Code Auto Completion with Hugging Face LangChain and Phi3 SLM

 

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You can create your own coding auto-completion co-pilot using Hugging Face LangChain and Phi3 SLM! Here's a breakdown of the steps involved:

1. Setting Up the Environment:

• Install the required libraries:
  Bash

  pip install langchain transformers datasets phi3
  

• Download the Phi3 SLM model:
  Bash

  from transformers import AutoModelForSeq2SeqLM
  model_name = "princeton-ml/ph3_base"
  model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
  

2. Preprocessing Code for LangChain:

• LangChain provides a AutoTokenizer class to preprocess code. Identify the programming language you want to support and install the corresponding tokenizer from Hugging Face. For example, for Python:
  Bash

  from langchain.llms import AutoTokenizer
  tokenizer = AutoTokenizer.from_pretrained("openai/gpt-code-code")
  

• Define a function to preprocess code into LangChain format. This might involve splitting the code into tokens, adding special tokens (e.g., start/end of code), and handling context (previous lines of code).

3. Integrating Phi3 SLM with LangChain:

• LangChain allows creating custom prompts and completions. Leverage this to integrate Phi3 SLM for code completion suggestions.
• 
  
• Here's a basic outline:
  Python

  def generate_completion(code_input):
      # Preprocess code using tokenizer
      input_ids = tokenizer(code_input, return_tensors="pt")
  
      # Define LangChain prompt (e.g., "Write the next line of code: ")
      prompt = f"{prompt} {code_input}"
      prompt_ids = tokenizer(prompt, return_tensors="pt")
  
      # Generate outputs from Phi3 SLM using LangChain
      outputs = langchain.llms.TextLMRunner(model)(prompt_ids)
      generated_code = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
  
      return generated_code
  

4. Training and Fine-tuning (Optional):

• While Phi3 SLM is a powerful model, you can further enhance its performance for specific coding tasks by fine-tuning on a dataset of code and completions. This might involve creating a custom training loop using LangChain's functionalities.

5. User Interface and Deployment:

• Develop a user interface (UI) to accept code input from the user and display the generated completions from your co-pilot. This could be a web application or a plugin for an existing code editor.
• Explore cloud platforms or containerization tools (e.g., Docker) to deploy your co-pilot as a service.

Additional Tips:

• Refer to LangChain's documentation for detailed examples and usage guides: https://python.langchain.com/v0.1/docs/integrations/platforms/huggingface/
• Explore Hugging Face's model hub for various code-specific pre-trained models that you can integrate with LangChain: https://huggingface.co/models
• Consider incorporating error handling and edge cases in your code to make the co-pilot more robust.

Remember, this is a high-level overview, and you'll need to adapt and implement the code based on your specific requirements and chosen programming language.