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Code Llama 70B beats ChatGPT-4 at coding and programming

Code Llama 70B beats ChatGPT-4 at coding and programming

Developers, coders and those of you learning to program might be interested to know that the latest Code Llama 70B large language model released by Meta and specifically designed to help you improve your coding. Has apparently beaten OpenAI’s ChatGPT  when asking for coding advice, code snippets and coding across a number of different programming languages.

Meta AI recently unveiled Codellama-70B, the new sophisticated large language model (LLM) that has outperformed the well-known GPT-4 in coding tasks. This model is a part of the Codellama series, which is built on the advanced Lama 2 architecture, and it comes in three specialized versions to cater to different coding needs.

The foundational model is designed to be a versatile tool for a variety of coding tasks. For those who work primarily with Python, there’s a Python-specific variant that has been fine-tuned to understand and generate code in this popular programming language with remarkable precision. Additionally, there’s an instruct version that’s been crafted to follow and execute natural language instructions with a high degree of accuracy, making it easier for developers to translate their ideas into code. If you’re interested in learning how to run the new Code Llama 70B AI model locally on your PC check out our previous article

Meta Code Llama AI coding assistant

What sets Codellama-70B apart from its predecessors is its performance on the HumanEval dataset, a collection of coding problems used to evaluate the proficiency of coding models. Codellama-70B scored higher than GPT-4, marking a significant achievement for LLMs in the realm of coding. The training process for this model was extensive, involving the processing of a staggering 1 trillion tokens, focusing on the version with 70 billion parameters.

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The specialized versions of Codellama-70B, particularly the Python-specific and instruct variants, have undergone fine-tuning to ensure they don’t just provide accurate responses but also offer solutions that are contextually relevant and can be applied to real-world coding challenges. This fine-tuning process is what enables Codellama-70B to deliver high-quality, practical solutions that can be a boon for developers.

Recognizing the potential of Codellama-70B, Meta AI has made it available for both research and commercial use. This move underscores the model’s versatility and its potential to be used in a wide range of applications. Access to Codellama-70B is provided through a request form, and for those who are familiar with the Hugging Face platform, the model is available there as well. In an effort to make Codellama-70B even more accessible, a quantized version is in development, which aims to offer the same robust performance but with reduced computational requirements.

One of the key advantages of Codellama-70B is its compatibility with various operating systems. This means that regardless of the development environment on your local machine, you can leverage the capabilities of Codellama-70B. But the model’s expertise isn’t limited to simple coding tasks. It’s capable of generating code for complex programming projects, such as calculating the Fibonacci sequence or creating interactive web pages that respond to user interactions.

For developers and researchers looking to boost coding efficiency, automate repetitive tasks, or explore the possibilities of AI-assisted programming, Codellama-70B represents a significant step forward. Its superior performance on coding benchmarks, specialized versions for targeted tasks, and broad accessibility make it a valuable asset in the toolkit of any developer or researcher in the field of AI and coding. With Codellama-70B, the future of coding looks more efficient and intelligent, offering a glimpse into how AI can enhance and streamline the development process.

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New Zephyr-7B LLM fine-tuned, beats Llama-2 70B

New Zephyr-7B LLM fine-tuned Mistral-7B AI model

The world of artificial intelligence has witnessed another remarkable milestone with the release of the new Zephyr-7B AI model on Hugging Face. This innovative model is a fine-tuned successor to the original Mistral 7B, and it has managed to outperform larger 70 billion parameter models, even while being uncensored. The company has also unveiled a comprehensive technical report, offering a detailed overview of the training process of the model. Try out the Zephyr 7B Beta new here.

Direct preference optimization (DPO)

The Zephyr-7B model has been trained using a three-step strategy. The first step involves distilled supervised fine-tuning using the Ultra Chat dataset. This dataset, comprising 1.47 million multi-dialogues generated by GPT 3.5 Turbo, underwent a rigorous cleaning and filtering process, leaving only 200,000 examples. The distilled supervised fine-tuning process involves a teacher-student model dynamic, with a larger model like GPT 3.5 playing the role of the teacher and Zephyr-7B as the student. The teacher model generates a conversation based on a prompt, which is then used to fine-tune the student model, Zephyr-7B.

Zephyr-7B beats Llama-2 70B

The second step of the training strategy is AI feedback. This step utilizes the Ultra Feedback dataset, consisting of 64,000 different prompts. Four different models generate responses to each prompt, which are then rated by GP4 based on honesty and helpfulness. This process aids in refining the model’s responses, contributing to its overall performance.

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The final step of the training strategy involves training another model using the dataset created with a winner and loser. This step further solidifies the learning of the Zephyr-7B model, ensuring that it can generate high-quality, reliable responses.

The performance of the Zephyr-7B model has been impressive, outperforming all other 7 billion models and even larger models like the Falcon 40 billion and Llama 2 70 billion models. However, it’s important to note that the model’s performance varies depending on the specific task. For instance, it lags behind in tasks like coding and mathematics. Thus, users should choose a model based on their specific needs, as the Zephyr-7B model may not be the best fit for all tasks.

Zephyr-7B LLM

One unique aspect of the Zephyr-7B model is its uncensored nature. While it is uncensored to a certain extent, it has been designed to advise against illegal activities when prompted, ensuring that it maintains ethical guidelines in its responses. This aspect is crucial in maintaining the integrity and responsible use of the model.

Running the Zephyr-7B model can be done locally using LMStudio or UABA Text Generation WebUI. This provides users with the flexibility to use the model in their preferred environment, enhancing its accessibility and usability.

The Zephyr-7B model is a significant addition to the AI landscape. Its unique training strategy, impressive performance, and uncensored nature set it apart from other models. However, its performance varies depending on the task at hand, so users should choose a model that best suits their specific needs. The company’s active Discord server provides a platform for discussions related to generative AI, fostering a community of learning and growth. As the field of AI continues to evolve, it will be exciting to see what future iterations of models like Zephyr-7B will bring.

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