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
```

python-bytes-distilgpt2 / README.md

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	---
	license: apache-2.0
	tags:
	- generated_from_trainer
	metrics:
	- accuracy
	model-index:
	- name: python-bytes-distilgpt2
	results: []
	widget:
	- text: "fastAPI is a great new web framework to easily build web APIs."
	example_title: "fastAPI"
	- text: "The new tool uses sqlite for performance"
	example_title: "sqlite"
	---

	<!-- This model card has been generated automatically according to the information the Trainer had access to. You
	should probably proofread and complete it, then remove this comment. -->

	# python-bytes-distilgpt2

	This model is not affiliated with the Python Bytes podcast in any way.

	This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on [Python Bytes show notes](https://github.com/mikeckennedy/python_bytes_show_notes/tree/master/transcripts).

	It achieves the following results on the evaluation set:
	- Loss: 3.0372
	- Accuracy: 0.3969

	## Model description

	This model generates conversation between the two show hosts (Michael Kennedy and Brian Okken), and sometimes guests appear :).

	## Intended uses & limitations

	This model was trained specifically for educational purposes and is intended for other users to use it in a similar manner.

	## Training and evaluation data

	Data is located [on GitHub](https://github.com/mikeckennedy/python_bytes_show_notes/tree/master/transcripts)

	## Training procedure

	### Training hyperparameters

	The following hyperparameters were used during training:
	- learning_rate: 5e-05
	- train_batch_size: 4
	- eval_batch_size: 4
	- seed: 42
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- num_epochs: 3.0

	### Training results



	### Framework versions

	- Transformers 4.22.0.dev0
	- Pytorch 1.12.0+cu113
	- Datasets 2.4.0
	- Tokenizers 0.12.1