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config.json

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+{
+  "architectures": [
+    "RwkvForCausalLM"
+  ],
+  "auto_map": {
+      "AutoConfig": "configuration_rwkv5.Rwkv5Config",
+      "AutoModelForCausalLM": "modeling_rwkv5.Rwkv5ForCausalLM"
+  },
+  "attention_hidden_size": 4096,
+  "bos_token_id": 0,
+  "context_length": 4096,
+  "eos_token_id": 0,
+  "head_size": 64,
+  "hidden_size": 4096,
+  "intermediate_size": null,
+  "layer_norm_epsilon": 1e-05,
+  "model_type": "rwkv5",
+  "model_version": "5_2",
+  "num_hidden_layers": 32,
+  "rescale_every": 6,
+  "tie_word_embeddings": false,
+  "transformers_version": "4.34.0",
+  "use_cache": true,
+  "vocab_size": 65536
+}

configuration_rwkv5.py

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+# coding=utf-8
+# Copyright 2023 The OpenAI Team Authors and HuggingFace Inc. team.
+# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
+#
+# 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
+#
+#     http://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.
+""" RWKV configuration"""
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+RWKV5_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
+
+
+class Rwkv5Config(PretrainedConfig):
+    """
+    This is the configuration class to store the configuration of a [`Rwkv5Model`]. It is used to instantiate a RWKV5
+    model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
+    defaults will yield a similar configuration to that of the RWVK-4
+    [RWKV/rwkv-5-world-1b5](https://huggingface.co/RWKV/rwkv-5-world-1b5) architecture.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 65536):
+            Vocabulary size of the RWKV5 model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`Rwkv5Model`].
+        hidden_size (`int`, *optional*, defaults to 768):
+            Dimensionality of the embeddings and hidden states.
+        num_hidden_layers (`int`, *optional*, defaults to 24):
+            Number of hidden layers in the model.
+        attention_hidden_size (`int`, *optional*):
+            Dimensionality of the attention hidden states. Will default to `hidden_size` if unset.
+        num_attention_heads (`int`, *optional*, defaults to 64):
+            The attention heads to use in rwkv5 self_attention module.
+        head_size (`int`, *optional*, defaults to 64): head_size of rwkv5 self_attention module.
+        intermediate_size (`int`, *optional*):
+            Dimensionality of the inner feed-forward layers. Will default to 4 times `hidden_size` if unset.
+        layer_norm_epsilon (`float`, *optional*, defaults to 1e-05):
+            The epsilon to use in the layer normalization layers.
+        bos_token_id (`int`, *optional*, defaults to 0):
+            The id of the beginning of sentence token in the vocabulary. Defaults to 0 as RWKV5 uses the same tokenizer
+            as GPTNeoX.
+        eos_token_id (`int`, *optional*, defaults to 0):
+            The id of the end of sentence token in the vocabulary. Defaults to 0 as RWKV5 uses the same tokenizer as
+            GPTNeoX.
+        rescale_every (`int`, *optional*, defaults to 6):
+            At inference, the hidden states (and weights of the correponding output layers) are divided by 2 every
+            `rescale_every` layer. If set to 0 or a negative number, no rescale is done.
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether or not to tie the word embeddings with the input token embeddings.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last state.
+
+
+    Example:
+
+    ```python
+    >>> from transformers import Rwkv5Config, Rwkv5Model
+
+    >>> # Initializing a Rwkv5 configuration
+    >>> configuration = Rwkv5Config()
+
+    >>> # Initializing a model (with random weights) from the configuration
+    >>> model = Rwkv5Model(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "rwkv5"
+
+    def __init__(
+        self,
+        vocab_size=65536,
+        hidden_size=768,
+        num_hidden_layers=24,
+        attention_hidden_size=None,
+        num_attention_heads=64,
+        head_size=64,
+        intermediate_size=None,
+        layer_norm_epsilon=1e-5,
+        bos_token_id=0,
+        eos_token_id=0,
+        rescale_every=6,
+        tie_word_embeddings=False,
+        use_cache=True,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.num_hidden_layers = num_hidden_layers
+        self.attention_hidden_size = attention_hidden_size if attention_hidden_size is not None else hidden_size
+        self.num_attention_heads = num_attention_heads
+        self.head_size = head_size
+        self.intermediate_size = None
+        self.layer_norm_epsilon = layer_norm_epsilon
+        self.rescale_every = rescale_every
+        self.use_cache = use_cache
+
+        self.bos_token_id = bos_token_id
+        self.eos_token_id = eos_token_id
+
+        super().__init__(
+            tie_word_embeddings=tie_word_embeddings, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs
+        )

generation_config.json

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+{
+    "chat_format": "chatml",
+    "eos_token_id": 0,
+    "pad_token_id": 0,
+    "max_window_size": 4096,
+    "max_new_tokens": 4096,
+    "do_sample": true,
+    "top_k": 0,
+    "top_p": 0.1,
+    "repetition_penalty": 1.0,
+    "transformers_version": "4.31.1"
+}

special_tokens_map.json

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+{}