When did you last struggle to make sense of a new programming language or framework? Developers frequently face steep learning curves and overwhelming documentation when adopting new technologies. Fortunately, large language models (LLMs) can help ease this burden by understanding natural language prompts and producing human-like text, enabling developers to integrate new tools more efficiently. This article will help you identify the best multimodal LLM for coding so you can enhance your team’s development process and improve coding efficiency and innovation.
Lamatic’s Generative AI tech stack offers a robust solution to help you achieve your goals. It provides customizable AI tools to help you identify the best LLM for coding that meets your and your team's needs.
What is an LLM for Coding?
A Large Language Model for Coding is an artificial intelligence tool that helps developers write code faster and with fewer errors. Trained on vast datasets of publicly available code, LLMs can generate snippets, whole functions, and modules based on simple prompts.
They excel at understanding and predicting human language, which enables them to translate natural language instructions into programming code. The latest LLMs can also analyze existing code to identify bugs and suggest improvements, making them powerful assistants for debugging and code optimization tasks.
How Do Coding Assistants Work?
At their core, coding assistants powered by LLMs are designed to improve productivity and efficiency in software development. Here's how they generally function:
- Training Data: LLMs are trained on extensive datasets that consist of publicly available code repositories, technical documentation, and other relevant sources.
- Code Generation: When given a prompt or instruction, the LLM can generate code snippets, functions, or entire modules relevant to the context.
- Inline Suggestions: Many coding assistants can be integrated into popular Integrated Development Environments (IDEs) to provide real-time code suggestions as developers work.
- Debugging Assistance: LLMs can analyze existing code to identify potential errors, suggest fixes, and explain why certain solutions may work better.
- Dynamic Learning: Some LLMs can be fine-tuned with specific organizational data, allowing them to contextualize their advice according to the unique coding practices of a team or project.
Why are LLMs Important for Developers?
1. Increased Efficiency
LLMs allow developers to focus on more strategic tasks by handling repetitive coding operations. Whether generating boilerplate code or completing functions based on a few instructions, LLMs drastically reduce the time it takes to write and debug code.
2. Improved Accuracy
LLMs reduce human errors, especially in syntax-heavy programming languages like Python, Java, or C++. LLMs can help developers avoid common coding mistakes by providing real-time suggestions and syntax corrections.
3. Enhanced Learning and Documentation
For new programmers, LLMs can act as real-time tutors. They can explain code simply, helping developers understand how different functions work. These models can generate documentation, which is crucial for team projects and maintaining clean, understandable code.
4. Better Collaboration
Teams using LLMs for coding can collaborate better as these models help maintain coding standards across members. By generating uniform code and offering consistent suggestions, LLMs ensure that everyone on the team writes cleaner, more maintainable code.
Related Reading
- LLM Security Risks
- What is an LLM Agent
- AI in Retail
- LLM Deployment
- How to Run LLM Locally
- How to Use LLM
- LLM Model Comparison
- AI-Powered Personalization
- How to Train Your Own LLM
17 Best LLM for Coding Faster and Smarter
1. GitHub Copilot: Your AI-Powered Coding Assistant
Originally released in October 2021, GitHub Copilot is a version of Microsoft’s Copilot LLM specifically trained with data to assist coders and developers with their work to improve efficiency and productivity.
While the original release used OpenAI’s Codex model, a modified version of GPT-3 which was also trained as a coding assistant, GitHub Copilot was updated to use the more advanced GPT-4 model in November 2023. A core feature of GitHub Copilot is the extension provided that allows direct integration of the LLM into commonly used Integrated Development Environments (IDEs) popular among developers today, including:
- Visual Studio Code
- Visual Studio
- Vim
- Neovim
- The JetBrains suite of IDEs
- Azure Data Studio
This direct integration allows GitHub Copilot to access your existing project to improve the suggestions made when given a prompt while also providing users with hassle-free installation and access to the features provided.
Maximizing Code Efficiency with GitHub Copilot
For enterprise users, the model can also be granted access to existing repositories and knowledge bases from your organization to further enhance the quality of outputs and suggestions. When writing code, GitHub Copilot can offer suggestions in a few different ways.
- You can write a prompt using an inline comment that can be converted into a block of code. This works in a similar way to how you might use other LLMs to generate code blocks from a prompt, but with the added advantage of GitHub Copilot being able to access existing project files to use as context and produce a better output.
- GitHub Copilot can provide real-time suggestions as you are writing your code. For example, if you are writing a regex function to validate an email address, simply starting to write the function can offer an autocomplete suggestion that provides the required syntax.
