Introduction
The era of digital transformation has ushered in a wave of advancements in the tech industry, including the burgeoning field of Artificial Intelligence (AI). Large Language Models (LLMs) have turned into game-changers, especially in the domain of software development. LLMs can read, understand, and even generate code, bridging the gap between natural language and computer languages. Their versatility has made them an indispensable tool for developers, automating a multitude of tasks ranging from code completion and bug fixing to even generating whole code bases.
As the technology progresses, we're seeing more specialized language models designed for very specific tasks. WizardCoder 34B, a model created with a focus on code generation, is one of the latest LLMs that is being hailed by experts. Unlike generic LLMs that are good at a range of tasks, WizardCoder 34B excels at understanding and generating code. This specialization has already made WizardCoder a noteworthy competitor to other established models like GPT-4 and ChatGPT-3.5.
WizardCoder 34B is built on Code Llama, a large language model (LLM) developed by Meta. Code Llama serves as the foundational architecture upon which WizardCoder 34B has been fine-tuned and optimized for coding tasks. The adaptation and specialization of WizardCoder 34B make it distinct from the general-purpose capabilities of Code Llama, focusing primarily on code generation, debugging, and other development-related activities.
Developed by Wizard LM, it has been fine-tuned to perform exceptionally well in coding tasks, boasting impressive results on coding benchmarks like HumanEval. In summary, WizardCoder 34B is not just another language model; it is a targeted solution for software development tasks [1]. As we explore its capabilities and features in this blog, we'll see why it's drawing the attention of developers and tech companies alike.
WizardCoder 34B vs the Competition
The world of large language models is teeming with contenders. Models like GPT-4 and GPT-3.5 have been around for some time and are celebrated for their wide range of capabilities, including code generation. Then there is Code Llama, a model developed by Meta, which serves as the base for WizardCoder and is specifically aimed at coding tasks. But what makes WizardCoder 34B stand apart?
- GPT-4: While GPT-4 is a general-purpose model proficient at various tasks, it is not specialized in code generation. When it comes to more complex coding tasks, GPT-4 may fall short.
- GPT-3.5: Similar to GPT-4 but slightly older, ChatGPT-3.5 offers broad capabilities. However, when the stakes are coding-specific, WizardCoder 34B has the edge.
- Code Llama: Developed by Meta, this is more of a cousin to WizardCoder 34B. While Code Llama lays the groundwork, WizardCoder builds upon it, refining and optimizing the model specifically for code generation tasks.
Performance Metrics: HumanEval Benchmarks
An important benchmark test for these models' coding capabilities lies in their performance on HumanEval, a widely-recognized benchmark for evaluating the coding prowess of LLMs [2].
- GPT-4: The August release of GPT-4 scored an impressive 67% on the HumanEval benchmarks in the month of march, and 82% in the month of August
- GPT-3.5: Managed to secure a respectable score but lagged behind the specialized models with a score around 65%.
- Code Llama: Scored pass rates of 67.6% and 69.5% on its fine-tuned versions, making it almost on par with GPT-4 [3].
- WizardCoder 34B: Steals the spotlight by scoring a 73.2% on the HumanEval Benchmarks, outdoing not just its predecessor, Code Llama, but also more generalized models like ChatGPT-3.5.
In March 2023, GPT-4 scored 67% on the HumanEval benchmarks. However, by August, this figure had jumped to 82%. This significant increase in just a few months highlights the rapid development and fine-tuning that GPT-4 has undergone. WizardCoder 34B has an impressive score of 73.2% on the HumanEval benchmarks. While this is a strong showing, it's essential to note that this score was compared to GPT-4's March version, not the more recent August update.
However, it's important to consider the scope and context. WizardCoder 34B is specialized for coding tasks, particularly in Python, and might offer more nuanced and specialized outputs for such tasks compared to a generalist model like GPT-4. WizardCoder 34B is also fine-tuned for code generation, which may make it better for very specific coding scenarios that are not captured by the HumanEval benchmark.
Under the Hood: How WizardCoder Works
Let's take a look at the technical aspects that make WizardCoder 34B tick. We'll focus on the architecture, underlying technologies, and any special features like the Evol-Instruct method that set this model apart.
