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

Libraries

How to use underactuated/opt-125m_ft_test with Transformers:

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

pipe = pipeline("text-generation", model="underactuated/opt-125m_ft_test")

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

tokenizer = AutoTokenizer.from_pretrained("underactuated/opt-125m_ft_test")
model = AutoModelForCausalLM.from_pretrained("underactuated/opt-125m_ft_test", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use underactuated/opt-125m_ft_test with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/underactuated/opt-125m_ft_test

SGLang

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

Docker Model Runner
How to use underactuated/opt-125m_ft_test with Docker Model Runner:
```
docker model run hf.co/underactuated/opt-125m_ft_test
```

underactuated commited on May 28, 2025

Commit

2a295ea

verified ·

1 Parent(s): 695b4c4

End of training

Browse files

Files changed (2) hide show

model.safetensors +1 -1
training_args.bin +1 -1

model.safetensors CHANGED Viewed

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:4d9e6ad6b0af236364aa34f2a883befaa5338e40b45d52513f1730dfb36225e8
 size 500979600

 version https://git-lfs.github.com/spec/v1
+oid sha256:a210e233e90aa31d9189e4daec19e036679528070dabe67f75825f3f69c69759
 size 500979600

training_args.bin CHANGED Viewed

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:fcc8d0bd7395ac976ee12afc48042534d0d0729549a8d6a56b33dfbc81170ae5
 size 5368

 version https://git-lfs.github.com/spec/v1
+oid sha256:cfb9b8c0d7876f7f08f1fa9b5e0501fdb509f873979b07b582c2962529318df0
 size 5368