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Coding Power: How IBM Granite Models are Shaping Innovation

A family of generative AI models created specifically for corporate applications is IBM Granite model. Below is a summary of its salient attributes:

Emphasis on Generative AI: Granite models are helpful for jobs like content generation or code completion because they can generate new text, code, or other data types.

Business-focused: Granite models are designed to meet corporate needs and adhere to standards, having been trained on reliable enterprise data.

A Variety of Models Are Available: The Granite series has a number of versions with varying capacities and sizes to meet different needs.

Open Source Code Models: IBM has made its Granite code model family available as open-source, enabling programmers to use them for a range of coding jobs.

Put Trust and Customisation First: Granite models are designed with an emphasis on transparency and trust, and they can be customised to meet the unique requirements and values of an organisation.

All things considered, companies wishing to include generative AI into their processes can find a strong and flexible answer in IBM’s Granite models.

Optimising Generative AI Quality with Flexible Models

In dynamic gen AI, one-size-fits-all techniques fail. A variety of model options is needed for enterprises to exploit AI’s power:

Encourage creativity

Teams may respond to changing business requirements and consumer expectations by utilising a varied palette of models, which also encourages innovation by bringing unique strengths to bear on a variety of issues.

Customize for edge

A variety of methods enable businesses to do just that. Gen AI is adaptable to several jobs; it can be programmed to write code to generate short summaries or to answer questions in chat applications.

Cut down on time to market

Time is critical in the hectic corporate world of today. Companies may launch AI-powered products more quickly by streamlining the development process with a wide model portfolio. In the context of generation AI, this is particularly important since having access to the newest developments gives one a significant competitive edge.

Remain adaptable as things change

When things change, remember to remain adaptable. Both the market and company tactics are always changing. Companies may make swift and efficient changes by selecting from a variety of models. Having access to a variety of choices helps one to remain flexible and resilient by quickly adapting to emerging trends or strategic changes.

Reduce expenses for all use cases

The costs associated with various models differ. Enterprises can choose the most economical solution for every use case by utilising an assortment of models. More economical models can do certain activities without compromising quality, but others may not be able to handle the accuracy needed for expensive models. For example, in resource and development, accuracy is more important, but in customer care, throughput and latency may be more crucial.

Mitigate risks

Reduce risks by not depending too much on a single model or a small number of options. To ensure that firms are not overly affected by the limitations or failure of a particular strategy, a diversified portfolio of models is helpful in mitigating concentration risks. In addition to distributing risk, this approach offers backup plans in case problems develop.

Respect the law

Ethics are at the forefront of the ever-evolving regulatory landscape surrounding artificial intelligence. Fairness, privacy, and compliance can all be affected differently by different models. Businesses can choose models that adhere to ethical and legal requirements and handle this challenging environment with ease when there is a wide range of options.

Making the appropriate AI model choices

Now that IBM is aware of how important model selection is, how does the company handle the issue of too many options when choosing the best model for a given scenario? This difficult issue can be simplified by IBM into a few easy steps that you may use right now:

Choose an obvious use case

Find out what your company application’s particular needs and requirements are. For the model to closely match your goals, this entails creating intricate prompts that take into account nuances specific to your organisation and industry.

List every option for a model

Consider factors including latency, accuracy, size, and related dangers while evaluating different models. Accuracy, latency, and throughput trade-offs are just a few of the things you need to be aware of while evaluating any model.

Analyse the model’s characteristics

Consider how the model’s scale may impact its performance and the associated risks when you evaluate the model’s features. Determine whether the model’s size is acceptable for your purposes. Choosing the appropriate model size is crucial in this step, as larger models aren’t always better for the use case. In specific domains and application circumstances, smaller models can surpass larger ones in performance.

Test model options

Try out different model options to determine if the model behaves as predicted in situations that are similar to actual ones. For example, quick engineering or model tuning can be used to optimise the model’s performance. Output quality is assessed using academic benchmarks and domain-specific data sets.

Cost and deployment should guide your choice

Once you have tested, hone your selection by taking into account aspects like ROI, economy of scale, and the feasibility of implementing the model in your current infrastructure and processes. Consider other advantages like reduced latency or increased transparency while making your decision.

Select the most value-adding model

Decide on an AI model that best suits the requirements of your use case by weighing performance, cost, and associated hazards.

The IBM Watsonx Model Library

The IBM Watsonx library provides proprietary, open source, and third-party models by utilising a multimodel technique

Customers can choose from a variety of options, depending on what best suits their particular business needs, location, and risk tolerance.

Watsonx also gives customers the flexibility to install models on-premises, in hybrid cloud environments, and on-premises, allowing them to lower total cost of ownership and avoid vendor lock-in.

Enterprise-grade IBM Granite base models

The three primary traits that characterise foundation models can be combined. Businesses need to realise that if one quality is prioritised above the others, the others may suffer. The model must be adjusted to meet the unique needs of each organisation by striking a balance between these characteristics:

Reliable: Explicit, comprehensible, and non-toxic models.

Performant: Suitable performance level for the use cases and business domains being targeted.

Models with a lower total cost of ownership and lower risk are considered cost-effective.

IBM Research produced the flagship IBM Granite line of enterprise-grade models. Businesses can succeed in their gen AI ambitions with these models because they have an ideal combination of these traits, with a focus on trust and reliability. Do not forget that companies cannot grow artificial intelligence (AI) with unreliable foundation models.

Following a thorough refinement process, IBM Watsonx provides enterprise-grade AI models. This process starts with model innovation under the direction of IBM Research, which involves transparent data sharing through open collaborations and training on enterprise-relevant information in accordance with the IBM AI Ethics Code.

An instruction-tuning technique developed by IBM Research contributes features that are critical for enterprise use to both IBM-built and some open-source models. IBM’s “FM_EVAL” data set simulates enterprise AI systems that are used in real-world settings, going beyond academic benchmarks. Watsonx.ai offers clients dependable, enterprise-grade gen AI foundation models by showcasing the most robust models from this pipeline.

Most recent model announcements

Granite code models are a series of models that encompass both instruction-following model variants and a basic model, trained in 116 programming languages and with sizes ranging from 3 to 34 billion parameters.

Granite-7b-lab is a general-purpose work support system that is adjusted to integrate new abilities and information through the IBM large-scale alignment of chatbots (LAB) methodology.

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