Instructions to use azherali/python_codeparrot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use azherali/python_codeparrot with Transformers:

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

pipe = pipeline("text-generation", model="azherali/python_codeparrot")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("azherali/python_codeparrot")
model = AutoModelForCausalLM.from_pretrained("azherali/python_codeparrot")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use azherali/python_codeparrot with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/azherali/python_codeparrot

SGLang

How to use azherali/python_codeparrot 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 "azherali/python_codeparrot" \
    --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": "azherali/python_codeparrot",
		"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 "azherali/python_codeparrot" \
        --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": "azherali/python_codeparrot",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use azherali/python_codeparrot with Docker Model Runner:
```
docker model run hf.co/azherali/python_codeparrot
```

python_codeparrot

File size: 1,300 Bytes

4ff2cae

---
library_name: transformers
license: mit
base_model: gpt2
tags:
- generated_from_trainer
datasets:
- code_search_net
model-index:
- name: python_codeparrot
  results: []
---

<!-- 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_codeparrot

This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the code_search_net dataset.

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 1
- mixed_precision_training: Native AMP

### Training results



### Framework versions

- Transformers 4.55.4
- Pytorch 2.8.0+cu126
- Datasets 3.6.0
- Tokenizers 0.21.4