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c1638a56f1
Deepseek V2 is a MoE model from Deepseek. Relevant variations
compared to other models:
- Grouped top-K in expert selection.
- mscale in yarn is calculated using the `mscale` and `mscale_all_dim`
configuration options.
- `mscale_all_dim` is also used in scaling attention softmax.
- Permuting of the query/key representations before applying rotary
embeddings.
- Some projections cannot be sharded (`q_a_proj`, `kv_a_proj_with_mqa`).
So, we need weight loads that supports quantized weights. To this
end `{Weights,WeightLoader}.get_weight` was added.
- The query/key head dimensionality differs from that of the value,
so we need to pad during attention.
- Heads with size 192, needs an extension to our paged attention
fork and we need to ensure that the KV cache is allocated with the
correct size.
- Shared experts.
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| .. | ||
| basic_tutorials | ||
| conceptual | ||
| _toctree.yml | ||
| architecture.md | ||
| index.md | ||
| installation_amd.md | ||
| installation_gaudi.md | ||
| installation_inferentia.md | ||
| installation_intel.md | ||
| installation_nvidia.md | ||
| installation.md | ||
| messages_api.md | ||
| quicktour.md | ||
| supported_models.md | ||
| usage_statistics.md | ||