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
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# PyAutoCode: GPT-2 based Python auto-code.
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PyAutoCode is a cut-down python autosuggestion built on **GPT-2** (motivation: GPyT) model. This baby model 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**.
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# PyAutoCode: GPT-2 based Python auto-code.
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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**.
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*NOTE: Increased training and fine tuning would be highly appreciated and I firmly believe that it would improve the ability of PyAutoCode significantly.*
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## Some Model Features
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- Built on *GPT-2*
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- Tokenized with *ByteLevelBPETokenizer*
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- Data Sourced from *GitHub (almost 5 consecutive days of latest Python repositories)*
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- Makes use of *GPTLMHeadModel* and *DataCollatorForLanguageModelling* for training
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## Usage
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You can use my model too!. Here's a quick tour of how you can achieve this:
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```python
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```
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