Instructions to use marin-community/marin-32b-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use marin-community/marin-32b-base with Transformers:

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

pipe = pipeline("text-generation", model="marin-community/marin-32b-base")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("marin-community/marin-32b-base")
model = AutoModelForCausalLM.from_pretrained("marin-community/marin-32b-base")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use marin-community/marin-32b-base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "marin-community/marin-32b-base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "marin-community/marin-32b-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/marin-community/marin-32b-base

SGLang

How to use marin-community/marin-32b-base 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 "marin-community/marin-32b-base" \
    --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": "marin-community/marin-32b-base",
		"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 "marin-community/marin-32b-base" \
        --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": "marin-community/marin-32b-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use marin-community/marin-32b-base with Docker Model Runner:
```
docker model run hf.co/marin-community/marin-32b-base
```

eliebak HF Staff commited on Oct 29, 2025

Commit

a0fad73

verified ·

1 Parent(s): c48cd94

fix link to retrospective

Browse files

Files changed (1) hide show

README.md +1 -1

README.md CHANGED Viewed

@@ -103,7 +103,7 @@ print(tokenizer.batch_decode(response, skip_special_tokens=True)[0])
 - **Repositories:**
     - Core repo (data and experiment management): https://github.com/marin-community/marin
     - Training code: https://github.com/stanford-crfm/levanter
-- **Retrospective:** https://marin.readthedocs.io/en/latest/reports/marin-32b-retro.html
 - **W&B Logs:** [Marin 32B](https://wandb.ai/marin-community/marin/reports/32B-Figures--VmlldzoxNDg0MDM2NQ)

 - **Repositories:**
     - Core repo (data and experiment management): https://github.com/marin-community/marin
     - Training code: https://github.com/stanford-crfm/levanter
+- **Retrospective:** https://marin.readthedocs.io/en/latest/reports/marin-32b-retro/
 - **W&B Logs:** [Marin 32B](https://wandb.ai/marin-community/marin/reports/32B-Figures--VmlldzoxNDg0MDM2NQ)