- You can also use the GitHub Copilot Chat extension to ask questions, request suggestions, and help you debug code more context-awarely than you might get from LLMs trained on more broad datasets. Users can enjoy unlimited messages and interactions with GitHub Copilot’s chat feature across all subscription tiers.
GitHub Copilot is trained using data from publicly available code repositories, including GitHub itself.
Enhancing Code Assistance with GitHub Copilot
GitHub Copilot claims it can provide code assistance in any language where a public repository exists. The quality of the suggestions will depend on the volume of data available. All subscription tiers include a public code filter to reduce the risk of suggestions directly copying code from a public repository.
By default, GitHub Copilot excludes submitted data from being used to train the model further for business and enterprise tier customers and offers the ability to exclude files or repositories from being used to inform suggestions offered. Administrators can configure both features as needed based on your business use cases.
Ensuring Responsible Use of GitHub Copilot for Code Assistance
While these features aim to keep your data private, it’s worth keeping in mind that prompts aren’t processed locally and rely on external infrastructure to provide code suggestions. You should factor this into whether this is the right product for you. Users should also be cautious about implicitly trusting any outputs.
While the model is generally very good at providing suggestions, like all LLMs it is still prone to hallucinations and can make poor or incorrect suggestions. Always review any code generated by the model to ensure it does what you intend it to do. In the future it’s possible that GitHub will upgrade GitHub Copilot to use the recently released GPT-4o model.
GitHub Copilot Updates and Pricing Plans for 2024
GPT-4 was originally released in March 2023, and GitHub Copilot was updated to use the new model roughly seven months later. Given the:
- Improved intelligence
- Reduced latency
- With the reduced cost to operate GPT-4o
It makes sense to update the model further, though there has yet to be an official announcement. If you want to try before you buy, GitHub Copilot offers a free 30-day trial of its cheapest package, which should be sufficient to test out its capabilities. After that, there is a $10 per month fee. Copilot Business costs $19 per user, while Enterprise costs $39 monthly.
2. CodeQwen1.5: Alibaba’s Open-Source Coding Assistant
CodeQwen1.5 is a version of Alibaba’s open-source Qwen1.5 LLM specifically trained using public code repositories to assist developers in coding related tasks. This specialized version was released in April 2024, a few months after the release of Qwen1.5 to the public in February 2024. There are two different versions of CodeQwen1.5 available today:
- The base model of CodeQwen1.5 is designed for code generation
- Suggestions but limited chat functionality, while the second version can also be used as a chat interface that can answer questions more humanistically.
Both models have been trained with 3 trillion tokens of code-related data and support a respectable 92 languages, including some of the most common languages in use today such as:
- Python
- C++
- Java
- PHP
- C#
- JavaScript
Unlike the base version of Qwen1.5, which has several different sizes available for download, CodeQwen1.5 is only available in a single size: 7B. While this is quite small compared to other models on the market that can also be used as coding assistants, there are a few advantages that developers can take advantage of.
Evaluating CodeQwen1.5 as a Cost-Effective Alternative for Coding Assistance
Despite its small size, CodeQwen1.5 performs incredibly well compared to some larger models offering open and closed-source coding assistance. CodeQwen1.5 comfortably beats GPT3.5 in most benchmarks and provides a competitive alternative to GPT-4, though this can sometimes depend on the specific programming language.
While GPT-4 may perform better overall by comparison, it’s important to remember that GPT-4 requires a subscription and has per-token costs that could make using it very expensive compared to CodeQwen1.5. GPT -4 cannot be hosted locally. Like with all LLMs, it's risky to trust any suggestions or responses the model provides implicitly.
Local Usage and Hardware Needs for CodeQwen1.5
While steps have been taken to reduce hallucinations, always check the output to ensure it is correct. As CodeQwen1.5 is open source, you can download a copy of the LLM to use at no additional cost beyond the hardware needed to run it.
You’ll still need to ensure your system has enough resources to run the model well, but the smaller model size's bonus means a modern system with a GPU with at least 16GB of VRAM and at least 32GB of system RAM should be sufficient.
Evaluating CodeQwen1.5 as a Cost-Effective Alternative for Coding Assistance
CodeQwen1.5 can also be trained using code from existing projects or other code repositories to improve the context of the generated responses and suggestions. The ability to host CodeQwen1.5 within your own local or remote infrastructure, such as a Virtual Private Server (VPS) or dedicated server, also helps to alleviate some of the concerns related to data privacy or security often connected to submitting information to third-party providers.
