-3.1-7B-RL-Zero-Math

Merge model

this model

Paper for pmahdavi/Olmo-3.1-7B-Math-Code

Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time

Paper • 2203.05482 • Published Mar 10, 2022 • 8