Large language models (LLMs) are a type of artificial intelligence (AI) that are trained on massive datasets of text and code. This allows them to generate text, translate languages, write different kinds of creative content, and answer your questions in an informative way.
In recent years, there has been a growing interest in developing LLMs for coding applications. This is because LLMs can be used to generate code, debug code, and even write entire programs.
Open Source Coding LLM
Replit-code-v1-3b
Replit-code-v1-3b is a 2.7B parameter causal language model focused on code completion. It was developed by Replit in partnership with MosaicML, and it is trained on a subset of the Stack Dedup v1.2 dataset. The training dataset contains 175B tokens, which were repeated over 3 epochs. In total, replit-code-v1-3b has been trained on 525B tokens (~195 tokens per parameter).
Replit-code-v1-3b is powered by state-of-the-art LLM techniques, such as:
- Flash Attention for fast training and inference
- AliBi positional embeddings to support variable context length at inference time
- LionW optimizer, etc.
Replit-code-v1-3b is available for free under the CC BY-SA 4.0 license. It can be used for a variety of tasks, including:
- Code completion
- Code generation
- Code debugging
- Code linting
- Code documentation
StarCoder
StarCoder is a 15.5B parameter model trained on 80+ programming languages from The Stack (v1.2), with opt-out requests excluded. The model uses Multi Query Attention, a context window of 8192 tokens, and was trained using the Fill-in-the-Middle objective on 1 trillion tokens. The model was trained on GitHub code. As such it is not an instruction model and commands like “Write a function that computes the square root.” do not work well. However, by using the Tech Assistant prompt you can turn it into a capable technical assistant.
To use the Tech Assistant prompt, simply type “Tech Assistant” followed by your question or request. For example, you could type “Tech Assistant, what is the square root of 16?” or “Tech Assistant, how do I write a function that prints the Fibonacci sequence?” StarCoder will then generate a response that is relevant to your question or request.
StarCoder is still under development, but it has the potential to be a powerful tool for developers. It can be used to generate code, debug code, and even write entire programs. StarCoder can also be used as a technical assistant, providing developers with answers to their questions and helping them to solve problems.
Here are some of the features of StarCoder:
- It can generate code in over 80 programming languages.
- It can debug code.
- It can write entire programs.
- It can be used as a technical assistant.
Here are some of the strengths and weaknesses of StarCoder:
- Strengths:
- It is a powerful tool for developers.
- It can generate code, debug code, and even write entire programs.
- It can be used as a technical assistant.
- Weaknesses:
- It is still under development.
- It is not as accurate as some other LLMs.
- It can be difficult to use.
CodeGen
CodeGen is a family of autoregressive language models for program synthesis. It was developed by Salesforce and is based on the Transformer architecture. CodeGen is trained on a massive dataset of code and natural language descriptions, and it can generate code from natural language descriptions.
CodeGen has been shown to be effective at generating code from natural language descriptions. In a study, CodeGen was able to generate code that was as accurate as code generated by human experts. CodeGen is also able to generate code that is more efficient and easier to understand than code generated by human experts.
CodeGen is a promising new tool for program synthesis. It has the potential to revolutionize the way that software is developed. CodeGen can be used to generate code from natural language descriptions, which can save developers a lot of time and effort. CodeGen can also be used to generate more efficient and easier to understand code, which can improve the quality of software.
Here are some of the features of CodeGen:
- It can generate code from natural language descriptions.
- It is trained on a massive dataset of code and natural language descriptions.
- It has been shown to be effective at generating code from natural language descriptions.
- It is able to generate code that is as accurate as code generated by human experts.
- It is able to generate code that is more efficient and easier to understand than code generated by human experts.
Here are some of the strengths and weaknesses of CodeGen:
- Strengths:
- It can generate code from natural language descriptions.
- It is trained on a massive dataset of code and natural language descriptions.
- It has been shown to be effective at generating code from natural language descriptions.
- It is able to generate code that is as accurate as code generated by human experts.
- It is able to generate code that is more efficient and easier to understand than code generated by human experts.
- Weaknesses:
- It is still under development, so it may not be as accurate or efficient as human experts.
- It can be difficult to use, as it requires users to be familiar with natural language processing and programming languages.
CodeT5+
CodeT5+ is a large language model (LLM) that is specifically designed for code understanding and generation. It is based on the T5 architecture, but it has been extended to incorporate code-specific knowledge. CodeT5+ is trained on a massive dataset of code and natural language, which allows it to learn the relationships between code and its meaning.
Features
CodeT5+ has a number of features that make it well-suited for code understanding and generation tasks. These features include:
A large vocabulary of code tokens
The ability to understand the structure of code
The ability to generate code that is both correct and idiomatic
The ability to translate between code and natural language
Strengths
CodeT5+ has a number of strengths, including:
It can be used for a wide range of code understanding and generation tasks.
It is able to learn the relationships between code and its meaning.
It can generate code that is both correct and idiomatic.
It can translate between code and natural language.
Weaknesses
CodeT5+ has a few weaknesses, including:
It can be computationally expensive to train and use.
It is not as accurate as some other LLMs for some tasks.
It is still under development, so it may not be able to handle all code understanding and generation tasks.
