# Copyright 2025 The EasyDeL Author @erfanzar (Erfan Zare Chavoshi).
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from functools import partial
import chex
import jax
import jax.numpy as jnp
from eformer import common_types
from eformer.escale import apply_logical_sharding
from ejkernel.types import MaskInfo
from flax import nnx as nn
from jax.ad_checkpoint import checkpoint_name
from jaxtyping import Array, Bool, Float, Int
from easydel.infra.base_module import EasyDeLBaseModule
from easydel.infra.factory import TaskType, register_module
from easydel.infra.modeling_outputs import (
BaseModelOutput,
CausalLMOutput,
DecoderLayerOutput,
SequenceClassifierOutput,
)
from easydel.infra.utils import ArrayParam, auto_remat, block_wise_ffn, get_dot_general_by_bits
from easydel.layers.attention import FlexibleAttentionModule
from easydel.layers.attention_unified import UnifiedAttention
from easydel.layers.base_modules import BaseCausalLMModule, BaseSequenceClassificationModule
from easydel.layers.caching import (
RaggedPagesCache,
RaggedPagesCacheView,
RaggedPagesMetadata,
TransformerCache,
TransformerCacheView,
TransformerMetadata,
)
from easydel.layers.linear import ColumnParallelLinear, RowParallelLinear
from .cohere2_configuration import Cohere2Config
[docs]class Cohere2LayerNorm(nn.Module):
"""Cohere Layer Normalization.
Attributes:
dim (Union[int, tuple]): The dimension(s) to normalize over.
eps (float): A small epsilon value to prevent division by zero.
dtype (jnp.dtype): The data type for computation.
param_dtype (jnp.dtype): The data type for the parameters.
rngs (Optional[nn.Rngs]): Random number generators.
"""
kernel_init = staticmethod(nn.initializers.ones)
def __init__(
self,
dim: int | tuple,
eps: float = 1e-6,
dtype: jnp.dtype = jnp.bfloat16,
param_dtype: jnp.dtype = jnp.bfloat16,
rngs: nn.Rngs = None,
):
super().__init__()
if rngs is None:
rngs = nn.Rngs(0)
self.dim = dim
self.eps = eps
self.dtype = dtype
self.param_dtype = param_dtype
self.kernel = ArrayParam.bound(
shape=(self.dim,) if isinstance(self.dim, int) else self.dim,
dtype=self.param_dtype,
init_method="ones",
key=rngs.params(),
)
def _norm(self, x: jnp.ndarray) -> jnp.ndarray:
"""Computes the Layer Normalization for a given input tensor."""
mean = jnp.mean(x, -1, keepdims=True)
variance = jnp.mean(jnp.pow((x - mean), 2), -1, keepdims=True)
return (x - mean) * jax.lax.rsqrt(variance + self.eps)
def __call__(self, x: jnp.ndarray) -> jnp.ndarray:
"""Applies Layer Normalization to the input tensor.
Args:
x (jnp.ndarray): The input tensor.
Returns:
jnp.ndarray: The normalized output tensor.
"""
if self.dtype in [
jnp.float8_e4m3b11fnuz,
jnp.float8_e4m3fn,
jnp.float8_e4m3fnuz,
jnp.float8_e5m2,
jnp.float8_e5m2fnuz,
]:
x = x.astype(jnp.float32)
else:
x = x.astype(jnp.promote_types(self.dtype, jnp.float32))
output = self._norm(x).astype(self.dtype)
weight = self.kernel.value.astype(self.dtype)
return output * weight
[docs]class Cohere2Attention(UnifiedAttention):
"""Cohere2 Attention with layer-specific sliding window and conditional RoPE.
Inherits from UnifiedAttention with Cohere2-specific customizations:
- Layer-specific sliding window (only applies to sliding_attention layers)
- Conditional RoPE application (only when sliding window is enabled)
"""
def __init__(
self,
config: Cohere2Config,
dtype: jnp.dtype = jnp.bfloat16,
param_dtype: jnp.dtype = jnp.bfloat16,
precision: jax.lax.PrecisionLike = None,
*,
rngs: nn.Rngs,
layer_idx: int,
) -> None:
"""Initialize Cohere2Attention with layer-specific configuration.
