Instructions to use pmahdavi/Olmo-3.1-7B-Math-Pruned-50 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use pmahdavi/Olmo-3.1-7B-Math-Pruned-50 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-Pruned-50")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("pmahdavi/Olmo-3.1-7B-Math-Pruned-50")
model = AutoModelForCausalLM.from_pretrained("pmahdavi/Olmo-3.1-7B-Math-Pruned-50")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use pmahdavi/Olmo-3.1-7B-Math-Pruned-50 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-Pruned-50"
# 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-Pruned-50",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/pmahdavi/Olmo-3.1-7B-Math-Pruned-50

SGLang

How to use pmahdavi/Olmo-3.1-7B-Math-Pruned-50 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-Pruned-50" \
    --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-Pruned-50",
		"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-Pruned-50" \
        --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-Pruned-50",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use pmahdavi/Olmo-3.1-7B-Math-Pruned-50 with Docker Model Runner:
```
docker model run hf.co/pmahdavi/Olmo-3.1-7B-Math-Pruned-50
```

merged-model

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

Merge Details

Merge Method

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

Models Merged

The following models were included in the merge:

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

Configuration

The following YAML configuration was used to produce this model:

# Magnitude pruning on OLMo-3.1-7B-RL-Zero-Math task vector
#
# Logic:
#   1. Compute task vector: delta = finetuned - base
#   2. Keep top 50% of delta by absolute magnitude, zero the rest
#   3. Output = base + pruned_delta
#
# Usage:
#   modal run modal_merge.py --config examples/olmo-math-magnitude-prune.yaml --hf-repo pmahdavi/Olmo-3.1-7B-Math-Pruned-50

merge_method: ties
base_model: allenai/Olmo-3-1025-7B

models:
  - model: allenai/Olmo-3.1-7B-RL-Zero-Math
    parameters:
      weight: 1.0
      density: 0.5  # Keep top 50% by magnitude

parameters:
  normalize: false  # Don't normalize (single model, no effect anyway)

dtype: bfloat16

Downloads last month: 2

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for pmahdavi/Olmo-3.1-7B-Math-Pruned-50

allenai/Olmo-3-1025-7B

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

Merge model

this model

Paper for pmahdavi/Olmo-3.1-7B-Math-Pruned-50

Resolving Interference When Merging Models

Paper • 2306.01708 • Published Jun 2, 2023 • 19