Finetuned Open-Source Models
The best models tuned specifically for your use case.
Running privately and securely.
Open-Source Models
In the rapidly evolving world of machine learning, open-source models are making remarkable strides in capability and are released with increasing regularity. Driven by a collaborative ethos, these models are often on par with, if not surpassing, their larger counterparts, especially when fine-tuned for specific domains. This fine-tuning allows them to harness their generalised knowledge and adapt it precisely, leading to superior performance in niche areas, demonstrating the potent combination of open-source flexibility and domain-specific adaptation.
Why does finetuning produce such great results?
In essence, fine-tuning is akin to specialisation after general education. Just as a medical doctor, after going through general medical training, might specialise in cardiology or neurology, a pre-trained model specialises in a specific task after its general training. The process ensures that the vast knowledge captured during extensive pre-training is not discarded, but rather refined and adapted for specific needs.
Open-Source Models in Secure Environments: A Fortified Approach to Data Security
Open-Source Models operating in a dedicated computational domain that is isolated, controlled, and safeguarded against potential threats, offers the best of all worlds.
Data Isolation: Operating in a secure domain ensures that data is kept in a confined environment, mitigating the risk of unauthorised access or data leaks.
Controlled Access: Only authorised personnel can interact with the model, reducing the chances of malicious interference or accidental mishandling.
End-to-End Encryption: Data, while in transit or at rest, can be encrypted, ensuring that even if intercepted, it remains unintelligible.
Audit Trails: Secure environments often come with robust logging mechanisms. This means that any access or operation is logged, allowing for accountability and traceability.
Compliance with Regulations: Many sectors have stringent data protection regulations. Operating in a secure environment often aligns with these regulations, ensuring that the handling of sensitive data meets industry standards.
Sovereignty: Guaranteeing that all data and model interactions are exclusively managed within Australian borders, ensuring compliance with local data protection laws and maintaining data sovereignty.
In essence, by deploying open-source models in secure domains, organisations are not just leveraging the power of these models but doing so in a manner that places paramount importance on data security and model reliability.
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A pre-trained model comes with established weights (parameters) from its initial training. During fine-tuning, these weights are adjusted, albeit usually by smaller amounts than in initial training. This is because the model has already learned general features from its pre-training, and during fine-tuning, it is refining these features based on the new dataset.
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The primary benefit of fine-tuning is leveraging the knowledge that the model has gained during its pre-training. For instance, a language model trained on a vast corpus of text would have learned the structure of the language, grammar, and even some world facts. Fine-tuning allows this model to specialise this knowledge for specific tasks like sentiment analysis or medical text interpretation.
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Often, in deep neural networks, the initial layers capture generic features (like edges in image processing or basic syntax in language processing), while the deeper layers capture more abstract and task-specific features. During fine-tuning, the deeper layers typically undergo more significant adjustments than the initial layers.
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One of the technical nuances in fine-tuning is the modulation of the learning rate. Typically, a smaller learning rate is used compared to the initial training to ensure that the model doesn't deviate drastically from its pre-trained state. This helps in retaining the knowledge from pre-training while making necessary adjustments for the new task.
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Based on the task, some parts of the model might be frozen (i.e., their weights are kept constant), while others are allowed to change. For instance, when fine-tuning for image classification, the convolutional layers might be frozen, and only the fully connected layers are trained.
GPU Compute
The Powerhouse Behind AI
The transformative capabilities of transformers come with equally transformative computational requirements. Finetuning and deploying these models requires significant GPU compute power.
Why GPUs are Essential for Transformers
Parallel Processing: Transformers have thousands to millions of parameters. GPUs, with their thousands of cores, can process these in parallel, significantly accelerating model training and inference.
Large Memory Bandwidth: The self-attention mechanism in transformers requires handling large matrices simultaneously. GPUs, with their high memory bandwidth, manage these operations efficiently.
Scalability: As the models scale, the need for more GPU resources escalates. With the right infrastructure, GPUs can be scaled horizontally, allowing for distributed training across multiple GPU units.
AIVOSI and RESET DATA
A Partnership Powering Innovation
Recognising the imperative of GPU compute for our transformer models, AIVOSI has forged a strategic partnership with Cloud Compute Provider Reset Data. This collaboration provides us with:
Access to State-of-the-Art GPU Resources: Reset Data's infrastructure boasts the latest NVIDIA GPUs, ensuring that our models train efficiently and deliver optimal performance.
Scalable Infrastructure: As our projects grow and the demands intensify, Reset Data's cloud infrastructure can effortlessly scale, ensuring uninterrupted innovation.
Cost Efficiency: Leveraging cloud-based GPU resources means we only utilise and pay for what we need, ensuring optimal resource allocation and cost-effectiveness.