Alibaba surprised us by releasing their new Qwen2 LLM at the start of June. They claim it offers significant gains over the base model of Qwen1.5. Alibaba also mentioned that the training data used for CodeQwen1.5 is included in Qwen2-72B. It has the potential to offer improved results, but it’s currently unclear if there is a plan to upgrade CodeQwen to use the new model.
3. LLama 3: Affordable LLM with Impressive Coding Abilities
Regarding the best bang for the buck, Meta’s open-source Llama 3 model, released in April 2024, is one of the best low-cost models available today. Unlike many other models specifically trained with code-related data to assist developers with coding tasks, Llama 3 is a more general LLM capable of assisting in many ways – one of which also happens to be as a coding assistant – and outperforms CodeLlama, a coding model released by Meta in August 2023 based on Llama 2.
In like-for-like testing with models of the same size, Llama 3 outperforms CodeLlama by a considerable margin regarding code generation, interpretation, and understanding. This is impressive, considering Llama 3 wasn’t trained specifically for code-related tasks but can still outperform those that have. This means that you can use Llama 3 to improve efficiency and productivity when performing coding tasks and can also be used for other tasks.
Llama 3 Capabilities and Hardware Requirements for Deployment
Llama 3 has a training data cutoff of December 2023, which isn’t always of critical importance for code-related tasks. Some languages can develop quickly and having the most recent data available can be incredibly valuable. Llama 3 is an open-source model that allows developers to download and deploy the model to their local system or infrastructure.
Like CodeQwen1.5, Llama 3 8B is small enough that a modern system with at least 16GB of VRAM and 32GB of system RAM is sufficient to run the model. The larger 70B version of Llama 3 naturally has better capabilities due to the increased parameter number. Still, the hardware requirement is an order of magnitude greater and would require a significant injection of funds to build a system capable of running it effectively.
Maximizing Value with Llama 3 8B and Scalable Hosting Options
The Llama 3 8B offers enough capability that users can get excellent value without breaking the bank simultaneously. Suppose you need the added capability of the larger model. In that case, the open-source nature of the model means you can easily rent an external VPS or dedicated server to support your needs, though costs will vary depending on the provider.
Suppose you decide that you’d like the increased capability of the larger model, but the investment needed for the required hardware, or the cost to rent an external host, is outside your budget. In that case, AWS offers API access to the model via a pay as you go plan which charges you by the token instead. AWS currently charges $3.50 per million output tokens, a considerable quantity for a very small price.
Evaluating Llama 3 for Code Generation and Flexible Hosting Solutions
OpenAI’s GPT-4o costs $15.00 for the same quantity of tokens. If this type of solution appeals to you, shop for the best location, budget, and needs provider. Llama 3 performs well in code generation tasks and adheres well to the prompts.
It will sometimes simplify the code based on the prompt, but it's reasonably receptive to being instructed to provide a complete solution. If requested, it will segment if it reaches the token limit for a single response. During testing, we asked Llama 3 to write a complete solution in Python for a chess game that would immediately compile and could be played via text prompts, and it dutifully provided the requested code.
Llama 3's Code Debugging Capabilities
Although the code initially failed to compile, providing Llama 3 with the error messages from the compiler allowed it to identify where the mistakes were and provided a correction. Llama 3 can effectively debug code segments to identify issues and provide new code to fix the error.
It can also explain where the error was located and why it needs to be fixed to help the user understand the mistake. Like all models generating code-related solutions, it's important to check the output and not trust it implicitly.
The Limitations of AI Models
Although the models are becoming increasingly intelligent and accurate, they also hallucinate at times and provide incorrect or insecure responses. Like other open-source models, any data you submit to train Llama 3 from your code repositories remains within control. This helps to alleviate some of the concerns and risks associated with submitting proprietary and personal data to third parties.
However, remember to also consider what that means for your information security policies where required. Training a model you have hosted within your infrastructure costs nothing extra. Some hosts providing API access do incur additional costs associated with further training.
4. Claude 3 Opus: The Latest Model from Anthropic
Released in April 2024, Claude 3 Opus is the latest and most capable LLM from Anthropic. They claim it is the most intelligent LLM on the market today and is designed to tackle various tasks. Although most LLMs can generate code, the accuracy and correctness of the generated outputs can vary. They may have mistakes or need to be corrected because they were specifically designed with code generation in mind.