Architecture and Technologies
WizardCoder 34B is built on Code Llama, a large language model developed by Meta. Code Llama itself is a code-specialized version of Llama 2. It comes with 34 billion parameters, making it one of the most substantial models focused on code generation. The model is optimized for Python code and leverages Transformer architecture, similar to other leading large language models like GPT-4.
Evol-Instruct Method
One of the standout features of WizardCoder 34B is the Evol-Instruct method. This is a fine-tuning process that evolves the model's instruction-following capabilities through iterative training. The method allows WizardCoder to better understand the context of the coding task and generate more accurate and optimized code. Here's how Evol-Instruct works:
- Initial Training: The model is trained with a large dataset of code snippets and corresponding natural language instructions.
- Evaluation: It is then evaluated using HumanEval or other benchmarks to assess its capability in following instructions to generate code.
- Iterative Fine-tuning: Based on the evaluations, the model is fine-tuned iteratively to enhance its ability to understand and generate task-specific code.
Special Features
- Context-Awareness: WizardCoder 34B has a better understanding of the code's context, making it more effective in generating coherent and functional code.
- Language Support: Although optimized for Python, WizardCoder 34B is designed to adapt to other programming languages as well.
- Quantization Levels: To manage computational needs, WizardCoder 34B offers different quantization levels. The higher the number, the more accurate the model is, although it might require more computational power.
Understanding these architectural nuances and features can help users gain a fuller understanding of the WizardCoder 34B's capabilities and limitations, useful to developers, data scientists, and AI enthusiasts.
Installation and Setup
In this section, we will walk you through the installation process for WizardCoder, specifically the 34B variant, and list the system requirements and dependencies you need to run the model smoothly.
System Requirements
- RAM: At least 32GB for the 34B model
- Python 3.6 or higher
Installation Steps
Step 1: Install the required Python packages.
pip install transformers deepspeed
Here, transformers are used for utilizing the Hugging Face model hub, and Deepspeed for optimization and running models more efficiently.
Step 2: Import the WizardCoder Model from the Hugging Face library.
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('WizardLM/WizardCoder-Python-34B-V1.0')
model = AutoModelForCausalLM.from_pretrained('WizardLM/WizardCoder-Python-34B-V1.0')
Step 3: Verify the Installation.
After installation, it's always good to verify that everything is working as expected. You can do this by running a sample instruction to generate code.
instruction = 'Write a Python function to calculate the nth Fibonacci number.'
input_ids = tokenizer(instruction, return_tensors='pt').input_ids
output = model.generate(input_ids)
decoded_output = tokenizer.decode(output[0], skip_special_tokens=True)
print(decoded_output)
Now that the installation procedure for WizardCoder is discussed, along with the system requirements, developers can make use of this advanced language model for their coding tasks.
Demo and Inference
Once WizardCoder 34B is installed and configured, it may be necessary to run some quick demos or inferences to test its capabilities. This section provides a straightforward guide on how to achieve this. Inference Demos can be run by the following:
With Ollama CLI
Start Ollama Server: Run the command ollama serve to start the Ollama server.
Run the Model: Open a new terminal and run the following command:
ollama run wizardcoder:34b-python
With API Calls
Start Ollama Server: If not already running, initiate it using ollama serve.
Run the Model: Use a curl command to run the model. Here's an example:
curl -X POST http://localhost:11434/api/generate -d '{
'model': 'wizardcoder:34b-python',
'prompt':'Write a Python function to return the Fibonacci sequence up to n'
}'
Getting Started: Coding with WizardCoder
This section shows how to start coding with WizardCoder 34B. Some example use-cases are explored, including generating DevOps scripts, building machine learning pipelines, and more.
Example Use Cases
Automating DevOps Scripts
WizardCoder can assist you in writing shell or Python scripts to automate various DevOps tasks like environment setup, server provisioning, and so on.
WizardCoder can assist you in writing shell or Python scripts to automate various DevOps tasks like environment setup, server provisioning, and so on.
instruction = 'Write a Python script to automate the setup of a new Linux environment.'
input_ids = tokenizer(instruction, return_tensors='pt').input_ids
output = model.generate(input_ids)
decoded_output = tokenizer.decode(output[0], skip_special_tokens=True)
print(decoded_output)
Other Examples Include
- Data Analysis - instruction = 'Generate Python code for data analysis using pandas and matplotlib for a sample dataset'
- Machine Learning Pipelines - instruction = 'Generate a Python code snippet for a machine learning pipeline using scikit-learn.'
