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added_tokens.json

@@ -0,0 +1,13 @@
+{
+  "<|assistant|>": 32001,
+  "<|endoftext|>": 32000,
+  "<|end|>": 32007,
+  "<|placeholder1|>": 32002,
+  "<|placeholder2|>": 32003,
+  "<|placeholder3|>": 32004,
+  "<|placeholder4|>": 32005,
+  "<|placeholder5|>": 32008,
+  "<|placeholder6|>": 32009,
+  "<|system|>": 32006,
+  "<|user|>": 32010
+}

chat_template.jinja

@@ -0,0 +1,5 @@
+{% for message in messages %}{% if (message['role'] == 'user') %}{{'<|user|>' + '
+' + message['content'] + '<|end|>' + '
+' + '<|assistant|>' + '
+'}}{% elif (message['role'] == 'assistant') %}{{message['content'] + '<|end|>' + '
+'}}{% endif %}{% endfor %}

config.json

@@ -0,0 +1,180 @@
+{
+  "architectures": [
+    "Phi3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "auto_map": {
+    "AutoConfig": "configuration_phi3.Phi3Config",
+    "AutoModelForCausalLM": "modeling_phi3.Phi3ForCausalLM"
+  },
+  "bos_token_id": 1,
+  "dtype": "bfloat16",
+  "embd_pdrop": 0.0,
+  "eos_token_id": 32000,
+  "hidden_act": "silu",
+  "hidden_size": 5120,
+  "initializer_range": 0.02,
+  "intermediate_size": 17920,
+  "max_position_embeddings": 131072,
+  "model_type": "phi3",
+  "num_attention_heads": 40,
+  "num_hidden_layers": 40,
+  "num_key_value_heads": 10,
+  "original_max_position_embeddings": 4096,
+  "pad_token_id": null,
+  "quantization_config": {
+    "group_size": 128,
+    "inference_bitwidth": 4,
+    "master_bitwidth": 8,
+    "modules_to_not_convert": [
+      "lm_head",
+      "norm"
+    ],
+    "quant_dtype": "float16",
+    "quant_method": "matgptq_quant"
+  },
+  "resid_pdrop": 0.0,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": {
+    "long_factor": [
+      1.0,
+      1.0,
+      1.0,
+      1.0,
+      1.0,
+      1.0,
+      1.0,
+      1.0,
+      1.0,
+      1.0,
+      1.0,
+      1.0,
+      1.0,
+      1.25,
+      1.25,
+      1.5,
+      2.0,
+      2.75,
+      5.75,
+      5.75,
+      6.5,
+      9.25,
+      11.0,
+      13.25,
+      19.25,
+      19.75,
+      19.75,
+      21.25,
+      21.5,
+      26.5,
+      30.0,
+      33.75,
+      35.25,
+      38.5,
+      42.0,
+      42.25,
+      46.0,
+      47.0,
+      50.0,
+      50.5,
+      51.0,
+      52.0,
+      52.75,
+      53.75,
+      54.75,
+      57.0,
+      57.25,
+      58.5,
+      59.25,
+      59.5,
+      62.0,
+      62.5,
+      62.75,
+      63.25,
+      63.25,
+      63.25,
+      63.75,
+      64.0,
+      64.0,
+      64.25,
+      64.5,
+      64.5,
+      65.0,
+      65.0
+    ],
+    "short_factor": [
+      1.0,
+      1.0,
+      1.0,
+      1.0,
+      1.0,
+      1.0,
+      1.01,
+      1.02,
+      1.02,
+      1.04,
+      1.04,
+      1.07,
+      1.07,
+      1.1,
+      1.3000000000000003,
+      1.3000000000000003,
+      1.5000000000000004,
+      1.5700000000000005,
+      1.9000000000000008,
+      2.3100000000000014,
+      2.759999999999992,
+      3.3899999999999784,
+      3.9399999999999666,
+      4.009999999999965,
+      4.289999999999959,
+      4.349999999999958,
+      5.349999999999937,
+      6.659999999999909,
+      7.029999999999901,
+      7.51999999999989,
+      8.00999999999988,
+      8.249999999999876,
+      8.279999999999875,
+      9.629999999999846,
+      9.89999999999984,
+      10.589999999999826,
+      11.049999999999816,
+      11.7899999999998,
+      12.189999999999792,
+      12.889999999999777,
+      13.129999999999772,
+      13.16999999999977,
+      13.20999999999977,
+      13.479999999999764,
+      13.539999999999763,
+      13.779999999999758,
+      13.929999999999755,
+      14.429999999999744,
+      14.759999999999737,
+      15.149999999999729,
+      15.419999999999723,
+      15.53999999999972,
+      15.659999999999718,
+      15.749999999999716,
+      15.759999999999716,
+      15.799999999999715,
+      16.05999999999971,
+      16.079999999999714,
+      16.11999999999972,
+      16.11999999999972,
+      16.18999999999973,
+      16.31999999999975,
+      16.539999999999786,
+      16.799999999999827
+    ],
+    "type": "su"
+  },
+  "rope_theta": 10000.0,
