diff --git a/.gitattributes b/.gitattributes
index a6344aac8c09253b3b630fb776ae94478aa0275b..52373fe24473b1aa44333d318f578ae6bf04b49b 100644
--- a/.gitattributes
+++ b/.gitattributes
@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text
diff --git a/chat_template.jinja b/chat_template.jinja
new file mode 100644
index 0000000000000000000000000000000000000000..13268c1a841b2dbe113bdc6dc2c11c3200d4067d
--- /dev/null
+++ b/chat_template.jinja
@@ -0,0 +1,156 @@
+{#- ======== Template Parameters ========  #}
+{%- set add_generation_prompt = add_generation_prompt if add_generation_prompt is defined else true %}
+{%- set default_system_prompt = default_system_prompt if default_system_prompt is defined else true %}
+{%- set reasoning_effort = reasoning_effort if reasoning_effort is defined else "high" %}
+{%- set think_render_option = think_render_option if think_render_option is defined else "lastthink" %}
+
+{#- ======== System Block State ========  #}
+{%- set sys_ns = namespace(is_first_block=true) -%}
+
+{#- ======== Find last user message index ========  #}
+{%- set last_user_idx = namespace(value=-1) -%}
+{%- for message in messages -%}
+    {%- if message.role == 'user' -%}
+        {%- set last_user_idx.value = loop.index0 -%}
+    {%- endif -%}
+{%- endfor -%}
+
+{#- ======== System messages renderers ========  #}
+{%- macro render_system_message(user_system_messages) %}
+    {%- if default_system_prompt %}
+        {%- if not sys_ns.is_first_block %}{{- "\n\n" }}{%- endif %}
+        {%- set sys_ns.is_first_block = false %}
+        {{- "## Provider System Prompt\n\nYou are Solar Open 100B, a large language model trained by Upstage AI, a Korean startup. Your knowledge cutoff is 2025-07. The current date is " + strftime_now("%Y-%m-%d") + "." }}
+    {%- endif -%}
+    {%- if user_system_messages %}
+        {%- if not sys_ns.is_first_block %}{{- "\n\n" }}{%- endif %}
+        {%- set sys_ns.is_first_block = false %}
+        {{- "## System Prompt" }}
+        {%- for system_message in user_system_messages %}
+            {{- "\n\n" }}
+            {{- system_message }}
+        {%- endfor %}
+    {%- endif -%}
+{%- endmacro %}
+
+{%- macro render_tool_instruction(tools) %}
+    {%- if not sys_ns.is_first_block %}{{- "\n\n" }}{%- endif %}
+    {%- set sys_ns.is_first_block = false %}
+    {{- "## Tools\n\n### Tool Call Instruction" }}
+    {{- "\nYou may invoke one or more tools to assist with the user's query. Available tools are provided in JSON Schema format: <|tools:begin|><|tool:begin|><tools-json-object><|tool:end|>...<|tools:end|>\n" }}
+    {{- "\n### Available Tools\n" }}
+    {{- "<|tools:begin|>" }}
+    {%- for tool in tools %}
+        {{- "<|tool:begin|>" }}
+        {{- tool.function | tojson }}
+        {{- "<|tool:end|>" }}
+    {%- endfor %}
+    {{- "<|tools:end|>\n" }}
+    {{- "\n### Tool Call Format\n" }}
+    {{- "For each tool call, return a JSON object with the following structure, enclosed within <|tool_call:begin|> and <|tool_call:end|> tags: \n<|tool_call:begin|><tool-call-id><|tool_call:name|><tool-name><|tool_call:args|><args-json-object><|tool_call:end|>\n" }}
+    {{- "- The <tool-call-id> must be a randomly generated string consisting of 10 lowercase letters (a-z) and/or digits (0-9) (e.g., a1b2c3d4e5)\n" }}
+    {{- "\n### Tool Response Format\n" }}
+    {{- "Each tool is responded by `tool` with the following structure:\n<|tool_response:id|><tool-call-id><|tool_response:name|><tool-name><|tool_response:result|><results><|tool_response:end|>\n" }}
+    {{- "- Ensure the <tool-call-id> matches the corresponding tool call" -}}
+{%- endmacro %}
+
+{%- macro render_json_response_format_instruction(response_format) %}
+    {%- if not sys_ns.is_first_block %}{{- "\n\n" }}{%- endif %}
+    {%- set sys_ns.is_first_block = false %}
+    {{- "## Output Format Constraint" }}
+    {{- "\n\nYour final response should follow the JSON schema: \n[Start of schema]" }}
+    {{- response_format }}
+    {{- "\n[End of schema]\nPlease ensure your answers adhere to this format and do not contain any unnecessary text." }}
+{%- endmacro %}
+
+{%- macro get_tool_name(messages, tool_call_id) %}
+    {%- for msg in messages -%}
+        {%- if msg.role == 'assistant' and msg.tool_calls -%}
+            {%- for tool_call in msg.tool_calls -%}
+                {%- if tool_call.id == tool_call_id -%}
+                    {{- tool_call.function.name }}
+                {%- endif -%}
+            {%- endfor -%}
+        {%- endif -%}
+    {%- endfor -%}
+{%- endmacro %}
+
