Instructions to use P0intMaN/PyAutoCode with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use P0intMaN/PyAutoCode with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="P0intMaN/PyAutoCode")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("P0intMaN/PyAutoCode") model = AutoModelForCausalLM.from_pretrained("P0intMaN/PyAutoCode") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use P0intMaN/PyAutoCode with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "P0intMaN/PyAutoCode" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "P0intMaN/PyAutoCode", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/P0intMaN/PyAutoCode
- SGLang
How to use P0intMaN/PyAutoCode 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 "P0intMaN/PyAutoCode" \ --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": "P0intMaN/PyAutoCode", "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 "P0intMaN/PyAutoCode" \ --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": "P0intMaN/PyAutoCode", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use P0intMaN/PyAutoCode with Docker Model Runner:
docker model run hf.co/P0intMaN/PyAutoCode
| license: mit | |
| # PyAutoCode: GPT-2 based Python auto-code. | |
| PyAutoCode is a cut-down python autosuggestion built on **GPT-2** *(motivation: GPyT)* model. This baby model *(trained only up to 3 epochs)* is not **"fine-tuned"** yet therefore, I highly recommend not to use it in a production environment or incorporate PyAutoCode in any of your projects. It has been trained on **112GB** of Python data sourced from the best crowdsource platform ever -- **GitHub**. | |
| *NOTE: Increased training and fine tuning would be highly appreciated and I firmly believe that it would improve the ability of PyAutoCode significantly.* | |
| ## Some Model Features | |
| - Built on *GPT-2* | |
| - Tokenized with *ByteLevelBPETokenizer* | |
| - Data Sourced from *GitHub (almost 5 consecutive days of latest Python repositories)* | |
| - Makes use of *GPTLMHeadModel* and *DataCollatorForLanguageModelling* for training | |
| - Newline characters are custom coded as `<N>` | |
| ## Get a Glimpse of the Model | |
| You can make use of the **Inference API** of huggingface *(present on the right sidebar)* to load the model and check the result. Just enter any code snippet as input. Something like: | |
| ```sh | |
| for i in range( | |
| ``` | |
| ## Usage | |
| You can use my model too!. Here's a quick tour of how you can achieve this: | |
| Install transformers | |
| ```sh | |
| $ pip install transformers | |
| ``` | |
| Call the API and get it to work! | |
| ```python | |
| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| tokenizer = AutoTokenizer.from_pretrained("P0intMaN/PyAutoCode") | |
| model = AutoModelForCausalLM.from_pretrained("P0intMaN/PyAutoCode") | |
| # input: single line or multi-line. Highly recommended to use doc-strings. | |
| inp = """import pandas""" | |
| format_inp = inp.replace('\n', "<N>") | |
| tokenize_inp = tokenizer.encode(format_inp, return_tensors='pt') | |
| result = model.generate(tokenize_inp) | |
| decode_result = tokenizer.decode(result[0]) | |
| format_result = decode_result.replace('<N>', "\n") | |
| # printing the result | |
| print(format_result) | |
| ``` | |
| Upon successful execution, the above should probably produce *(your results may vary when this model is fine-tuned)* | |
| ```sh | |
| import pandas as pd | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| ``` | |
| ## Credits | |
| ##### *Developed as a part of a university project by [Pratheek U](https://www.github.com/P0intMaN) and [Sourav Singh](https://github.com/Sourav11902312lpu)* |