Args:
config: Model configuration
layer_idx: Layer index for determining sliding window usage
dtype: Data type for computations
param_dtype: Data type for parameters
precision: JAX precision setting
rngs: Random number generators
"""
super().__init__(
config,
dtype,
param_dtype,
precision,
rngs=rngs,
layer_idx=layer_idx,
attention_type="standard",
causal=True,
sliding_window=config.sliding_window if config.layer_types[layer_idx] == "sliding_attention" else None,
)
def _create_rotary(self, config: Cohere2Config, dtype: jnp.dtype):
"""Create Cohere2-specific rotary embedding layer."""
return config.get_basic_rope(dtype, self.head_dim, self.head_dim, False)
def _create_attention_performer(self, config: Cohere2Config, rngs: nn.Rngs):
"""Create attention performer with Cohere2's attention dropout."""
return FlexibleAttentionModule(
rngs=rngs,
dropout_prob=config.attention_dropout,
base_config=config,
softmax_scale=self.head_dim**-0.5,
)
def _apply_rotary(self, query_states, key_states, position_ids, frequencies):
"""Override to apply RoPE only when sliding window is enabled (Cohere2-specific)."""
if self.sliding_window is not None:
return self.rotary(
query=query_states,
key=key_states,
positions=position_ids,
frequencies=frequencies,
)
return query_states, key_states
[docs]class Cohere2MLP(nn.Module):
"""Feed-forward network used in Cohere v2 decoder layers."""
def __init__(
self,
config: Cohere2Config,
dtype: jnp.dtype = jnp.bfloat16,
param_dtype: jnp.dtype = jnp.bfloat16,
precision: jax.lax.PrecisionLike = None,
*,
rngs: nn.Rngs,
):
self.config = config
self.dtype = dtype
self.param_dtype = param_dtype
self.precision = precision
column_parallel_linear = partial(
ColumnParallelLinear,
dtype=dtype,
param_dtype=param_dtype,
use_bias=False,
kernel_init=jax.nn.initializers.normal(config.initializer_range),
precision=self.precision,
rngs=rngs,
**get_dot_general_by_bits(config.bits, config.easy_method),
)
row_parallel_linear = partial(
RowParallelLinear,
dtype=dtype,
param_dtype=param_dtype,
use_bias=False,
kernel_init=jax.nn.initializers.normal(config.initializer_range),
precision=self.precision,
rngs=rngs,
**get_dot_general_by_bits(config.bits, config.easy_method),
)
self.gate_proj = column_parallel_linear(config.hidden_size, config.intermediate_size)
self.down_proj = row_parallel_linear(config.intermediate_size, config.hidden_size)
self.up_proj = column_parallel_linear(config.hidden_size, config.intermediate_size)
def __call__(
self, hidden_states: Float[Array, "batch seq_len hidden_dim"]
) -> Float[Array, "batch seq_len hidden_dim"]:
hidden_states = apply_logical_sharding(
hidden_states,
dynamic_axes=common_types.HiddenStateSharding,
partition_manager=self.config.partition_manager,
)
gate = jax.nn.silu(checkpoint_name(self.gate_proj(hidden_states), name="mlp_gate"))
up = checkpoint_name(self.up_proj(hidden_states), name="mlp_up")
hidden_states = checkpoint_name(self.down_proj(gate * up), name="mlp_down")
hidden_states = apply_logical_sharding(
hidden_states,
dynamic_axes=common_types.HiddenStateSharding,
partition_manager=self.config.partition_manager,
)
return hidden_states
[docs]class Cohere2Block(nn.Module):
"""Cohere v2 transformer block combining attention and MLP."""
