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")

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 Oct 8, 2022

Commit

ed200a6

1 Parent(s): c316b87

Add links to data

Browse files

Files changed (1) hide show

README.md +5 -4

README.md CHANGED Viewed

@@ -21,22 +21,23 @@ should probably proofread and complete it, then remove this comment. -->
 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 an unknown dataset.
 It achieves the following results on the evaluation set:
 - Loss: 3.0372
 - Accuracy: 0.3969
 ## Model description
-More information needed
 ## Intended uses & limitations
-More information needed
 ## Training and evaluation data
-More information needed
 ## Training procedure

 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