Instructions to use Experimental-Orange/opt_125m_summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Experimental-Orange/opt_125m_summarization with Transformers:

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

pipe = pipeline("text-generation", model="Experimental-Orange/opt_125m_summarization")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Experimental-Orange/opt_125m_summarization")
model = AutoModelForCausalLM.from_pretrained("Experimental-Orange/opt_125m_summarization", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Experimental-Orange/opt_125m_summarization with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Experimental-Orange/opt_125m_summarization"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Experimental-Orange/opt_125m_summarization",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Experimental-Orange/opt_125m_summarization

SGLang

How to use Experimental-Orange/opt_125m_summarization 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 "Experimental-Orange/opt_125m_summarization" \
    --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": "Experimental-Orange/opt_125m_summarization",
		"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 "Experimental-Orange/opt_125m_summarization" \
        --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": "Experimental-Orange/opt_125m_summarization",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Experimental-Orange/opt_125m_summarization with Docker Model Runner:
```
docker model run hf.co/Experimental-Orange/opt_125m_summarization
```

Benjamin Sturgeon commited on Dec 10, 2023

Commit

0a34913

1 Parent(s): 8b4fcd3

Training in progress, epoch 0

Browse files

Files changed (3) hide show

model.safetensors +1 -1
runs/Dec10_18-57-52_ddd1f30cf116/events.out.tfevents.1702234673.ddd1f30cf116.42576.4 +3 -0
training_args.bin +1 -1

model.safetensors CHANGED Viewed

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:aa41c1b32dda4a70cde19475b4996e9547241f81a8e8a67e79f11015d13b63dc
 size 500979600

 version https://git-lfs.github.com/spec/v1
+oid sha256:7bdbe38b91121e563390f078638793dc308372dd05b7405ef225a22826cf6870
 size 500979600

runs/Dec10_18-57-52_ddd1f30cf116/events.out.tfevents.1702234673.ddd1f30cf116.42576.4 ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:bbe6338626b6fd380d14e7fe87e0159c205b57b57418b0d1560d6b65c67f4348
+size 7157

training_args.bin CHANGED Viewed

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:2def54584f3072183274c0f1106ec0e8b917adfae00639c1e49de10701ac9220
 size 4600

 version https://git-lfs.github.com/spec/v1
+oid sha256:0d54e8898dcaf888e1f19d7a0e08dbcfc02074ddded6ac30ae0631ccf7f5b15b
 size 4600