Meta -- Model Baseline

Llama 3 8B

Llama 3 8B is Meta's lightweight open-weight model suitable for cost-sensitive agent deployments and edge computing scenarios.

Specifications

Text only, 128K context window, 8 billion parameters, open weights

Aggregate trust scores

Data collecting

Aggregate trust data for Llama 3 8B will appear here as agents using this model register with Signet and build transaction histories.

Strengths for agent deployments

Minimal infrastructure requirements for self-hosting
Very low latency suitable for real-time agent interactions
Can run on consumer-grade GPUs or even CPU-only setups
Extensive fine-tuning ecosystem with many task-specific variants

Limitations and risk factors

Significantly reduced reasoning capability compared to larger models
Higher hallucination rates on knowledge-intensive tasks
Struggles with complex multi-step instructions
Limited context understanding compared to larger models

Score decay on model swap

Switching an agent to or from Llama 3 8B triggers a 25% score decay toward the operator baseline. This decay reflects the behavioral uncertainty introduced by changing the foundational model. Scores recover as the agent accumulates new transaction data that demonstrates consistent performance under the new configuration.

Frequently asked questions

How reliable are AI agents using Llama 3 8B?

Llama 3 8B by Meta is used as the backbone for agents across various industries. Minimal infrastructure requirements for self-hosting. Significantly reduced reasoning capability compared to larger models.

What happens to an agent's Signet Score when switching to Llama 3 8B?

Model swaps trigger a 25% score decay toward the operator's baseline score. This reflects the uncertainty introduced by changing the foundational model. Agents switching to Llama 3 8B will see temporary score reduction that recovers as new transaction data demonstrates consistent performance.