Instructions to use JstnMcBrd/gpt2-finetuned-python-purpose with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use JstnMcBrd/gpt2-finetuned-python-purpose with Transformers:

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

pipe = pipeline("text-generation", model="JstnMcBrd/gpt2-finetuned-python-purpose")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("JstnMcBrd/gpt2-finetuned-python-purpose")
model = AutoModelForCausalLM.from_pretrained("JstnMcBrd/gpt2-finetuned-python-purpose")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use JstnMcBrd/gpt2-finetuned-python-purpose with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/JstnMcBrd/gpt2-finetuned-python-purpose

SGLang

How to use JstnMcBrd/gpt2-finetuned-python-purpose 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 "JstnMcBrd/gpt2-finetuned-python-purpose" \
    --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": "JstnMcBrd/gpt2-finetuned-python-purpose",
		"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 "JstnMcBrd/gpt2-finetuned-python-purpose" \
        --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": "JstnMcBrd/gpt2-finetuned-python-purpose",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use JstnMcBrd/gpt2-finetuned-python-purpose with Docker Model Runner:
```
docker model run hf.co/JstnMcBrd/gpt2-finetuned-python-purpose
```

JstnMcBrd commited on Dec 11, 2025

Commit

847382a

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1 Parent(s): 000bd3c

Update README.md

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@@ -7,6 +7,10 @@ tags:
 model-index:
 - name: gpt2-finetuned-python-purpose
   results: []
 ---
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -14,7 +18,7 @@ should probably proofread and complete it, then remove this comment. -->
 # gpt2-finetuned-python-purpose
-This model is a fine-tuned version of [openai-community/gpt2](https://huggingface.co/openai-community/gpt2) on an unknown dataset.
 It achieves the following results on the evaluation set:
 - Loss: 0.4763
@@ -59,4 +63,4 @@ The following hyperparameters were used during training:
 - Transformers 4.57.1
 - Pytorch 2.4.1+cu121
 - Datasets 4.4.1
-- Tokenizers 0.22.1

 model-index:
 - name: gpt2-finetuned-python-purpose
   results: []
+datasets:
+- flytech/python-codes-25k
+language:
+- en
 ---
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 # gpt2-finetuned-python-purpose
+This model is a fine-tuned version of [openai-community/gpt2](https://huggingface.co/openai-community/gpt2) on [flytech/python-codes-25k](https://huggingface.co/flytech/python-codes-25k).
 It achieves the following results on the evaluation set:
 - Loss: 0.4763
 - Transformers 4.57.1
 - Pytorch 2.4.1+cu121
 - Datasets 4.4.1
+- Tokenizers 0.22.1