Instructions to use edbeeching/opt-125m-imdb with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use edbeeching/opt-125m-imdb with Transformers:

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

pipe = pipeline("text-generation", model="edbeeching/opt-125m-imdb")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("edbeeching/opt-125m-imdb")
model = AutoModelForCausalLM.from_pretrained("edbeeching/opt-125m-imdb", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use edbeeching/opt-125m-imdb with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "edbeeching/opt-125m-imdb"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "edbeeching/opt-125m-imdb",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/edbeeching/opt-125m-imdb

SGLang

How to use edbeeching/opt-125m-imdb 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 "edbeeching/opt-125m-imdb" \
    --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": "edbeeching/opt-125m-imdb",
		"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 "edbeeching/opt-125m-imdb" \
        --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": "edbeeching/opt-125m-imdb",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use edbeeching/opt-125m-imdb with Docker Model Runner:
```
docker model run hf.co/edbeeching/opt-125m-imdb
```

opt-125m-imdb

Commit History

Apr 13, 2025

06:43
Adding `safetensors` variant of this model⁠

SFconvertbot

Verified signature
7df9dd1

Feb 22, 2023

11:12
End of training

edbeeching HF Staff

c9afb71
11:12
update model card README.md

edbeeching HF Staff

0f5f0e6
11:09
Model save

edbeeching HF Staff

180bc42
11:00
initial commit

edbeeching HF Staff

d1ff336

Commit History

Apr 13, 2025

Adding `safetensors` variant of this model⁠

Feb 22, 2023

End of training

update model card README.md

Model save

initial commit