Claude 3 Opus bridges that gap by being trained to handle coding-related tasks alongside the regular tasks LLMs are often used for, making for a very powerful multi-faceted solution. While Anthropic doesn’t mention how many programming languages it supports, Claude 3 Opus can generate code across a wide range of programming languages, from incredibly popular languages such as:
- C++
- C#
- Python
- Java
To older or more niche languages such as:
- FORTRAN
- COBOL
- Haskell
Claude 3 Opus A Powerful Tool with Limitations
Claude 3 Opus relies on the patterns, syntaxes, coding conventions, and algorithms identified within the code-related training data to generate new code snippets from scratch, helping to avoid direct reproduction of the code used to train it. The large 200k token context window offered by Claude 3 Opus is incredibly useful when working with large code blocks as you iterate through suggestions and changes.
Like all LLMs, Claude 3 Opus also has an output token limit, which tends to summarise or truncate the response to fit within a single reply. While summarization of a pure text response isn’t too problematic as you can ask for additional context, not being provided with a large chunk of required code, such as when generating a test case, is quite a problem.
Level Up Your Coding with AI-Powered Explanations
Claude 3 Opus can segment its responses if you request it to do so in your initial prompt. You’ll still need to ask it to continue after each reply, but this does allow you to obtain more long-form responses where needed.
In addition to generating functional code, Claude 3 Opus also adds comments to the code and explains what the generated code does to help developers understand what is happening. In cases where you are using Claude 3 to debug code and generate fixes, this is extremely valuable as it not only helps solve the problem but also provides context as to why changes were made or why the code was generated in this specific way.
Privacy, Security, and Limitations of Claude 3 Opus
For those concerned about privacy and data security, Anthropic states that it doesn’t use any of the data submitted to Claude 3 to train the model further, a welcome feature that many will appreciate when working with proprietary code. It also includes copyright indemnity protections with its paid subscriptions.
Claude 3 Opus has some limitations in improving the context of responses as it doesn’t currently offer a way to connect your knowledge bases or codebases for additional training. This probably isn’t a deal breaker for most, but it could be worth considering when choosing the right LLM for your code generation solution. This comes with a hefty price tag compared to other LLMs offering code generation functionality.
How Much Will It Cost You to Use Claude 3 Opus?
API access is one of the more expensive ones on the market, at an eye-watering $75 per 1 million output tokens, considerably more than GPT-4o’s $15 price tag. Anthropic does offer two additional models based on:
- Claude 3
- Haiku
- Sonnet
They are much cheaper at $15 and $1.25 for the same quantity of tokens, though they have reduced capability compared to Opus.
In addition to API access, Anthropic offers three subscription tiers that grant access to Claude 3. The free tier has a lower daily limit and only grants access to the Sonnet model, but should give those looking to test its capabilities a good idea of what to expect. To access Opus, you must subscribe to Pro or Team for $20 and $30 per person per month. The Team subscription does need a minimum of 5 users for a total of $150 per month but increases the usage limits for each user compared to the Pro tier.
5. GPT-4: The Best All-Around LLM for Code
GPT-4 is the most capable and versatile model from OpenAI, released in March 2023 as an update to GPT-3.5. It’s not specifically designed to assist with coding tasks. However, it performs exceptionally well across a broad range of code-related tasks, including:
- Real-time code suggestions
- Generating blocks of code
- Writing test cases
- Debugging errors in code
GitHub Copilot has also been using a version of GPT-4 with additional training data since November 2023, leveraging its human response capabilities for code generation and within its chat assistant. This should give you an idea of the value it can provide.
GPT-4 has been trained with code-related data covering many different programming languages and coding practices to help it understand the vast array of:
- Logic flows
- Syntax rules
- Programming paradigms developers use
This allows GPT-4 to excel when debugging code by helping solve various issues commonly encountered by developers. Syntax errors can be incredibly frustrating when working with some languages - I’m looking at you and your indentations,
Python
Using GPT-4 to review your code can massively speed up the process when code doesn’t compile due to difficult-to-find errors. Logical errors are among the toughest to debug, as code usually compiles correctly but doesn’t provide the correct output or operate as desired.
By giving GPT-4 your code and explaining what it should be doing, GPT-4 can analyze and identify where the problem lies, offer suggestions or rewrites to solve it, and even explain the problem and how the suggested changes solve it. This can help developers quickly understand the cause of the problem and offer an opportunity to learn how to avoid it again.
Privacy Concerns and Data Usage with GPT-4
Although the training data cutoff for GPT-4 is September 2021, which is quite a long time ago considering the advancements in LLMs over the last year, GPT-4 is continuously trained using new data from user interactions. This allows GPT-4’s debugging to become more accurate over time.
This presents potential risks regarding the code you submit for analysis, especially when using it to write or debug proprietary code. Users can opt out of their data being used to train GPT-4 further, but it's not something that happens by default, so keep this in mind when using GPT-4 for code-related tasks.
