🤖Laguna S 2.1: 8B Activated Parameters in Under Nine Weeks
Tiny but mighty: Laguna S 2.1 packs a punch
TL;DR
Laguna S 2.1, an 8B activated parameter model, launches with impressive performance on coding benchmarks despite its size. Its compact nature makes it ideal for local machine tasks.
Laguna S 2.1, a 118B total parameter Mixture-of-Experts (MoE) model, is now live after just nine weeks of training. It boasts an impressive 70.2% score on Terminal-Bench 2.1 in thinking mode and holds its own against much larger models on long-horizon coding tasks. The model's small size makes it uniquely suitable for complex work on local machines without sacrificing performance, making it a standout choice for developers with limited resources or bandwidth constraints.
Key Points
Laguna S 2.1 has a total of 118 billion parameters, with only 8 billion activated per token.
The model supports up to 1 million tokens in context window, both thinking and no-thinking modes.
Scores 70.2% on Terminal-Bench 2.1 in thinking mode; outperforms many larger models on long-horizon coding benchmarks.
Evaluation includes adversarial judging for reward hacking detection, ensuring robust performance metrics.
All trajectories from final evaluations are available at trajectories.poolside.ai for transparency.
Why It Matters
If you're working with limited resources or bandwidth constraints, Laguna S 2.1's compact size and strong performance make it a game-changer. Its ability to handle complex tasks on local machines without sacrificing efficiency can significantly enhance developer workflows.
Frequently Asked Questions
Why does this matter?
If you're working with limited resources or bandwidth constraints, Laguna S 2.1's compact size and strong performance make it a game-changer. Its ability to handle complex tasks on local machines without sacrificing efficiency can significantly enhance developer workflows.
What happened?
Laguna S 2.1, an 8B activated parameter model, launches with impressive performance on coding benchmarks despite its size. Its compact nature makes it ideal for local machine tasks.
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