+{%- macro render_tool_arguments(tool_arguments) %}
+    {%- if tool_arguments is mapping -%}
+        {{- tool_arguments | tojson }}
+    {%- else -%}
+        {{- tool_arguments }}
+    {%- endif -%}
+{%- endmacro %}
+
+{#- ======== Render system message ========  #}
+{%- set ns = namespace(system_messages=[]) -%}
+{%- for message in messages -%}
+    {%- if message.role == 'system' -%}
+        {%- set ns.system_messages = ns.system_messages + [message.content] -%}
+    {%- endif -%}
+{%- endfor -%}
+
+{%- if ns.system_messages or default_system_prompt or tools or response_format -%}
+    {{- "<|begin|>system<|content|>" }}
+        {{- render_system_message(ns.system_messages) }}
+        {%- if tools -%}
+            {{- render_tool_instruction(tools) }}
+        {%- endif %}
+        {%- if response_format -%}
+            {{- render_json_response_format_instruction(response_format) }}
+        {%- endif %}
+    {{- "<|end|>" }}
+{%- endif -%}
+
+{#- ======== Render main messages ========  #}
+{%- for message in messages -%}
+    {%- if message.role == 'user' -%}
+         {{- "<|begin|>user<|content|>" + message.content + "<|end|>" }}
+    {%- elif message.role == 'tool' -%}
+        {%- set prev_is_tool = loop.index0 > 0 and messages[loop.index0 - 1].role == 'tool' -%}
+        {%- set next_is_tool = loop.index0 < (messages | length - 1) and messages[loop.index0 + 1].role == 'tool' -%}
+        {%- if not prev_is_tool -%}
+            {{- "<|begin|>tool<|tool_response|>" }}
+        {%- endif -%}
+        {{- "<|tool_response:begin|>" + message.tool_call_id + "<|tool_response:name|>" }}
+        {{- get_tool_name(messages, message.tool_call_id) }}
+        {{- "<|tool_response:result|>" }}
+        {{- message.content }}
+        {{- "<|tool_response:end|>" }}
+        {%- if not next_is_tool -%}
+            {{- "<|end|>" }}
+        {%- endif -%}
+    {%- elif message.role == 'assistant' -%}
+        {#- ======== Assistant Thinking ========  #}
+        {%- if think_render_option == "all" -%}
+            {%- if message.reasoning -%}
+                {{- "<|begin|>assistant<|think|>" + message.reasoning + "<|end|>" }}
+            {%- endif -%}
+        {%- elif think_render_option == "lastthink" -%}
+            {%- if message.reasoning and loop.index0 > last_user_idx.value -%}
+                {{- "<|begin|>assistant<|think|>" + message.reasoning + "<|end|>" }}
+            {%- endif -%}
+        {%- endif -%}
+
+        {#- ======== Assistant Messages ========  #}
+        {%- if message.tool_calls -%}
+            {{- "<|begin|>assistant<|tool_calls|>" }}
+            {%- for tool_call in message.tool_calls -%}
+                {{- "<|tool_call:begin|>" + tool_call.id +"<|tool_call:name|>" + tool_call.function.name + "<|tool_call:args|>" }}
+                {{- render_tool_arguments(tool_call.function.arguments) }}
+                {{- "<|tool_call:end|>" }}
+            {%- endfor -%}
+            {{- "<|calls|>" }}
+        {%- else -%}
+            {{- "<|begin|>assistant<|content|>" + message.content + "<|end|>" }}
+        {%- endif -%}
+    {%- endif -%}
+{%- endfor -%}
+
+{%- if add_generation_prompt -%}
+    {%- if reasoning_effort in ["low", "minimal"] -%}
+        {{- "<|begin|>assistant<|think|><|end|>" }}
+    {%- endif -%}
+    {{- "<|begin|>assistant" }}
+{%- endif -%}
diff --git a/config.json b/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..ede0d23ffeb9a7d9a04a3cf2e4f8933ce6fddbe8
--- /dev/null
+++ b/config.json
@@ -0,0 +1,49 @@
+{
+  "architectures": [
+    "SolarOpenForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "auto_map": {
+    "AutoConfig": "configuration_solar_open.SolarOpenConfig",
+    "AutoModel": "modeling_solar_open.SolarOpenModel",
+    "AutoModelForCausalLM": "modeling_solar_open.SolarOpenForCausalLM"
+  },
+  "bos_token_id": 1,
+  "dtype": "bfloat16",
+  "eos_token_id": 2,
+  "first_k_dense_replace": 0,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 10240,
+  "max_position_embeddings": 131072,
+  "model_type": "solar_open",
+  "moe_intermediate_size": 1280,
+  "n_group": 1,
+  "n_routed_experts": 128,
+  "n_shared_experts": 1,
+  "norm_topk_prob": true,
+  "num_attention_heads": 64,
+  "num_experts_per_tok": 8,
+  "num_hidden_layers": 48,
+  "num_key_value_heads": 8,
+  "pad_token_id": 2,
+  "partial_rotary_factor": 1.0,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": {
+    "factor": 2.0,
+    "original_max_position_embeddings": 65536,
+    "rope_type": "yarn",
+    "type": "yarn"
+  },
+  "rope_theta": 1000000,
+  "routed_scaling_factor": 1.0,
