Instructions to use arvkevi/python-bytes-distilgpt2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use arvkevi/python-bytes-distilgpt2 with Transformers:

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

pipe = pipeline("text-generation", model="arvkevi/python-bytes-distilgpt2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("arvkevi/python-bytes-distilgpt2")
model = AutoModelForCausalLM.from_pretrained("arvkevi/python-bytes-distilgpt2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use arvkevi/python-bytes-distilgpt2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "arvkevi/python-bytes-distilgpt2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "arvkevi/python-bytes-distilgpt2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/arvkevi/python-bytes-distilgpt2

SGLang

How to use arvkevi/python-bytes-distilgpt2 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 "arvkevi/python-bytes-distilgpt2" \
    --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": "arvkevi/python-bytes-distilgpt2",
		"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 "arvkevi/python-bytes-distilgpt2" \
        --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": "arvkevi/python-bytes-distilgpt2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use arvkevi/python-bytes-distilgpt2 with Docker Model Runner:
```
docker model run hf.co/arvkevi/python-bytes-distilgpt2
```

arvkevi commited on Aug 3, 2022

Commit

602baf8

1 Parent(s): b3b0af8

update model card README.md

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@@ -2,6 +2,8 @@
 license: apache-2.0
 tags:
 - generated_from_trainer
 model-index:
 - name: python-bytes-distilgpt2
   results: []
@@ -13,6 +15,9 @@ should probably proofread and complete it, then remove this comment. -->
 # python-bytes-distilgpt2
 This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset.
 ## Model description

 license: apache-2.0
 tags:
 - generated_from_trainer
+metrics:
+- accuracy
 model-index:
 - name: python-bytes-distilgpt2
   results: []
 # python-bytes-distilgpt2
 This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 3.0372
+- Accuracy: 0.3969
 ## Model description