def __init__(
self,
config: Cohere2Config,
dtype: jnp.dtype = jnp.bfloat16,
param_dtype: jnp.dtype = jnp.bfloat16,
precision: jax.lax.PrecisionLike = None,
*,
rngs: nn.Rngs,
layer_idx: int,
) -> None:
super().__init__()
self.config = config
self.layer_idx = layer_idx
self.dtype = dtype
self.param_dtype = param_dtype
self.precision = precision
self.rngs = rngs
attn_block = Cohere2Attention
mlp_block = Cohere2MLP
attn_block, mlp_block = auto_remat(
attn_block,
mlp_block,
policy=config.gradient_checkpointing,
save_names=config.gradient_checkpointing_targets,
exclude_names=config.gradient_checkpointing_targets,
)
self.self_attn = attn_block(
config,
layer_idx=layer_idx,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
self.mlp = mlp_block(
config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
self.input_layernorm = Cohere2LayerNorm(
self.config.hidden_size,
eps=self.config.layer_norm_eps,
dtype=dtype,
param_dtype=param_dtype,
rngs=rngs,
)
self.is_sliding = (layer_idx + 1) % self.config.sliding_window_pattern != 0
self.sliding_window = config.sliding_window
def __call__(
self,
hidden_states: Float[Array, "batch seq_len hidden_dim"],
mask_info: MaskInfo | None,
position_ids: Int[Array, "batch seq_len"],
mode: common_types.RUNTIME_MODE_TYPES, # type:ignore
cache_view: TransformerCacheView | RaggedPagesCacheView | None = None,
cache_metadata: TransformerMetadata | RaggedPagesMetadata | None = None,
output_attentions: bool = False,
frequencies: Float[Array, "seq_len head_dim"] | None = None,
):
"""
Forward pass of the module block.
Args:
hidden_states (chex.Array): Input hidden states.
attention_mask (chex.Array): Mask to apply on the attention scores.
position_ids (chex.Array): Position indices for the tokens.
causal_mask (chex.Array): Causal mask for ensuring autoregressive behavior.
segment_ids (tp.Optional[chex.Array]): Segment IDs for segment-based attention (optional).
deterministic (bool): If True, disables dropout for deterministic behavior.
init_cache (bool): If True, initializes cache for caching keys and values.
output_attentions (bool): If True, outputs attention weights alongside the hidden states.
fcm_mask (tp.Optional[chex.Array]): fcm mask to be combined with attn mask and causal mask.
Returns:
tp.Tuple[chex.Array, chex.Array]: A tuple containing the attention output and the attention weights.
"""
residual = hidden_states
hidden_states = self.input_layernorm(hidden_states)
hidden_states = apply_logical_sharding(
hidden_states,
dynamic_axes=common_types.HiddenStateSharding,
partition_manager=self.config.partition_manager,
)
attn_outputs = self.self_attn(
hidden_states,
mask_info,
position_ids,
mode,
cache_view,
cache_metadata,
output_attentions,
frequencies,
)
feed_forward_input = hidden_states
if self.config.use_scan_mlp:
feed_forward_hidden_states = block_wise_ffn(
self.mlp,
feed_forward_input,
self.config.scan_mlp_chunk_size,
)
else:
feed_forward_hidden_states = self.mlp(feed_forward_input)
hidden_states = attn_outputs.attention_output + feed_forward_hidden_states + residual
hidden_states = apply_logical_sharding(
hidden_states,
dynamic_axes=common_types.HiddenStateSharding,
partition_manager=self.config.partition_manager,
)
return DecoderLayerOutput(
hidden_states=hidden_states,
attention_weight=attn_outputs.attention_weight,
router_logits=None,
gate_loss=None,
cache_view=attn_outputs.cache_view,
)
[docs]@register_module(TaskType.BASE_MODULE, config=Cohere2Config, model_type="cohere2")
class Cohere2Model(EasyDeLBaseModule):
"""Decoder-only Cohere v2 model with embeddings, blocks, and final norm."""
