fix docs

docs/source/en/_toctree.yml

@@ -382,6 +382,8 @@
         title: HerBERT
       - local: model_doc/ibert
         title: I-BERT
+      - local: model_doc/jamba
+        title: Jamba
       - local: model_doc/jukebox
         title: Jukebox
       - local: model_doc/led

src/transformers/models/jamba/modeling_jamba.py

@@ -225,11 +225,11 @@ class HybridMambaAttentionDynamicCache(DynamicCache):
 
     This cache has two sets of lists of tensors: `key_cache` and `value_cache` for attention cache and `conv_states`
     and `ssm_states` for mamba cache. Each of these lists has `num_layers` tensors. The expected shape for each tensor
-    For attention layers, `key_cache` and `value_cache` have a shape of `[batch_size, num_heads, seq_len, head_dim]`,
-    while `conv_states` and `ssm_states` have a shape of `[batch_size, 0]` (empty tensors).
-    For mamba layers, `key_cache` and `value_cache` have a shape of `[batch_size, 0]` (empty tensors),
-    while `conv_states` represents the convolution state and has a shape of `[batch_size, d_inner, d_conv]`,
-    and `ssm_states` represents the ssm state and has a shape of `[batch_size, d_inner, d_state]`.
+    For attention layers, `key_cache` and `value_cache` have a shape of `(batch_size, num_heads, seq_len, head_dim)`,
+    while `conv_states` and `ssm_states` have a shape of `(batch_size, 0)` (empty tensors).
+    For mamba layers, `key_cache` and `value_cache` have a shape of `(batch_size, 0)` (empty tensors),
+    while `conv_states` represents the convolution state and has a shape of `(batch_size, d_inner, d_conv)`,
+    and `ssm_states` represents the ssm state and has a shape of `(batch_size, d_inner, d_state)`.
     """
 
     def __init__(self, config, batch_size, dtype=torch.float16, device=None):
@@ -1304,9 +1304,9 @@ def _init_weights(self, module):
             A HybridMambaAttentionDynamicCache object containing pre-computed hidden-states (keys and values in the
             self-attention blocks and convolution and ssm states in the mamba blocks) that can be used (see
             `past_key_values` input) to speed up sequential decoding.
-            Key and value cache tensors have shape `[batch_size, num_heads, seq_len, head_dim]`.
-            Convolution and ssm states tensors have shape `[batch_size, d_inner, d_conv]` and
-            `[batch_size, d_inner, d_state]` respectively.
+            Key and value cache tensors have shape `(batch_size, num_heads, seq_len, head_dim)`.
+            Convolution and ssm states tensors have shape `(batch_size, d_inner, d_conv)` and
+            `(batch_size, d_inner, d_state)` respectively.
             See the `HybridMambaAttentionDynamicCache` class for more details.
 
             If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that
@@ -1605,6 +1605,22 @@ def forward(
                 can save memory, which becomes pretty significant for long sequences.
 
         Returns:
+
+        Example:
+
+        ```python
+        >>> from transformers import AutoTokenizer, JambaForCausalLM
+
+        >>> model = JambaForCausalLM.from_pretrained("ai21labs/Jamba-v0.1")
+        >>> tokenizer = AutoTokenizer.from_pretrained("ai21labs/Jamba-v0.1")
+
+        >>> prompt = "Hey, are you conscious? Can you talk to me?"
+        >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+        >>> # Generate
+        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+        "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
         ```"""
 
         output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions