마일스톤

Templar successfully launched on the Bittensor mainnet after winning a registration slot for Subnet 3, establishing itself as a decentralized AI training protocol.

The first 'Crusade' campaign was initiated, focusing on training a specialized Large Language Model for code generation, serving as a major proof-of-concept for the network's capabilities.

The network grew significantly, surpassing 1,000 active, concurrent miners contributing computational power to the protocol.

Completed the pre-training of Covenant-72B, a 72-billion parameter LLM, which was the largest collaborative, globally distributed pre-training run at the time.

The research team published a paper demonstrating up to a 30% cost-efficiency improvement for specific AI workloads compared to traditional cloud providers.

Templar successfully launched on the Bittensor mainnet after winning a registration slot for Subnet 3, establishing itself as a decentralized AI training protocol.

The first 'Crusade' campaign was initiated, focusing on training a specialized Large Language Model for code generation, serving as a major proof-of-concept for the network's capabilities.

The network grew significantly, surpassing 1,000 active, concurrent miners contributing computational power to the protocol.

Completed the pre-training of Covenant-72B, a 72-billion parameter LLM, which was the largest collaborative, globally distributed pre-training run at the time.

The research team published a paper demonstrating up to a 30% cost-efficiency improvement for specific AI workloads compared to traditional cloud providers.