def __init__(
self,
config: Cohere2Config,
dtype: jnp.dtype = jnp.bfloat16,
param_dtype: jnp.dtype = jnp.bfloat16,
precision: jax.lax.PrecisionLike = None,
*,
rngs: nn.Rngs,
):
super().__init__(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
embed_block = auto_remat(
nn.Embed,
policy=config.gradient_checkpointing,
save_names=config.gradient_checkpointing_targets,
exclude_names=config.gradient_checkpointing_targets,
)
self.embed_tokens = embed_block(
config.vocab_size,
config.hidden_size,
embedding_init=nn.initializers.normal(stddev=config.initializer_range),
dtype=dtype,
param_dtype=param_dtype,
rngs=rngs,
)
self.layers = [
Cohere2Block(
config=config,
layer_idx=idx,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
for idx in range(config.num_hidden_layers)
]
self.norm = Cohere2LayerNorm(
self.config.hidden_size,
eps=self.config.layer_norm_eps,
dtype=dtype,
param_dtype=param_dtype,
)
def __call__(
self,
input_ids: Int[Array, "batch seq_len"] | None = None,
inputs_embeds: Float[Array, "batch seq_len hidden_dim"] | None = None,
attention_mask: Bool[Array, "batch seq_len"] | None = None,
mask_info: MaskInfo | None = None,
position_ids: Int[Array, "batch seq_len"] | None = None,
mode: common_types.RUNTIME_MODE_TYPES | None = None, # type:ignore
past_key_values: TransformerCache | RaggedPagesCache | None = None,
cache_metadata: TransformerMetadata | RaggedPagesMetadata | None = None,
output_attentions: bool | None = None,
output_hidden_states: bool | None = None,
) -> BaseModelOutput:
if (input_ids is None) ^ (inputs_embeds is not None):
raise ValueError(
"You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
)
if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids.astype("i4"))
sequence_length = inputs_embeds.shape[1]
all_attentions = () if output_attentions else None
all_hidden_states = () if output_hidden_states else None
assert sequence_length <= self.config.max_position_embeddings, (
f"Maximum Position Embedding Reached ! "
f"(Excepted <= {self.config.max_position_embeddings} got {sequence_length})"
)
mask_info = MaskInfo.dynamic_init(
mask_info=mask_info,
input_ids=input_ids,
inputs_embeds=inputs_embeds,
attention_mask=attention_mask,
)
if position_ids is None:
position_ids = mask_info.q_position_ids
hidden_states = inputs_embeds
if mode is None:
mode = (
common_types.MODE_DECODE
if sequence_length == 1 and past_key_values is not None
else common_types.MODE_TRAIN
)
if past_key_values is None:
past_key_values = TransformerCache.init_empty(len(self.layers))
hidden_states = apply_logical_sharding(
hidden_states,
dynamic_axes=common_types.HiddenStateSharding,
partition_manager=self.config.partition_manager,
)
for idx, block in enumerate(self.layers):
if output_hidden_states:
all_hidden_states += (hidden_states,)
layer_outputs = block(
hidden_states=hidden_states,
mask_info=mask_info,
position_ids=position_ids,
mode=mode,
cache_view=past_key_values.views[idx],
cache_metadata=cache_metadata,
output_attentions=output_attentions,
frequencies=self.frequencies,
)
hidden_states = layer_outputs.hidden_states
if output_attentions:
all_attentions += (layer_outputs.attention_weight,)
past_key_values[idx] = layer_outputs.cache_view
hidden_states = self.norm(hidden_states)
if output_hidden_states:
all_hidden_states = (*all_hidden_states, hidden_states)
return BaseModelOutput(
last_hidden_state=hidden_states,
hidden_states=all_hidden_states,
attentions=all_attentions,
past_key_values=past_key_values,
)
[docs] def get_encoder(self) -> nn.Module:
"""
Returns the encoder part of the model's graph definition.
For Cohere2Model (decoder-only), this is not applicable.
"""
raise NotImplementedError("Cohere2Model is a decoder-only model and does not have a separate encoder.")
[docs] def get_decoder(self) -> nn.Module:
"""
Returns the decoder part of the model's graph definition.
For Cohere2Model, this is the model itself.
"""
return self
[docs] def get_lm_head(self) -> nn.Module:
"""
Returns the language model head of the module.
Cohere2Model does not include the lm_head.
"""
raise NotImplementedError("Cohere2Model does not include the language model head. See Cohere2ForCausalLM.")