+  "sliding_window": 131072,
+  "tie_word_embeddings": false,
+  "transformers_version": "4.57.1",
+  "use_cache": true,
+  "vocab_size": 32064
+}

configuration_phi3.py

@@ -0,0 +1,213 @@
+# coding=utf-8
+# Copyright 2024 Microsoft and the HuggingFace Inc. team. 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.
+
+""" Phi-3 model configuration"""
+
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+PHI3_PRETRAINED_CONFIG_ARCHIVE_MAP = {
+    "microsoft/Phi-3-mini-4k-instruct": "https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/resolve/main/config.json",
+    "microsoft/Phi-3-mini-128k-instruct": "https://huggingface.co/microsoft/Phi-3-mini-128k-instruct/resolve/main/config.json",
+}
+
+
+class Phi3Config(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`Phi3Model`]. It is used to instantiate a Phi-3
+    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
+    [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct).
+
+    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 32064):
+            Vocabulary size of the Phi-3 model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`Phi3Model`].
+        hidden_size (`int`, *optional*, defaults to 3072):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 8192):
+            Dimension of the MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 32):
+            Number of hidden layers in the Transformer decoder.
+        num_attention_heads (`int`, *optional*, defaults to 32):
+            Number of attention heads for each attention layer in the Transformer decoder.
+        num_key_value_heads (`int`, *optional*):
+            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+            `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+            by meanpooling all the original heads within that group. For more details checkout [this
+            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
+            `num_attention_heads`.
+        resid_pdrop (`float`, *optional*, defaults to 0.0):
+            Dropout probability for mlp outputs.
+        embd_pdrop (`int`, *optional*, defaults to 0.0):
+            The dropout ratio for the embeddings.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio after computing the attention scores.
+        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+            The non-linear activation function (function or string) in the decoder.
+        max_position_embeddings (`int`, *optional*, defaults to 4096):
+            The maximum sequence length that this model might ever be used with.
+        original_max_position_embeddings (`int`, *optional*, defaults to 4096):
+            The maximum sequence length that this model was trained with. This is used to determine the size of the
+            original RoPE embeddings when using long scaling.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        rms_norm_eps (`float`, *optional*, defaults to 1e-05):
+            The epsilon value used for the RMSNorm.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models). Only
+            relevant if `config.is_decoder=True`. Whether to tie weight embeddings or not.
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether to tie weight embeddings
+        rope_theta (`float`, *optional*, defaults to 10000.0):
+            The base period of the RoPE embeddings.
+        rope_scaling (`dict`, *optional*):
+            The scaling strategy for the RoPE embeddings. If `None`, no scaling is applied. If a dictionary, it must
+            contain the following keys: `type`, `short_factor` and `long_factor`. The `type` must be either `su` or `yarn` and
+            the `short_factor` and `long_factor` must be lists of numbers with the same length as the hidden size
+            divided by the number of attention heads divided by 2.
+        bos_token_id (`int`, *optional*, defaults to 1):
+            The id of the "beginning-of-sequence" token.
+        eos_token_id (`int`, *optional*, defaults to 32000):
+            The id of the "end-of-sequence" token.
+        pad_token_id (`int`, *optional*, defaults to 32000):
+            The id of the padding token.