GPT-4 vs. GPT-4o: A Cost-Benefit Analysis for Code-Related Tasks
You might be wondering why the recommendation here is to use GPT-4 when it is 4 times more expensive than the newer, cheaper, and more intelligent GPT-4o model released in May 2024. In general, GPT-4o has proven to be a more capable model. Still, for code-related tasks, GPT-4 tends to provide better responses that are more correct, adhere to the prompt better, and offer better error detection than GPT-4o.
The gap is small and GPT-4o will likely become more capable and overtake GPT-4 as the model matures further through additional training from user interactions. If cost is a major factor in your decision, GPT-4o is a good alternative that covers most of what GPT-4 can provide at a much lower cost.
6. Mistral 7B & Mixtral 8X7B
Mistral 7B and Mixtral 8x7B are two open-source language models developed by Mistral AI, both released under the Apache 2.0 license. Mistral 7B is a 7.3B parameter model that outperforms Llama 2 13B on all benchmarks and surpasses Llama 1 34B on many tasks.
It approaches the performance of CodeLlama 7B on coding tasks while maintaining strong performance in English-language tasks. Mistral 7B uses techniques like Grouped Query Attention (GQA) for faster inference and Sliding Window Attention (SWA) to efficiently handle longer sequences.
A Powerful and Efficient LLM for Code Generation
Mixtral 8x7B is a larger, 46.7B parameter Sparse Mixture-of-Experts (SMoE) model. Despite its high parameter count, it only uses 12.9B parameters per token, allowing it to process input and generate output at the same speed and cost as much as a 12.9B model.
Mixtral 8x7B matches or outperforms Llama 2 70B on most benchmarks. Both models demonstrate strong performance on coding-related tasks:
- 1. Mistral 7B approaches CodeLlama 7 B's performance on code generation tasks while maintaining its proficiency in English-language tasks.
- 2. Mixtral 8x7B shows strong performance in code generation. The models can be easily fine-tuned for various tasks.
For example, Mistral 7B was fine-tuned on publicly available instruction datasets to create Mistral 7B Instruct, which outperforms all 7B models on the MT-Bench benchmark.
7. CodeLlama: Meta’s Code Generation Model
CodeLlama by Meta is a state-of-the-art large language model (LLM) designed for code generation and natural language tasks related to code. It is built on top of Llama 2 and is available in three versions:
- 1. CodeLlama: The foundational code model.
- 2. CodeLlama - Python: Specialized for Python programming.
- 3. CodeLlama - Instruct: Fine-tuned for understanding natural language instructions.
Four sizes of CodeLlama have been released:
- 7B
- 13B
- 34B
- 70B parameters
The models are trained on a massive dataset of code and code-related data:
- -7B
- 13B
- 34B
Are trained on 500B tokens of code and code-related data.
- 70B model is trained on 1T tokens
The 7B and 13B base and instruct models have also been trained with fill-in-the-middle (FIM) capability, allowing them to insert code into existing code for tasks like completion.
CodeLlama - Python
CodeLlama - Python is further fine-tuned on 100B tokens of Python code, while CodeLlama - Instruct is instruction fine-tuned and aligned to understand human prompts better.
In benchmark tests using HumanEval and Mostly Basic Python Programming (MBPP), CodeLlama outperformed state-of-the-art publicly available LLMs on code tasks. CodeLlama 34B scored 53.7% on HumanEval and 56.2% on MBPP, the highest among open-source solutions. The models are released under the same community license as Llama 2, and the training recipes and model weights are available on GitHub.
Models Available:
- CodeLlama-34b-Instruct-hf
- CodeLlama-13b-Instruct-hf
- CodeLlama-7b-Instruct-hf
- CodeLlama-70b-Instruct-hf
- CodeLlama-70b-Python-hf
- CodeLlama-70b-hf
- CodeLlama-7b-hf
- CodeLlama-13b-hf
- CodeLlama-34b-hf
- CodeLlama-7b-Python-hf
- CodeLlama-13b-Python-hf
- CodeLlama-34b-Python-hf
8. Phind-CodeLlama: Fine-Tuned CodeLlama
Models Phind, an AI company, has fine-tuned two models, CodeLlama-34B, and CodeLlama-34B-Python, using their internal dataset. The resulting models, named:
- Phind-CodeLlama-34B-v1
- Phind-CodeLlama-34B-Python-v1
Have achieved impressive results on the HumanEval benchmark, scoring 67.6% and 69.5% pass@1, respectively. Phind's dataset consists of approximately 80,000 high-quality programming problems and solutions, structured as instruction-answer pairs rather than code completion examples.
