fix quality

src/transformers/models/cohere/modeling_cohere.py

@@ -161,7 +161,6 @@ def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
     return q_embed.to(dtype=dtype), k_embed.to(dtype=dtype)
 
 
-# Copied from transformers.models.llama.modeling_llama.LlamaMLP Llama->Cohere
 class CohereMLP(nn.Module):
     def __init__(self, config):
         super().__init__()

src/transformers/models/llama/configuration_llama.py

@@ -94,10 +94,12 @@ class LlamaConfig(PretrainedConfig):
             these scaling strategies behave:
             https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
             experimental feature, subject to breaking API changes in future versions.
-        attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
+        attention_bias (`bool`, *optional*, defaults to `False`):
             Whether to use a bias in the query, key, value and output projection layers during self-attention.
         attention_dropout (`float`, *optional*, defaults to 0.0):
             The dropout ratio for the attention probabilities.
+        mlp_bias (`bool`, *optional*, defaults to `False`):
+            Whether to use a bias in up_proj, down_proj and gate_proj layers in the MLP layers.
 
     ```python
     >>> from transformers import LlamaModel, LlamaConfig