[docs] def get_embedding(self) -> nn.Module:
"""
Returns the embedding layer of the module.
"""
return self.embed_tokens
[docs]@register_module(TaskType.CAUSAL_LM, config=Cohere2Config, model_type="cohere2")
class Cohere2ForCausalLM(BaseCausalLMModule[Cohere2Model, Cohere2Config]):
"""Cohere2 model with a Causal Language Modeling head."""
_task_type = TaskType.CAUSAL_LM
_model_type = "cohere2"
_config_class = Cohere2Config
def __init__(
self,
config: Cohere2Config,
dtype: jnp.dtype = jnp.bfloat16,
param_dtype: jnp.dtype = jnp.bfloat16,
precision: jax.lax.PrecisionLike = None,
*,
rngs: nn.Rngs,
):
super().__init__(
config=config,
base_model_class=Cohere2Model,
base_model_name="model",
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
lm_head_bias=False,
)
self.logit_scale = self.config.logit_scale
def __call__(
self,
input_ids: Int[Array, "batch seq_len"] | None = None,
inputs_embeds: Float[Array, "batch seq_len hidden_dim"] | None = None,
attention_mask: Bool[Array, "batch seq_len"] | None = None,
mask_info: MaskInfo | None = None,
position_ids: Int[Array, "batch seq_len"] | None = None,
mode: common_types.RUNTIME_MODE_TYPES | None = None, # type:ignore
past_key_values: TransformerCache | RaggedPagesCache | None = None,
cache_metadata: TransformerMetadata | RaggedPagesMetadata | None = None,
apply_lm_head: bool = True,
output_attentions: bool | None = None,
output_hidden_states: bool | None = None,
) -> CausalLMOutput:
"""
Forward pass through the Cohere module.
Args:
input_ids (chex.Array): Input tensor containing token IDs.
attention_mask (chex.Array): Mask for attention.
position_ids (chex.Array): Positional indices.
segment_ids (tp.Optional[chex.Array]): Segment IDs for different input parts.
inputs_embeds (tp.Optional[chex.Array]): Embedded input tensor.
output_attentions (tp.Optional[bool]): If True, output attention weights.
output_hidden_states (tp.Optional[bool]): If True, output hidden states.
init_cache (bool): If True, initialize cache for decoding.
deterministic (bool): If True, disable dropout.
Returns:
CausalLMOutput | tp.Tuple: Model output, either as a named tuple or a standard tuple.
"""
outputs = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
mask_info=mask_info,
position_ids=position_ids,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
mode=mode,
past_key_values=past_key_values,
cache_metadata=cache_metadata,
inputs_embeds=inputs_embeds,
)
hidden_states = outputs.last_hidden_state
hidden_states = apply_logical_sharding(
hidden_states,
dynamic_axes=common_types.HiddenStateSharding,
partition_manager=self.config.partition_manager,
)
lm_logits = None
if apply_lm_head:
lm_logits = self.apply_lm_head(hidden_states)
return CausalLMOutput(
logits=lm_logits,
hidden_states=outputs.hidden_states,
last_hidden_state=outputs.last_hidden_state,
attentions=outputs.attentions,
past_key_values=outputs.past_key_values,
)
[docs] def apply_lm_head(self, hidden_states: chex.Array) -> chex.Array:
"""
Applies the language model head to the hidden states.
Args:
hidden_states (chex.Array): The last hidden states from the model.
Returns:
chex.Array: The logits after applying the language model head.
"""
lm_logits = self.lm_head(hidden_states)
if self.logit_scale is not None:
lm_logits *= self.logit_scale
return lm_logits
[docs] def get_encoder(self) -> nn.Module:
"""
Returns the encoder part of the model's graph definition.
For Cohere2ForCausalLM (decoder-only), this is not applicable.
"""
raise NotImplementedError("Cohere2ForCausalLM is a decoder-only model and does not have a separate encoder.")
[docs] def get_decoder(self) -> nn.Module:
"""
Returns the decoder part of the model's graph definition.
For Cohere2ForCausalLM, this is the underlying Cohere2Model.