+        sliding_window (`int`, *optional*):
+            Sliding window attention window size. If `None`, no sliding window is applied.
+
+    Example:
+
+    ```python
+    >>> from transformers import Phi3Model, Phi3Config
+
+    >>> # Initializing a Phi-3 style configuration
+    >>> configuration = Phi3Config.from_pretrained("microsoft/Phi-3-mini-4k-instruct")
+
+    >>> # Initializing a model from the configuration
+    >>> model = Phi3Model(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "phi3"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=32064,
+        hidden_size=3072,
+        intermediate_size=8192,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=None,
+        resid_pdrop=0.0,
+        embd_pdrop=0.0,
+        attention_dropout=0.0,
+        hidden_act="silu",
+        max_position_embeddings=4096,
+        original_max_position_embeddings=4096,
+        initializer_range=0.02,
+        rms_norm_eps=1e-5,
+        use_cache=True,
+        tie_word_embeddings=False,
+        rope_theta=10000.0,
+        rope_scaling=None,
+        bos_token_id=1,
+        eos_token_id=32000,
+        pad_token_id=32000,
+        sliding_window=None,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+
+        self.num_key_value_heads = num_key_value_heads
+        self.resid_pdrop = resid_pdrop
+        self.embd_pdrop = embd_pdrop
+        self.attention_dropout = attention_dropout
+        self.hidden_act = hidden_act
+        self.max_position_embeddings = max_position_embeddings
+        self.original_max_position_embeddings = original_max_position_embeddings
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
+        self._rope_scaling_validation()
+        self.sliding_window = sliding_window
+
+        super().__init__(
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            pad_token_id=pad_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
+
+    def _rope_scaling_validation(self):
+        """
+        Validate the `rope_scaling` configuration.
+        """
+        if self.rope_scaling is None:
+            return
+
+        if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 3:
+            raise ValueError(
+                "`rope_scaling` must be a dictionary with three fields, `type`, `short_factor` and `long_factor`, "
+                f"got {self.rope_scaling}"
+            )
+        rope_scaling_type = self.rope_scaling.get("type", None)
+        rope_scaling_short_factor = self.rope_scaling.get("short_factor", None)
+        rope_scaling_long_factor = self.rope_scaling.get("long_factor", None)
+        if rope_scaling_type is None or rope_scaling_type not in ["su", "yarn"]:
+            raise ValueError(f"`rope_scaling`'s type field must be one of ['su', 'yarn'], got {rope_scaling_type}")
+        if not (
+            isinstance(rope_scaling_short_factor, list)
+            and all(isinstance(x, (int, float)) for x in rope_scaling_short_factor)
+        ):
+            raise ValueError(
+                f"`rope_scaling`'s short_factor field must be a list of numbers, got {rope_scaling_short_factor}"
+            )
+        if not len(rope_scaling_short_factor) == self.hidden_size // self.num_attention_heads // 2:
+            raise ValueError(
+                f"`rope_scaling`'s short_factor field must have length {self.hidden_size // self.num_attention_heads // 2}, got {len(rope_scaling_short_factor)}"
+            )
+        if not (
+            isinstance(rope_scaling_long_factor, list)
+            and all(isinstance(x, (int, float)) for x in rope_scaling_long_factor)
+        ):