- Boilerplate code for building RESTful APIs. - instruction = 'Generate Python code for a simple RESTful API using Flask.'
Memory and Performance Tuning
Working with a large language model for code generation like WizardCoder 34B requires careful planning around memory usage and performance. In this section, we’ll delve into the memory requirements, optimization techniques, model variants, and quantization levels to help you get the best out of WizardCoder 34B. Generally require at least 32GB of RAM for smooth operation. If you're running this model, make sure to allocate sufficient memory to prevent crashes or slowdowns.
Model Variants
WizardCoder 34B is the most parameter-rich variant, but there are lighter versions available with fewer parameters. These can be useful for tasks that do not require the full power of the 34B model.
- 13B Model: Requires at least 16GB of RAM.
- 7B Model: Suited for less memory-intensive tasks.
Quantization Levels
WizardCoder 34B allows you to choose different levels of quantization to balance accuracy against speed and memory usage.
- 4-bit Quantization (q4): Faster but less accurate.
- 8-bit Quantization (q8): Slower but more accurate. Requires more memory.
You can select the quantization level when running your model by specifying the corresponding tag. For example, to use 8-bit quantization, you can append -q8 to the model name during initialization.
ollama run wizardcoder:34b-python-q8
Here, Ollama is an application to run open-source large language models, such as LLaMA2, locally. Ollama bundles model weights, configuration, and data into a single package, defined by a Modelfile. It optimizes setup and configuration details, including GPU usage.
Future Direction
As the field of machine learning and natural language processing evolves, so does WizardCoder 34B. Here are some insights into the future directions the developers might take and the opportunities for synergies with other technologies.
Upcoming Features and Version Updates
- Better Code Quality Metrics: To address the limitations of current benchmarks like HumanEval, WizardCoder developers must consider incorporating more holistic code quality metrics, like code readability, maintainability, and even automated testing scores.
- Advanced Fine-Tuning Capabilities: The future versions may offer more customizable fine-tuning options, enabling users to adapt the model to highly specialized tasks.
- Real-Time Collaboration: An exciting possibility is the integration of WizardCoder into IDEs for real-time coding assistance and automated code review.
Possible Collaborations with Other Technologies
- Integrated Developer Environments (IDEs): One of the most obvious and useful collaborations could be with IDEs. A WizardCoder plugin could provide real-time coding assistance, significantly speeding up the development process.
- Continuous Integration Tools: Integration with CI/CD pipelines could make automated code generation and review a seamless part of the software development lifecycle.
- AI in Edge Computing: Combining WizardCoder with edge computing technologies could enable powerful code generation and analysis tasks to be performed locally, reducing the need for cloud resources.
- Data Science Platforms: A collaboration with data science platforms could mean automated script generation for data preprocessing, analysis, and visualization, making the data science workflow more efficient.
- Educational Software: WizardCoder can also serve as an educational tool, helping learners understand coding practices, algorithms, and data structures more effectively.
Conclusion
WizardCoder 34B has emerged as a powerful tool in the realm of automated coding and software development. Built on Code Llama by Meta, it has shown promising results in tasks evaluated using the HumanEval benchmark. While it lags behind GPT-4 in its August 2023 iteration, the model shows significant promise and is subject to rapid iterations.
WizardCoder 34B is also marked by its ease of installation, advanced features like the Evol-Instruct method, and options for memory and performance tuning.
While the data and metrics cited in this blog are accurate as of the time of writing, it's crucial to acknowledge that this is a rapidly evolving field. The performance and features of WizardCoder 34B could change. Always make sure to consult the most recent documentation for the latest information.
If you need to run WizardCoder 34B, E2E cloud has a large selection of GPUs to select from. NVIDIA H100 is a good fit, as it is highly compatible for LLMs.
References
[1] Z. Luo et al., 'WizardCoder: Empowering Code Large Language Models with Evol-Instruct,' Jun. 2023, [Online]. Available: http://arxiv.org/abs/2306.08568.
[2] M. Chen et al., 'Evaluating Large Language Models Trained on Code,' Jul. 2021, [Online]. Available: http://arxiv.org/abs/2107.03374.
[3] L. Tomassi, 'All About Code Llama: Meta’s New Coding AI,' CodeMotion, 2023. https://www.codemotion.com/magazine/ai-ml/all-about-code-llama-metas-new-coding-ai/.
