Instructions to use kaizerBox/gpt2-summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kaizerBox/gpt2-summarization with Transformers:

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

pipe = pipeline("text-generation", model="kaizerBox/gpt2-summarization")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("kaizerBox/gpt2-summarization")
model = AutoModelForCausalLM.from_pretrained("kaizerBox/gpt2-summarization")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use kaizerBox/gpt2-summarization with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kaizerBox/gpt2-summarization"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kaizerBox/gpt2-summarization",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/kaizerBox/gpt2-summarization

SGLang

How to use kaizerBox/gpt2-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 "kaizerBox/gpt2-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": "kaizerBox/gpt2-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 "kaizerBox/gpt2-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": "kaizerBox/gpt2-summarization",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use kaizerBox/gpt2-summarization with Docker Model Runner:
```
docker model run hf.co/kaizerBox/gpt2-summarization
```

kaizerBox commited on Nov 28, 2023

Commit

e4725a0

1 Parent(s): ec348d3

Time is 197.06894804239272 minutes, Memory is 9.5GB

Browse files

Files changed (4) hide show

README.md +4 -4
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runs/Nov28_19-01-57_a0d0a02a0f22/events.out.tfevents.1701198117.a0d0a02a0f22.772.0 +3 -0
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README.md CHANGED Viewed

@@ -15,7 +15,7 @@ should probably proofread and complete it, then remove this comment. -->
 This model is a fine-tuned version of [](https://huggingface.co/) on the xsum dataset.
 It achieves the following results on the evaluation set:
-- Loss: 4.0256
 ## Model description
@@ -50,9 +50,9 @@ The following hyperparameters were used during training:
 | Training Loss | Epoch | Step  | Validation Loss |
 |:-------------:|:-----:|:-----:|:---------------:|
-| 4.7214        | 1.0   | 5762  | 4.2254          |
-| 4.1228        | 2.0   | 11525 | 4.0669          |
-| 4.0011        | 3.0   | 17286 | 4.0256          |
 ### Framework versions

 This model is a fine-tuned version of [](https://huggingface.co/) on the xsum dataset.
 It achieves the following results on the evaluation set:
+- Loss: 4.0100
 ## Model description
 | Training Loss | Epoch | Step  | Validation Loss |
 |:-------------:|:-----:|:-----:|:---------------:|
+| 4.7132        | 1.0   | 5762  | 4.2108          |
+| 4.111         | 2.0   | 11525 | 4.0528          |
+| 3.9871        | 3.0   | 17286 | 4.0100          |
 ### Framework versions

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