Efficient Training and Validation
The models were trained over two epochs, totaling around 160,000 examples, using native fine-tuning without LoRA. The training process was optimized using DeepSpeed ZeRO 3 and Flash Attention 2, allowing the models to be trained in just three hours using 32 A100-80GB GPUs with a sequence length of 4096 tokens.
To ensure the validity of their results, Phind applied the decontamination methodology to their dataset. This methodology involves sampling substrings from each evaluation example and checking for matches in the processed training examples. No contaminated examples were found in Phind's dataset.
- Phind-CodeLlama-34B-v2 is a newer version, which was initialized from
- Phind-CodeLlama-34B-v1 and trained on an additional 1.5 billion tokens.
This new model achieved an even higher score of 73.8% pass@1 on the HumanEval benchmark, further demonstrating the effectiveness of Phind's fine-tuning approach.
Models Available:
- Phind-CodeLlama-34B-v2
- Phind-CodeLlama-34B-v1
- Phind-CodeLlama-34B-Python-v1
9. StarCoder & StarCoder2: BigCode Models for Code
StarCoder and StarCoder2 are two large language models developed by the BigCode project, an open scientific collaboration focused on the responsible development of Large Language Models for Code (Code LLMs).
StarCoder
StarCoder is a 15.5B parameter model with an 8K context length, infilling capabilities, and fast large-batch inference enabled by multi-query attention. It is built upon StarCoderBase, which was trained on 1 trillion tokens from The Stack. It is an extensive collection of permissively licensed GitHub repositories with inspection tools and an opt-out process. StarCoder is a fine-tuned version of StarCoderBase, trained on an additional 35B Python tokens.
StarCoder2
StarCoder2 is built upon The Stack v2, 4× larger than the first StarCoder dataset, in partnership with Software Heritage (SWH). The Stack v2 contains over 3B files in 600+ programming and markup languages derived from the Software Heritage archive.
Starcoder2 Models Come in Three Sizes:
- 3B
- 7B
- 15B parameters
Trained on 3.3 to 4.3 trillion tokens. StarCoder2-3B outperforms other Code LLMs of similar size on most benchmarks and also outperforms StarCoderBase-15B.
Models Available:
- StarCoder2-15b
- StarCoder2-7b
- StarCoder2-3b
- StarCoder
- StarCoderBase
10. WizardCoder: Improved Code Generation
WizardCoder is a code large language model (LLM) that enhances the open-source StarCoder model through complex instruction fine-tuning using the Evol-Instruct method adapted for code. WizardLM introduced the Evol-Instruct method, a technique for generating more complex and diverse instruction data to improve the fine-tuning of language models. The key idea is to “evolve” an existing dataset of instructions by iteratively applying various transformations to make the instructions more challenging and varied.
Available Models:
- WizardCoder-Python-34B-V1.0
- WizardCoder-15B-V1.0
- WizardCoder-Python-13B-V1.0
- WizardCoder-Python-7B-V1.0
- WizardCoder-3B-V1.0
- WizardCoder-1B-V1.0
- WizardCoder-33B-V1.1
11. Solar-10.7B: Strong Instruction
Follower for Code SOLAR 10.7B is a large language model with 10.7 billion parameters demonstrating strong performance in various natural language processing tasks. The model was initialized from the pre-trained weights of Mistral 7B. For fine-tuning, SOLAR 10.7B underwent a two-stage process:
- Instruction tuning
- Alignment tuning
The instruction tuning stage utilized mostly open-source datasets such as Alpaca-GPT4 and OpenOrca and a synthetically generated math question-answering dataset called “Synth. Math-Instruct.”
In the alignment tuning stage, the model was fine-tuned using human preference data from datasets like Orca DPO Pairs, Ultrafeedback Cleaned, and a synthesized math alignment dataset called “Synth. Math-Alignment”. The resulting instruction-tuned and alignment-tuned model, SOLAR 10.7B-Instruct, outperforms larger models like Mixtral 8x7B-Instruct on benchmark tasks, demonstrating the effectiveness of the training approach.
12. DeepSeek Coder V2.5: A Versatile Coding Model
DeepSeek Coder V2.5 is part of the DeepSeek Coder series, a range of code language models developed by DeepSeek AI. These models are notable for their significant size and comprehensive training data, which includes a blend of code and natural language. They are also among the cheapest coding models.
Key Features:
- Training Data: Trained on a vast dataset, the model covers multiple domains, including math, code, and reasoning, with context support up to 128K tokens.