"""
return self.model.get_decoder() # self.model is the Cohere2Model instance
[docs] def get_lm_head(self) -> nn.Module:
"""
Returns the language model head of the module.
"""
return self.lm_head
[docs] def get_embedding(self) -> nn.Module:
"""
Returns the embedding layer of the module.
"""
# Access the embedding layer through the decoder (Cohere2Model)
return self.model.get_embedding() # Leverages Cohere2Model's get_embedding
[docs]@register_module(TaskType.SEQUENCE_CLASSIFICATION, config=Cohere2Config, model_type="cohere2")
class Cohere2ForSequenceClassification(BaseSequenceClassificationModule[Cohere2Model, Cohere2Config]):
"""Cohere2 model for sequence classification."""
_task_type = TaskType.SEQUENCE_CLASSIFICATION
_model_type = "cohere2"
_config_class = Cohere2Config
def __init__(
self,
config: Cohere2Config,
dtype: jnp.dtype = jnp.bfloat16,
param_dtype: jnp.dtype = jnp.bfloat16,
precision: jax.lax.PrecisionLike = None,
*,
rngs: nn.Rngs,
):
super().__init__(
config=config,
base_model_class=Cohere2Model,
base_model_name="model",
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
classifier_name="score",
classifier_bias=False,
)
def __call__(
self,
input_ids: Int[Array, "batch seq_len"] | None = None,
inputs_embeds: Float[Array, "batch seq_len hidden_dim"] | None = None,
attention_mask: Bool[Array, "batch seq_len"] | None = None,
mask_info: MaskInfo | None = None,
position_ids: Int[Array, "batch seq_len"] | None = None,
output_attentions: bool | None = None,
output_hidden_states: bool | None = None,
mode: common_types.RUNTIME_MODE_TYPES | None = None, # type:ignore
past_key_values: TransformerCache | RaggedPagesCache | None = None,
cache_metadata: TransformerMetadata | RaggedPagesMetadata | None = None,
) -> SequenceClassifierOutput:
transformer_outputs = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
mask_info=mask_info,
position_ids=position_ids,
mode=mode,
past_key_values=past_key_values,
cache_metadata=cache_metadata,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
inputs_embeds=inputs_embeds,
)
hidden_states = transformer_outputs.last_hidden_state
logits = self.score(hidden_states)
if input_ids is not None:
batch_size = input_ids.shape[0]
else:
batch_size = inputs_embeds.shape[0]
if self.config.pad_token_id is None and batch_size != 1:
raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
if self.config.pad_token_id is None:
sequence_lengths = -1
else:
if input_ids is not None:
sequence_lengths = jnp.argmax(jnp.equal(input_ids, self.config.pad_token_id).astype("i4"), -1) - 1
sequence_lengths = sequence_lengths % input_ids.shape[-1]
else:
sequence_lengths = -1
pooled_logits = logits[jnp.arange(batch_size), sequence_lengths]
return SequenceClassifierOutput(
logits=pooled_logits,
past_key_values=past_key_values,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
)
[docs] def get_encoder(self) -> nn.Module:
"""
Returns the encoder part of the model's graph definition.
For Cohere2ForSequenceClassification (decoder-only), this is not applicable.
"""
raise NotImplementedError(
"Cohere2ForSequenceClassification is a decoder-only model and does not have a separate encoder."
)
[docs] def get_decoder(self) -> nn.Module:
"""
Returns the decoder part of the model's graph definition.
For Cohere2ForSequenceClassification, this is the underlying Cohere2Model.
"""
return self.model # self.model is the Cohere2Model instance
[docs] def get_lm_head(self) -> nn.Module:
"""
Returns the language model head of the module.
Cohere2ForSequenceClassification uses a classification head instead.
"""
raise NotImplementedError(
"Cohere2ForSequenceClassification uses a classification head (self.score), not an lm_head."
)
[docs] def get_embedding(self) -> nn.Module:
"""
Returns the embedding layer of the module.
"""
# Access the embedding layer through the decoder (Cohere2Model)
return self.model.get_embedding() # Leverages Cohere2Model's get_embedding