Instructions to use Sentdex/GPyT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sentdex/GPyT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Sentdex/GPyT")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Sentdex/GPyT") model = AutoModelForCausalLM.from_pretrained("Sentdex/GPyT") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Sentdex/GPyT with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Sentdex/GPyT" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Sentdex/GPyT", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Sentdex/GPyT
- SGLang
How to use Sentdex/GPyT 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 "Sentdex/GPyT" \ --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": "Sentdex/GPyT", "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 "Sentdex/GPyT" \ --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": "Sentdex/GPyT", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Sentdex/GPyT with Docker Model Runner:
docker model run hf.co/Sentdex/GPyT
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("Sentdex/GPyT")
model = AutoModelForCausalLM.from_pretrained("Sentdex/GPyT")GPyT is a GPT2 model trained from scratch (not fine tuned) on Python code from Github. Overall, it was ~80GB of pure Python code, the current GPyT model is a mere 2 epochs through this data, so it may benefit greatly from continued training and/or fine-tuning.
Newlines are replaced by <N>
Input to the model is code, up to the context length of 1024, with newlines replaced by <N>
Here's a quick example of using this model:
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("Sentdex/GPyT")
model = AutoModelWithLMHead.from_pretrained("Sentdex/GPyT")
# copy and paste some code in here
inp = """import"""
newlinechar = "<N>"
converted = inp.replace("\n", newlinechar)
tokenized = tokenizer.encode(converted, return_tensors='pt')
resp = model.generate(tokenized)
decoded = tokenizer.decode(resp[0])
reformatted = decoded.replace("<N>","\n")
print(reformatted)
Should produce:
import numpy as np
import pytest
import pandas as pd<N
This model does a ton more than just imports, however. For a bunch of examples and a better understanding of the model's capabilities: https://pythonprogramming.net/GPT-python-code-transformer-model-GPyT/
Considerations:
- This model is intended for educational and research use only. Do not trust model outputs.
- Model is highly likely to regurgitate code almost exactly as it saw it. It's up to you to determine licensing if you intend to actually use the generated code.
- All Python code was blindly pulled from github. This means included code is both Python 2 and 3, among other more subtle differences, such as tabs being 2 spaces in some cases and 4 in others...and more non-homologous things.
- Along with the above, this means the code generated could wind up doing or suggesting just about anything. Run the generated code at own risk...it could be anything
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Sentdex/GPyT")