- Model Variants: DeepSeek Coder includes a 236B parameter model optimized for various applications.
- Advanced Code Completion: Enhanced code completion capabilities with state-of-the-art performance in benchmarks like AlignBench and MT-Bench.
- Cost-Effectiveness: Competitive pricing at $0.14 per million input tokens and $0.28 per million output tokens. Development and Usage Initially based on the foundational DeepSeek-Coder-Base models, DeepSeek-Coder-33b-instruct was further fine-tuned with an additional 2 billion tokens of instruction data.
This fine-tuning has enhanced its capabilities, particularly in instruction-based tasks. Its remarkable performance metrics indicate its suitability for various coding-related applications, including complex project-level coding tasks.
Pros and Cons Pros
Versatility in handling multiple programming languages. High performance in code generation and problem-solving tasks. Flexible model sizes for different computational needs.
Cons
The substantial model size may require significant computational resources. Complexities in fine-tuning for specific tasks or languages may be challenging for some users. Terms of Use and Privacy Policy Concerns (Grant full license to use and reproduce inputs and outputs)
13. WizardCoder-Python-34B-V1.0: Fine-Tuned for Python
WizardCoder-Python-34B-V1.0 is a highly advanced code generation model, part of the WizardCoder series developed by WizardLM. It is specifically fine-tuned to understand and execute complex coding instructions, making it a significant tool in the coding LLMs space.
Key Features:
- Advanced Coding Capabilities: WizardCoder-Python-34B-V1.0 excels in coding-related tasks like code generation, completion, and summarization.
- Evol-Instruct Method: This model utilizes Evol-Instruct, an evolutionary algorithm, to generate a diverse set of complex instructions, enhancing the model’s performance in understanding and executing coding tasks.
- High Performance: It has shown impressive results on code generation benchmarks such as HumanEval, surpassing many other models, including:
- GPT-4
- ChatGPT-3.5
- Versatile Applications: It is suitable for various coding tasks, including:
- Automating DevOps scripts
- Data analysis
- Machine learning pipeline generation
- Web scraping
- API development
- Blockchain programming
Development and Usage
The development of WizardCoder-Python-34B-V1.0 involved training on an extensive dataset, with the fine-tuning process designed to improve its ability to generate coherent and relevant responses to a range of coding instructions. This model has been validated on several coding benchmarks and has demonstrated superior performance compared to other open-source and closed LLMs on these benchmarks.
Pros and Cons Pros
Exceptional ability to handle complex coding instructions. Versatility in various programming-related tasks. High performance on multiple coding benchmarks.
Cons
The complexity of the model may require significant computational resources for effective use. It might have limitations outside the specific domain of code generation and completion.
14. Moe-2x7b-QA-Code: Get Answers to Coding Questions
The Moe-2x7b-QA-Code is a top-notch language model perfect for answering and handling code-related queries. It uses a Mixture of Experts (MoE) architecture and has been trained on various technical data, including documentation, forums, and code repositories. This makes it very accurate and context-aware.
This model is a great fit for the article “Best LLM For Coding.” It’s specialized in code-related queries, making it a fantastic resource for readers interested in coding. Being open-source, it’s accessible to everyone. Its high performance in understanding language shows its effectiveness. So, if you’re looking for a helpful tool in coding, Moe-2x7b-QA-Code is a great choice.
15. Stable Code 3B: Compact Model Excels at Code Completion
Stable Code 3B is a state-of-the-art large language model (LLM) developed by Stability AI. It is a 3 billion-parameter model that allows accurate and responsive code completion. This model is on par with models such as CodeLLaMA 7b, which are 2.5 times larger.
Key Features:
- One of the key features of Stable Code 3B is its ability to operate offline even without a GPU on common laptops such as a MacBook Air. This model was trained on software engineering-specific data, including code.
- It offers more features and significantly better performance across multiple languages.
- Stable Code 3B supports Fill in the Middle capabilities (FIM) and expanded context size. It is trained on 18 programming languages (selected based on the 2023 StackOverflow Developer Survey) and demonstrates state-of-the-art performance on the MultiPL-E metrics across multiple programming languages tested.
In the range of models with 3 billion to 7 billion parameters, Stable Code 3B stands out as one of the best due to its high-level performance and compact size. It is 60% smaller than CodeLLaMA 7b while offering similar performance, making it a top choice for developers seeking efficient and effective code completion tools.
16. OctoCoder: Advanced Coding Model
OctoCoder is an advanced AI coding language model boasting an impressive 15.5 billion parameters. This model is the result of refining StarCoder through instruction tuning, which was trained on CommitPackFT and OASST datasets, as elucidated in the OctoPack research paper.
