OLM API Reference

`olm.models.microsoft.phi4`

Source: src/olm/models/microsoft/phi4.py:1

Classes

Phi4Block(embed_dim: int, intermediate_size: int, num_heads: int, num_kv_heads: int, max_seq_len: int, dropout: float, rope_theta: float)

Bases: olm.nn.structure.block.Block

Source: src/olm/models/microsoft/phi4.py:10

A single Transformer block for Phi 4.

Structure

x = x + GQA(RMSNorm(x)) x = x + SwiGLU(RMSNorm(x))

Parameters

  • embed_dim (int): Model dimension.
  • intermediate_size (int): FFN hidden dimension.
  • num_heads (int): Number of attention heads.
  • num_kv_heads (int): Number of KV heads.
  • max_seq_len (int): Max sequence length.
  • dropout (float): Dropout probability.
  • rope_theta (float): RoPE base.

Methods

forward(self, x: torch.Tensor) -> torch.Tensor (inherited from Block)

Source: src/olm/nn/structure/block.py:26

Apply each block to the input in sequence.

Parameters

  • x: Input tensor.

Returns

Output tensor after all blocks have been applied.

Phi4Model(vocab_size: int, embed_dim: int, intermediate_size: int, num_layers: int, num_heads: int, num_kv_heads: int, max_seq_len: int, rope_theta: float = 250000.0, dropout: float = 0.0, tie_weights: bool = True)

Bases: olm.nn.structure.block.Block

Source: src/olm/models/microsoft/phi4.py:73

Base class for Phi 4 models.

Structure

Embedding -> [Phi4Block] x N -> RMSNorm -> tied OutputHead.

Forward

Accepts token IDs shaped [batch, seq_len] and returns logits shaped [batch, seq_len, vocab_size].

Implementation Note

This implementation uses standard Rotary Positional Embeddings (RoPE) parameterized via rope_theta.

Methods

forward(self, x: torch.Tensor) -> torch.Tensor (inherited from Block)

Source: src/olm/nn/structure/block.py:26

Apply each block to the input in sequence.

Parameters

  • x: Input tensor.

Returns

Output tensor after all blocks have been applied.

Phi4_14B()

Bases: olm.models.microsoft.phi4.Phi4Model

Source: src/olm/models/microsoft/phi4.py:130

Phi-4 14B Model.

Methods

forward(self, x: torch.Tensor) -> torch.Tensor (inherited from Block)

Source: src/olm/nn/structure/block.py:26

Apply each block to the input in sequence.

Parameters

  • x: Input tensor.

Returns

Output tensor after all blocks have been applied.