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
Update README.md
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README.md
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@@ -19,10 +19,37 @@ PyAutoCode is a cut-down python autosuggestion built on **GPT-2** *(motivation:
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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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from transformers import AutoTokenizer, AutoModelForCausalLM
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tokenizer = AutoTokenizer.from_pretrained("P0intMaN/PyAutoCode")
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model = AutoModelForCausalLM.from_pretrained("P0intMaN/PyAutoCode")
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```
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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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Install transformers
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```sh
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$ pip install transformers
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```
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Call the API and get it to work!
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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tokenizer = AutoTokenizer.from_pretrained("P0intMaN/PyAutoCode")
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model = AutoModelForCausalLM.from_pretrained("P0intMaN/PyAutoCode")
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# input: single line or multi-line. Highly recommended to use doc-strings.
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inp = """import pandas"""
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format_inp = inp.replace('\n', "<N>")
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tokenize_inp = tokenizer.encode(format_inp, return_tensors='pt')
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result = model.generate(tokenize_inp)
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decode_result = tokenizer.decode(result[0])
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format_result = decode_result.replace('<N>', "\n")
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# printing the result
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print(format_result)
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```
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Upon successful execution, the above should probably produce *(your results may vary when this model is fine-tuned)*
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```sh
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import pandas as pd
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import numpy as np
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import matplotlib.pyplot as plt
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```
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