The Deep Seek Coder v2 is an impressive open-source large language model that is closely competing against the GPT-4 Turbo and is on par with GPT-3.5 Sonet in various benchmarks. This model has been continuously updated by the Deep Seek team, with new API, chat model for function calling, and chat completion features being released on a weekly basis.

The model's performance on the Big Bench Coder leaderboard, which evaluates large language models on practical and challenging programming tasks, is particularly noteworthy. Deep Seek Coder v2 is currently the top-performing model, showcasing its exceptional capabilities in code intelligence.

Compared to other open-source models like the new LLaMA 3.1 405 billion parameter model, the Deep Seek Coder v2 is miles ahead, demonstrating its superiority in the realm of coding-based tasks.

The model's performance on the AER (AI Pair Programmer) leaderboard further solidifies its position as the best open-source coding-based large language model. It is slightly ahead of the GPT-4 Omni model and slightly behind the GPT-3.5 Sonet model in terms of code generation, editing, and other code-specific tasks.

Deep Seek Coder v2 is an open-source mixture of experts code language model that achieves performance comparable to GPT-4 Turbo and GPT-4 Omni in code-specific tasks. It has been further pre-trained from the intermediate checkpoint of Deep Seek v2 with an additional 6 trillion tokens, supporting up to 338 programming languages and a 128K context window.

Overall, the Deep Seek Coder v2 is the best open-source coding-based large language model available, breaking the barrier of closed-source models in code intelligence. Its impressive performance across various benchmarks and its continuous updates make it a compelling choice for developers and researchers working on code-related tasks.

It is quite impressive to see that the Deep Seek Coder Version 2 is achieving superior performances in various benchmarks. It is quite comparable to many of these models in various benchmarks like Codeeval, MBPP, MathGSM, AER, and so many others. This just goes to show how impressive this model is in comparison to closed-source models like GPT-4 Omni, Chinchilla, as well as many of these other models.

In my opinion, this is the best model in comparison to other open-source models. The Deep Seek Coder Version 2 is closely competing against the GPT-4 Turbo model and is on par with GPT-3.5 Sonnet in the Big Bench Coder leaderboard. This evaluation showcases that this new model is the best open-source coding-based large language model, outperforming even the new Llama 3.1 405 billion parameter model.

The Deep Seek Coder Version 2 is further pre-trained from the intermediate checkpoint of Deep Seek V2, with an additional 6 trillion tokens. It supports up to 338 programming languages and has a 128K context window, which is great to see. It is truly, in my opinion, the best open-source coding-based large language model up to this date.

Let's dive into the capabilities of the Deep Seek Coder v2 model by testing it across various coding tasks:

Fibonacci Sequence Generator

The model was able to correctly generate a Python function to calculate the Fibonacci sequence up to the Nth number. It demonstrated a good understanding of basic algorithmic concepts and Python programming.

Sorting Algorithm

The model implemented a working Quick Sort algorithm in Java, showcasing its proficiency in recursive programming and partitioning logic. It was able to sort example arrays and print the sorted results.

CRUD API

The model successfully generated a complete RESTful API in Node.js using Express, implementing basic CRUD (Create, Read, Update, Delete) operations for a product resource. It demonstrated strong web development skills, knowledge of RESTful APIs, and proficiency in Node.js and Express.

SQL Query for Data Analysis

The model provided a step-by-step SQL query to find the top 5 customers who spent the most money in the last year. It showed its ability to handle data aggregation, filtering, and sorting in SQL, though it would have benefited from having access to the actual database schema and data.

Machine Learning Model Training

The model generated a Python script to train a simple linear regression model using the scikit-learn library to predict house prices. It covered the necessary steps, including data preprocessing, model training, and evaluation using mean squared error.

Overall, the Deep Seek Coder v2 model performed impressively across these diverse coding tasks, showcasing its strong capabilities in areas such as algorithmic understanding, programming language proficiency, web development, data analysis, and machine learning. This open-source model appears to be a highly capable alternative to closed-source models like GPT-4 Turbo and GPT-4 Omni for code-related tasks.

The Deep Seek Coder V2 is an impressive open-source large language model that is closely competing against the likes of GPT-4 Turbo and GPT-3.5 Sonic in various coding-related benchmarks. This model has demonstrated its capabilities in tasks such as generating Fibonacci sequence, implementing sorting algorithms, building a basic REST API, writing SQL queries for data analysis, and training a simple linear regression model.

The model's performance across these diverse coding challenges showcases its strong understanding of programming concepts, syntax, and problem-solving abilities. It is particularly noteworthy that the Deep Seek Coder V2 outperforms even the new LLaMA 3.1 405 billion parameter model, which is a testament to the team's efforts in continuously improving and refining this open-source model.

Compared to closed-source models like GPT-4 Omni, the Deep Seek Coder V2 has proven to be a highly capable alternative, offering impressive results in code-related tasks. This model's success highlights the potential of open-source AI solutions to challenge and even surpass the capabilities of proprietary models, making it an exciting development in the field of AI-powered coding assistance.

As the Deep Seek team continues to release new iterations and updates to this model, it will be interesting to see how it evolves and potentially widens the gap with other large language models in the realm of code intelligence. For developers and researchers looking to explore the capabilities of open-source AI in coding, the Deep Seek Coder V2 is undoubtedly a model worth considering and experimenting with.