τemplar (SN3)의 역사에서 주요 마일스톤과 중요한 이벤트.
τemplar (SN3)에 대한 마일스톤 15개 표시 중
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.
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.
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.
The research team published a paper demonstrating up to a 30% cost-efficiency improvement for specific AI workloads compared to traditional cloud providers.
The research team published a paper demonstrating up to a 30% cost-efficiency improvement for specific AI workloads compared to traditional cloud providers.
The network grew significantly, surpassing 1,000 active, concurrent miners contributing computational power to the protocol.
The network grew significantly, surpassing 1,000 active, concurrent miners contributing computational power to the protocol.
The network grew significantly, surpassing 1,000 active, concurrent miners contributing computational power to the 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 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 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.
Templar successfully launched on the Bittensor mainnet after winning a registration slot for Subnet 3, establishing itself as a decentralized AI training protocol.
Templar successfully launched on the Bittensor mainnet after winning a registration slot for Subnet 3, establishing itself as a decentralized AI training protocol.
Templar successfully launched on the Bittensor mainnet after winning a registration slot for Subnet 3, establishing itself as a decentralized AI training protocol.