+            raise ValueError(
+                f"`rope_scaling`'s long_factor field must be a list of numbers, got {rope_scaling_long_factor}"
+            )
+        if not len(rope_scaling_long_factor) == self.hidden_size // self.num_attention_heads // 2:
+            raise ValueError(
+                f"`rope_scaling`'s long_factor field must have length {self.hidden_size // self.num_attention_heads // 2}, got {len(rope_scaling_long_factor)}"
+            )

evo-kl-configuration-3.0.txt

@@ -0,0 +1,160 @@
+model.layers.0.mlp.down_proj: 2
+model.layers.1.mlp.down_proj: 3
+model.layers.2.mlp.down_proj: 3
+model.layers.3.mlp.down_proj: 3
+model.layers.4.mlp.down_proj: 3
+model.layers.5.mlp.down_proj: 3
+model.layers.6.mlp.down_proj: 3
+model.layers.7.mlp.down_proj: 3
+model.layers.8.mlp.down_proj: 3
+model.layers.9.mlp.down_proj: 3
+model.layers.10.mlp.down_proj: 3
+model.layers.11.mlp.down_proj: 3
+model.layers.12.mlp.down_proj: 3
+model.layers.13.mlp.down_proj: 3
+model.layers.14.mlp.down_proj: 3
+model.layers.15.mlp.down_proj: 3
+model.layers.16.mlp.down_proj: 3
+model.layers.17.mlp.down_proj: 3
+model.layers.18.mlp.down_proj: 3
+model.layers.19.mlp.down_proj: 3
+model.layers.20.mlp.down_proj: 3
+model.layers.21.mlp.down_proj: 3
+model.layers.22.mlp.down_proj: 3
+model.layers.23.mlp.down_proj: 3
+model.layers.24.mlp.down_proj: 3
+model.layers.25.mlp.down_proj: 3
+model.layers.26.mlp.down_proj: 4
+model.layers.27.mlp.down_proj: 3
+model.layers.28.mlp.down_proj: 3
+model.layers.29.mlp.down_proj: 3
+model.layers.30.mlp.down_proj: 3
+model.layers.31.mlp.down_proj: 3
+model.layers.32.mlp.down_proj: 3
+model.layers.33.mlp.down_proj: 3
+model.layers.34.mlp.down_proj: 3
+model.layers.35.mlp.down_proj: 3
+model.layers.36.mlp.down_proj: 3
+model.layers.37.mlp.down_proj: 3
+model.layers.38.mlp.down_proj: 3
+model.layers.39.mlp.down_proj: 3
+model.layers.0.mlp.gate_up_proj: 2
+model.layers.1.mlp.gate_up_proj: 3
+model.layers.2.mlp.gate_up_proj: 2
+model.layers.3.mlp.gate_up_proj: 3
+model.layers.4.mlp.gate_up_proj: 3
+model.layers.5.mlp.gate_up_proj: 3
+model.layers.6.mlp.gate_up_proj: 3
+model.layers.7.mlp.gate_up_proj: 3
+model.layers.8.mlp.gate_up_proj: 3
+model.layers.9.mlp.gate_up_proj: 3
+model.layers.10.mlp.gate_up_proj: 4
+model.layers.11.mlp.gate_up_proj: 3
+model.layers.12.mlp.gate_up_proj: 3
+model.layers.13.mlp.gate_up_proj: 3
+model.layers.14.mlp.gate_up_proj: 3
+model.layers.15.mlp.gate_up_proj: 3
+model.layers.16.mlp.gate_up_proj: 3
+model.layers.17.mlp.gate_up_proj: 3
+model.layers.18.mlp.gate_up_proj: 3
+model.layers.19.mlp.gate_up_proj: 3
+model.layers.20.mlp.gate_up_proj: 3
+model.layers.21.mlp.gate_up_proj: 3
+model.layers.22.mlp.gate_up_proj: 3
+model.layers.23.mlp.gate_up_proj: 3
+model.layers.24.mlp.gate_up_proj: 3
+model.layers.25.mlp.gate_up_proj: 3
+model.layers.26.mlp.gate_up_proj: 3
+model.layers.27.mlp.gate_up_proj: 3
+model.layers.28.mlp.gate_up_proj: 3
+model.layers.29.mlp.gate_up_proj: 3
+model.layers.30.mlp.gate_up_proj: 4
+model.layers.31.mlp.gate_up_proj: 3
+model.layers.32.mlp.gate_up_proj: 3
+model.layers.33.mlp.gate_up_proj: 2
+model.layers.34.mlp.gate_up_proj: 3
+model.layers.35.mlp.gate_up_proj: 3
+model.layers.36.mlp.gate_up_proj: 3
+model.layers.37.mlp.gate_up_proj: 3
+model.layers.38.mlp.gate_up_proj: 3
+model.layers.39.mlp.gate_up_proj: 3
+model.layers.0.self_attn.o_proj: 2
+model.layers.1.self_attn.o_proj: 3
+model.layers.2.self_attn.o_proj: 3
+model.layers.3.self_attn.o_proj: 3
+model.layers.4.self_attn.o_proj: 2
+model.layers.5.self_attn.o_proj: 3
+model.layers.6.self_attn.o_proj: 3
