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QodoA AI-AI code quality platform, formerly known as codium, announced the exit from Qodo-Embed-1-1.5bA new open source code integration model that offers advanced performance while being significantly lower and more efficient than competing solutions.
Designed to improve research, recovery and code understanding, the parameter model of 1.5 billion obtains high level results on industry references, surpassing the larger models of Openai and Salesforce.
For business development teams managing vast and complex code bases, Qodo innovation represents a leap forward in software engineering flows led by AI. By allowing a more precise and effective code recovery, Qodo-Embed-1-1.5b takes up a critical challenge in AI-assisted development: Consciousness of the context in large-scale software systems.
Why the code integration models count for the company AI
The coding solutions powered by AI have traditionally focused on the generation of code, with large language models (LLM) attracting attention to their ability to write a new code.
However, like Itamar Friedman, CEO and co-founder of Qodo, explained in a video call interview earlier this week: “Company software can have tens of millions, even hundreds of millions of code lines. The generation of code alone is not enough – you must make sure that the code is of high quality, works properly and integrates into the rest of the system. »»
Code integration models play a crucial role in AI -assisted development by allowing systems to search and effectively recover relevant code extracts. This is particularly important for major organizations where software projects cover millions of code lines in several teams, standards and programming languages.
“The context is king for all that is currently linked to the construction of software with models,” said Friedman. “More specifically, to recover the right context from a very large code base, you must go through a research mechanism.”
Qodo-Embed-1-1.5B offers performance and efficiency
Qodo-Embed-1-1.5b is distinguished by its balance of efficiency and precision. While many peak models are based on billions of parameters-the text-Embedding-3-Large of Openai is 7 billion, for example, the Qodo model obtains higher results with only 1.5 billion parameters.
On the benchmark (Coir) (Coir) (Coir), a standard test of industry for code recovery from several languages and tasks, Qodo-Embed-1.5b marked 70.06, outperforming SFR-EMBEDING-2_R de SALESFORCE (67.41) and OPENAI Text-Edding-3-LAGE (65.17).
This level of performance is essential for companies looking for profitable AI solutions. With the possibility of operating on low -cost GPUs, the model makes infrastructure costs accessible to a larger range of development teams, reducing infrastructure costs while improving the quality and productivity of software.
Approach the complexity, shade and specificity of the different code extracts
One of the biggest challenges in the development of software fueled by AI is that the similar code can have very different functions. Friedman illustrates this with a simple but impactful example:
“One of the greatest challenges in the integration of the code is that two almost identical functions – as” withdraw “and” deposit ” – differ only by a more or less sign. They must be close in vector space but also clearly distinct. »»
A key problem in integration models is to ensure that the functionally separate code is not badly grouped together, which could cause major software errors. “You need an incorporation model that includes the code enough to recover the right context without providing similar but incorrect functions, which could cause serious problems.”
To solve this problem, Qodo has developed a single training approach, combining high -quality synthetic data with real world code samples. The model was formed to recognize the nuanced differences in a functionally similar code, ensuring that when a developer is looking for a relevant code, the system recovers good results – not simply similar.
Friedman notes that this training process has been refined in collaboration with Nvidia and AWS, which both write technical blogs on Qodo methodology. “We have collected a unique data set which simulates the delicate properties of software development and has refined a model to recognize these nuances. This is why our model surpasses generic integration models for the code. »»
Multiprogrammant language support and plans for future expansion
The Qodo-Embed-1.5B model was optimized for the 10 most commonly used programming languages, including Python, Javascript and Java, with an additional support for a long tail of other languages and frames.
Future iterations of the model will expand this foundation, offering more in -depth integration with business development tools and additional linguistic support.
“Many models of incorporation have trouble making the difference between programming languages, sometimes mixing extracts from different languages,” said Friedman. “We have specifically formed our model to prevent this, focusing on the first 10 languages used in business development.”
Company deployment options and available
Qodo makes its new model widely accessible via several channels.
The 1.5B parameter version is available on the face of hugs under the Openrail ++ – M license, allowing developers to integrate it freely into their workflows. Companies needing additional capacities can access larger versions under commercial license.
For companies looking for a fully managed solution, Qodo offers a business quality platform that automates integration updates as code bases are evolving. This takes up a key challenge in AI development: ensuring that research and recovery models remain accurate as the code changes over time.
Friedman considers this as a natural step in Qodo’s mission. “We publish Qodo by incorporating one as the first step. Our objective is to permanently improve on three dimensions: precision, support for more languages and better management of specific frameworks and libraries. »»
Beyond the embrace, the model will also be available via the NIMS platform of NVIDIA and AWS SAGEMAKER JUMSSTART, which facilitates the deployment and integration of companies for deployment and integration into their existing development environments.
The future of AI in DEV company software
The coding tools powered by AI evolve rapidly, but the emphasis is put beyond the generation of code to the understanding of the code, recovery and quality assurance. While companies move to integrate more in-depth AI into their software engineering processes, tools like Qodo-Embed-1-1.5b will play a crucial role in the more reliable, efficient and profitable meeting.
“If you are a developer in a fortune company 15,000, you are not only using the co -pilot or the cursor. You have workflows and internal initiatives that require an in -depth understanding of the major code bases. This is where a high quality code integration model becomes essential. »»
The latest Qodo model is a step to a future where AI does not only make developers to write code – this helps them to understand, manage it and optimize it through complex -scale software ecosystems.
For business teams looking to take advantage of AI for more intelligent code, recovery and quality control, the new Qodo integration model offers a convincing and high performance alternative to larger and more intensive resource solutions.