OctoCoder is a polyglot proficient in over 80 programming languages, making it a versatile tool for various coding tasks. While its extensive capabilities are remarkable, it’s worth noting that OctoCoder’s resource requirements might vary across hardware setups, although achieving functionality on different systems is feasible with appropriate configuration.
17. Wavecoder-ultra-6.7b: Specialized Model for Coding
Tasks WaveCoder-Ultra-6.7B by Microsoft is a real powerhouse. This bad boy uses a fancy way of learning (instruction-following, they call it) to tackle those pesky coding problems. Trained on a bunch of super-useful code snippets, WaveCoder-Ultra-6.7B can handle four major coding tasks like a boss:
- Code Generation: Need some fresh code written? Tell this thing what you want, and it’ll whip it up for you in no time.
- Code Summary: Don’t have time to untangle a giant mess of code? WaveCoder-Ultra-6.7B can break it down into a clear and short summary.
- Code Translation: Talking to a computer in the wrong language? No problem! This LLM can translate your code from one programming language to another.
- Code Repair: Got a bug in your code acting like a gremlin? WaveCoder-Ultra-6.7B can find and fix those errors for you, like a code-cleaning superhero.
The results show that WaveCoder-Ultra-6.7B scores a super high 79.9 on this “HumanEval” thing, which means it’s good at understanding code, just like a human would. It also does well in different areas, like explaining code (scoring 45.7) and fixing it (with a score of 52.3). It might not be the absolute best at everything (looking at you, GPT-4), but WaveCoder-Ultra-6.7B is an excellent option because it focuses specifically on code.
Related Reading
- How to Fine Tune LLM
- How to Build Your Own LLM
- LLM Function Calling
- LLM Prompting
- What LLM Does Copilot Use
- LLM Evaluation Metrics
- LLM Use Cases
- LLM Sentiment Analysis
- LLM Evaluation Framework
- LLM Benchmarks
How to Choose the Best LLM for Coding
Model Size and Architecture: Which LLM Will Meet Your Needs?
When selecting LLMs for coding, start with the model size and architecture. Larger models often yield better performance but require more computational resources. If you have limited hardware infrastructure, a smaller model might be a better option to ensure you can run your LLM smoothly.
Another aspect to consider is the model's architecture. Most LLMs have a transformer architecture, which uses attention mechanisms to understand complex prompts and generate accurate responses. An LLM with a larger and more advanced architecture will likely perform better on your coding tasks.
Scalability Needs: Can the LLM Grow With Me?
Assessing the number of users and the volume of models required to meet your operational demands is crucial. Suppose your application needs to scale dynamically or you plan to use the data to refine your models further. In that case, a cloud-based solution might offer the flexibility and scalability required to accommodate these needs efficiently.
Data Privacy and Security Requirements: Will My Data Be Safe?
An on-premises deployment could be essential for organizations operating in sectors where data privacy and security are paramount and where compliance with stringent data protection regulations is mandatory. Going with the best local LLM provides greater control over data handling and security measures, aligning with legal and policy requirements.
Cost Constraints: How Much Does It Cost?
Budget considerations play a significant role in the decision-making process. If budgetary limitations are a concern, yet you have the necessary hardware infrastructure, opting to run LLMs locally might be a more cost-effective solution. This can minimize operational costs, provided the initial setup and maintenance requirements are within your capabilities if you’re asking yourself, “which LLM should I use?” you also need to consider which AI code generation tools you can afford to leverage.
For example, GitHub Copilot cost around $19/mo at the time this article was published, so you may want to look for free alternatives that support many of the best LLM models, such as Pieces.
Ease of Use: Will I Need a PhD to Use This LLM?
The complexity of deploying and managing LLMs should not be underestimated, especially for teams with limited technical expertise or resources. Cloud platforms often offer user-friendly, plug-and-play solutions that significantly reduce the technical barriers to entry, making the process more manageable and less time-consuming.
However, consider an offline AI tool that works directly in your browser, IDE, and collaboration tools for less context switching. Test community support and documentation, too. Strong developer support offers:
- More tutorials
- Tips
- Updates
Think about cost versus benefit. Some models may have lots of features at a high price. Others offer good functionality for free or at a lower cost. Balancing these factors and considering the model’s specific use cases will help you make an informed choice for your coding needs. The ability to generate code from natural language descriptions is a game-changer for developers. It makes the coding process faster and more efficient and helps bridge the gap between non-programmers and the world of code.
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