DeepSeek
DeepSeek-V2 adopts innovative architectures to guarantee economical training and efficient inference： - For attention, we design MLA (Multi-head Latent Attention), which utilizes low-rank key-value union compression to eliminate the bottleneck of inference-time key-value cache, thus supporting efficient inference.
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56.2
Quality Score
1273
Arena ELO
236B
Parameters
16K
Context
This measures the amount of verifiable public evidence we have, not how capable the model is. A missing field means it has not been verified yet, not that its value is zero.
18 of 22 public signals
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43.1K
Downloads
335
Likes
Apr 2024
Released
5/5 signals
4/4 signals
3/5 signals
3/4 signals
3/4 signals
Parameters
236B
Training compute
Not reported
Dataset scale
Not reported
Base model
Not reported
Gaps we are still tracking
Metadata sources
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Free access notes: Local Ollama runtime
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