+model.layers.7.self_attn.o_proj: 3
+model.layers.8.self_attn.o_proj: 3
+model.layers.9.self_attn.o_proj: 3
+model.layers.10.self_attn.o_proj: 3
+model.layers.11.self_attn.o_proj: 3
+model.layers.12.self_attn.o_proj: 3
+model.layers.13.self_attn.o_proj: 3
+model.layers.14.self_attn.o_proj: 3
+model.layers.15.self_attn.o_proj: 3
+model.layers.16.self_attn.o_proj: 3
+model.layers.17.self_attn.o_proj: 3
+model.layers.18.self_attn.o_proj: 4
+model.layers.19.self_attn.o_proj: 4
+model.layers.20.self_attn.o_proj: 4
+model.layers.21.self_attn.o_proj: 3
+model.layers.22.self_attn.o_proj: 3
+model.layers.23.self_attn.o_proj: 3
+model.layers.24.self_attn.o_proj: 3
+model.layers.25.self_attn.o_proj: 3
+model.layers.26.self_attn.o_proj: 3
+model.layers.27.self_attn.o_proj: 3
+model.layers.28.self_attn.o_proj: 3
+model.layers.29.self_attn.o_proj: 2
+model.layers.30.self_attn.o_proj: 3
+model.layers.31.self_attn.o_proj: 4
+model.layers.32.self_attn.o_proj: 3
+model.layers.33.self_attn.o_proj: 3
+model.layers.34.self_attn.o_proj: 3
+model.layers.35.self_attn.o_proj: 3
+model.layers.36.self_attn.o_proj: 2
+model.layers.37.self_attn.o_proj: 3
+model.layers.38.self_attn.o_proj: 3
+model.layers.39.self_attn.o_proj: 3
+model.layers.0.self_attn.qkv_proj: 3
+model.layers.1.self_attn.qkv_proj: 3
+model.layers.2.self_attn.qkv_proj: 3
+model.layers.3.self_attn.qkv_proj: 3
+model.layers.4.self_attn.qkv_proj: 3
+model.layers.5.self_attn.qkv_proj: 3
+model.layers.6.self_attn.qkv_proj: 3
+model.layers.7.self_attn.qkv_proj: 3
+model.layers.8.self_attn.qkv_proj: 3
+model.layers.9.self_attn.qkv_proj: 3
+model.layers.10.self_attn.qkv_proj: 4
+model.layers.11.self_attn.qkv_proj: 3
+model.layers.12.self_attn.qkv_proj: 3
+model.layers.13.self_attn.qkv_proj: 3
+model.layers.14.self_attn.qkv_proj: 3
+model.layers.15.self_attn.qkv_proj: 3
+model.layers.16.self_attn.qkv_proj: 3
+model.layers.17.self_attn.qkv_proj: 3
+model.layers.18.self_attn.qkv_proj: 4
+model.layers.19.self_attn.qkv_proj: 3
+model.layers.20.self_attn.qkv_proj: 3
+model.layers.21.self_attn.qkv_proj: 3
+model.layers.22.self_attn.qkv_proj: 3
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+  "added_tokens_decoder": {
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+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": false
+    },
+    "32000": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32001": {
+      "content": "<|assistant|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32002": {
+      "content": "<|placeholder1|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32003": {
+      "content": "<|placeholder2|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32004": {
+      "content": "<|placeholder3|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32005": {
+      "content": "<|placeholder4|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32006": {
+      "content": "<|system|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32007": {
+      "content": "<|end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32008": {
+      "content": "<|placeholder5|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32009": {
+      "content": "<|placeholder6|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32010": {
+      "content": "<|user|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|endoftext|>",
+  "extra_special_tokens": {},
+  "legacy": false,
+  "model_max_length": 131072,
+  "pad_token": "<|endoftext|>",
+  "padding_side": "left",
+  "sp_model_kwargs": {},
+  "tokenizer_class": "LlamaTokenizer",
+  "unk_token": "<unk>",
+  "use_default_system_prompt": false
+}