How many parameters is Claude 2 trained on? [2023]

How many parameters is Claude 2 trained on? Claude 2 is the latest AI assistant created by Anthropic, an AI safety startup based in San Francisco. It builds on the capabilities of the original Claude model while focusing more on safety through techniques like constitutional AI.

There has been much interest around how large Claude 2’s model size is and specifically the number of parameters it has been trained on. In this article, we will analyze this topic in depth.

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Defining Model Parameters

First, let us define what we mean by parameters in the context of machine learning models like Claude 2. Parameters refer to the adjustable settings within a model that are updated during the training process to improve its performance on various tasks.

For example, in neural networks – the most common architecture used today – these parameters would be the weights and biases that exist between neurons across the layers of the network. The number of parameters grows quickly as neural networks go wider (more neurons per layer) and deeper (more layers).

So when we talk about Claude 2 having some number of parameters, we are referring to the total number of trained weight and bias terms across its entire neural network architecture. More parameters allow a model to have greater representational capacity but also increase chances of overfitting among other tradeoffs.

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Motivations for Large Models

In recent years, there has been a trend towards massively scaled up models with trillions of parameters. Examples include OpenAI’s GPT-3 and Google’s PaLM with over 530 billion parameters. Larger models are able to capture more knowledge and nuances during training, leading to benefits like:

• Wider domain mastery: Can perform well on a broad range of tasks due to incorporating more contexts
• Knowledge retention: Tend to forget less compared to smaller models when learning new information
• Generalizability: Able to apply knowledge to new situations better after seeing more examples

However, the downside is it requires immense resources to train such large models, putting them out of reach for most. There are also increased concerns around biases, ethics and responsible usage.

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Anthropic’s Constitutional AI Approach

Anthropic specifically constrains model size as part of its safety-focused constitutional AI methodology. The values encoded into Claude 2 limit exploitation while maintaining helpfulness.

Some core tenets of their approach regarding model scaling include:

• Intelligibility: Small enough to be interpretable by humans
• Controllability: Modest size allows for easier alignment techniques
• Auditability: Reduces deleterious behavior by simplifying fact-checking
• Constraint-relaxation schema: Additional parameters introduced cautiously as safety permits

Anthropic runs widespread adversarial probing to empirically measure sensitive model attributes. This ensures minimal unintended biases are baked into the model prior to release.

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Analyzing Claude 2’s Published Model Size

Recently on its website, Anthropic revealed that:

“Claude 2 has approximately 12 billion parameters, runs on a single GPU, and can respond to users in under a second while maintaining high factual accuracy and transparency.”

To put this scale into context, it is much smaller than leading models – around 2.3% the size of PaLM or 0.2% of GPT-3’s capacity. Yet Claude 2 retains sufficient parameterization to conduct informative conversations across numerous topics.

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Model Size in Relation to Safety Goals

Anthropic has commented previously that Claude’s model size may still increase considerably over time but likely not at the scale of industry counterparts. This restraint links back to their constitutional AI approach favoring safety.

Some of the tradeoffs related to the smaller 12 billion parameter count today include:

• Reduced conversational ability in certain specialized domains that benefit from larger models’ greater knowledge capacity
• More limb-of-God questions that it simply cannot infer an accurate answer to, requiring it transparently ask the user for clarity
• Limited multitasking prowess across highly distinct tasks relative to models with 10x more parameters

However, this is all part of Anthropic willfully giving up some proficiencies in exchange for improved security. And Claude 2 strives to make up for its smaller model size through techniques like chain-of-thought prompting to expand reasoning ability despite the parameter constraint.

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Striking a Principled Balance

In determining the appropriate model capacity, companies must strike the right balance between safety and performance goals. Anthropic believes 12 billion parameters allows Claude 2 to possess useful abilities across areas like natural language processing, logical reasoning, summarization, and open-domain QA without becoming inordinately hazardous if mishandled.

At the same time, Anthropic is still a commercial company needing Claude to offer utility to customers. So they have set scaling ambition levels intended to keep Claude’s proficiency competitive in the marketplace as other models grow. Their indexed roadmap provides visibility into plans for gradually expanding model sizes in a controlled manner over the next decade.

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Conclusion

In conclusion, Claude 2 was trained on approximately 12 billion parameters – orders of magnitude less than comparable industry language models. Anthropic’s constitutional AI approach favors scaling back model size to enhance security properties like auditability. This requires more advanced training techniques to retain adequate functionality across diverse conversational domains.

It is a principled balance between safe footing on one hand and market viability on the other. We expect Claude’s model size to increase moderately over time as Anthropic continues advancing the state of AI safety through transparent model development. But for now 12 billion parameters enables an assistant both capable for users and aligned with developer values.

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