Python function call completion stable code 3b

A state-of-the-art language model called Stable Code 3B is presented by Stability AI with the goal of improving developers’ coding experience. This decoder-only transformer, with 2.7 billion parameters, offers cutting-edge performance in a wide range of programming languages, making it an effective tool for understanding and creating code.

Spiderchart Analysis

Stable Code 3B has been benchmarked against other prominent models in the field. In a spiderchart comparison, it exhibits strong capabilities across popular languages like Python, C++, JavaScript, Java, PHP, and Rust. The Fill in Middle Capability (FIM) is a standout feature, allowing developers to efficiently work with long contexts, sequences up to 16,384 tokens, and support for diverse programming languages.

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Model | Size | Python | C++ | Javascript | Java | PHP | Rust -----------------|------|--------|------|------------|-------|------|----- Stable Code 3B | 3B | 32.4% | 30.9%| 32.1% | 32.1% | 24.2%| 23.0% CodeLLama 7B | 7B | 30.0% | 28.2%| 32.5% | 31.1% | 25.7%| 26.3% ...

Key Features

Fill in Middle Capability (FIM)

Stable Code 3B shines with its Fill in Middle Capability, allowing developers to seamlessly fill in missing code snippets. This feature is invaluable for enhancing coding productivity and handling long sequences.

Long Context Support

The model supports long context sequences up to 16,384 tokens, providing developers with the ability to generate code in extensive and complex scenarios.

Usage Guide

Getting started with Stable Code 3B is straightforward. Utilize the provided code snippet to generate text, leveraging the Fill in Middle (FIM) feature. Additionally, explore the model’s capabilities by running with Fill in Middle (FIM) or Flash Attention 2.

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import torch from transformers import AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("stabilityai/stable-code-3b", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained( "stabilityai/stable-code-3b", trust_remote_code=True, torch_dtype="auto", ) model.cuda() inputs = tokenizer("import torch\nimport torch.nn as nn", return_tensors="pt").to(model.device) tokens = model.generate( **inputs, max_new_tokens=48, temperature=0.2, do_sample=True, ) print(tokenizer.decode(tokens[0], skip_special_tokens=True))

Model Architecture

Stable Code 3B employs a decoder-only transformer architecture with significant parameters, hidden size, layers, heads, and sequence length. The incorporation of Rotary Position Embeddings enhances throughput, contributing to its impressive performance.

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Parameters: 2,796,431,360 Hidden Size: 2560 Layers: 32 Heads: 32 Sequence Length: 16384

Python function call completion stable code 3b

Certainly! You may write a little code snippet that suggests autocompletion based on function signatures to aid developers in remembering Python function calls and arguments. The inspect module that comes with Python may be used to do this by retrieving details about functions and their arguments.

Here’s an example code snippet that demonstrates a basic function call autocompletion:

import inspect

def autocomplete_function(function_name):
    # Get the function object
    func = globals().get(function_name)

    if func is not None and inspect.isfunction(func):
        # Get the function signature
        signature = inspect.signature(func)
        parameters = list(signature.parameters.keys())

        # Return a string with suggested function call
        return f"{function_name}({', '.join(parameters)})"

    else:
        return f"Function '{function_name}' not found."

# Example functions
def example_function1(param1, param2):
    return param1 + param2

def example_function2(param1, param2, param3):
    return param1 * param2 - param3

# Example usage
function_name_input = input("Enter function name for autocompletion: ")
completion = autocomplete_function(function_name_input)
print(completion)

In this example, autocomplete_function takes a function name as input and checks if the function exists. If the function is found, it retrieves the function signature using the inspect module and provides a suggested function call string with parameter names.

You can extend this code based on your needs, such as incorporating it into an interactive development environment (IDE) or creating a more sophisticated tool to assist developers.

Training Details

Trained on a diverse dataset from open-source projects, Stability AI utilized a powerful infrastructure comprising 256 NVIDIA A100 40GB GPUs. The training process involved bfloat16 precision, AdamW optimization, and 2D parallelism with ZeRO-1.

Use and Limitations

Stable Code 3B is intended as a foundational base model for fine-tuning in specific applications. Developers are encouraged to evaluate and fine-tune the model for optimal performance in downstream tasks.

Limitations and Bias

Being a base model, users should be aware of potential unreliable or unsafe behaviours. The pre-training dataset, while filtered, may still contain offensive content. Caution is advised when deploying these models in production systems.

How to Cite

If you find Stable Code 3B useful in your research or projects, please cite it using the provided BibTeX entry:

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@misc{stable-code-3b, url={[https://huggingface.co/stabilityai/stable-code-3b](https://huggingface.co/stabilityai/stable-code-3b)}, title={Stable Code 3B}, author={Pinnaparaju, Nikhil and Adithyan, Reshinth and Phung, Duy and Tow, Jonathan and Baicoianu, James and Cooper, Nathan} }

FAQ’s

1. What is Stable Code 3B?

Stable Code 3B is an open-source, on-device AI model that assists developers with coding by suggesting completions and generating Python code based on context. It has three specialized models:

• Completion model: Suggests completions for single or multi-line code snippets.
• Instruction model: Generates code based on natural language prompts and instructions.
• Long context model: Handles large code blocks and complex contexts.

2. How does Stable Code 3B help with Python function call completion?

Stable Code 3B can help with Python function call completion in several ways:

• Autocompletes function names: As you start typing a function name, Stable Code 3B will suggest relevant functions based on the context.
• Shows function signatures: When you hover over a suggested function, Stable Code 3B displays its signature (parameter names and types).
• Generates code for arguments: Based on the function and context, Stable Code 3B can suggest or generate code for the function arguments.
• Provides documentation: Stable Code 3B can access and display documentation for suggested functions, helping you understand their usage.

3. What are the advantages of using Stable Code 3B for Python function call completion?

• Saves time and effort: Autocompleting function names and arguments can significantly speed up your coding workflow.
• Reduces errors: Stable Code 3B’s suggestions can help prevent typos and incorrect function calls.
• Improves code quality: The generated code can be well-formatted and follow best practices.
• Learns from your codeStable Code 3B vs. GitHub Copilot: A Detailed Comparison: Stable Code 3B adapts to your coding style and preferences over time, providing more personalized suggestions.

4. What are the limitations of using Stable Code 3B for Python function call completion?

• Accuracy: As a smaller model, Stable Code 3B might not always be accurate, especially with complex functions or contexts.
• Open-source nature: The open-source nature can lead to potential biases or issues in the model’s suggestions.
• Limited natural language support: Compared to GitHub Copilot, Stable Code 3B’s interaction through natural language prompts is less advanced.
• Technical requirements: Running Stable Code 3B requires some technical knowledge and setup.

5. How can I get started with Stable Code 3B for Python function call completion?

Stable Code 3B is available through various Python libraries and IDE integrations. Here are some resources to get started:

• Official website: https://stability.ai/news/stablecode-llm-generative-ai-coding
• GitHub repository: https://huggingface.co/stabilityai/stable-code-3b
• Jupyter notebook tutorial: https://colab.research.google.com/
• PyCharm integration: https://huggingface.co/TheBloke/stablecode-instruct-alpha-3b-GPTQ