+  "tie_word_embeddings": false,
+  "topk_group": 1,
+  "transformers_version": "4.57.6",
+  "use_cache": true,
+  "use_qk_norm": false,
+  "vocab_size": 196608
+}
diff --git a/configuration_solar_open.py b/configuration_solar_open.py
new file mode 100644
index 0000000000000000000000000000000000000000..cf2af0911d9a88377d1a0bc7612fb17fb135e83b
--- /dev/null
+++ b/configuration_solar_open.py
@@ -0,0 +1,242 @@
+# coding=utf-8
+# Copyright 2025 Upstage AI.
+# Copyright 2025 The ZhipuAI Inc. team and HuggingFace Inc. team.
+#
+# 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.
+#
+# This file has been modified by Upstage AI including
+# -  Hyperparameter Adjustments: Modified the model architecture by increasing vocab_size and num_hidden_layers, while decreasing num_attention_heads, intermediate_size, and moe_intermediate_size.
+# RoPE Configuration: Replaced the generic rope_parameters argument with explicit rope_theta and rope_scaling parameters to define Rotary Positional Embeddings settings.
+#
+# Based on code from: https://github.com/huggingface/transformers/blob/main/src/transformers/models/glm4_moe/configuration_glm4_moe.py
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.modeling_rope_utils import rope_config_validation
+
+
+class SolarOpenConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`SolarOpenModel`]. It is used to instantiate a
+    SolarOpen model according to the specified arguments, defining the model 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 196608):
+            Vocabulary size of the SolarOpen model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`SolarOpenModel`]
+        hidden_size (`int`, *optional*, defaults to 4096):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 10240):
+            Dimension of the MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 48):
+            Number of hidden layers in the Transformer encoder.
+        num_attention_heads (`int`, *optional*, defaults to 64):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        partial_rotary_factor (`float`, *optional*, defaults to 1.0):
+            The factor of the partial rotary position.
+        num_key_value_heads (`int`, *optional*, defaults to 8):
+            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, check out [this
+            paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to `32`.
+
+        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 131072):
+            The maximum sequence length that this model might ever be used with.
+        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 used by the rms normalization layers.
+        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`.
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether the model's input and output word embeddings should be tied.
+        rope_theta (`float`, *optional*, defaults to 1000000.0):
+            The base period of the RoPE embeddings.
+        rope_scaling (`Dict`, *optional*):
+            Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type
+            and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
+            accordingly.
+            Expected contents:
+                `rope_type` (`str`):
+                    The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
+                    'llama3'], with 'default' being the original RoPE implementation.
+                `factor` (`float`, *optional*):
+                    Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In
+                    most scaling types, a `factor` of x will enable the model to handle sequences of length x *
+                    original maximum pre-trained length.
+                `original_max_position_embeddings` (`int`, *optional*):
+                    Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during
+                    pretraining.
+                `attention_factor` (`float`, *optional*):
+                    Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
+                    computation. If unspecified, it defaults to value recommended by the implementation, using the
+                    `factor` field to infer the suggested value.
+                `beta_fast` (`float`, *optional*):
+                    Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
+                    ramp function. If unspecified, it defaults to 32.
+                `beta_slow` (`float`, *optional*):
+                    Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
+                    ramp function. If unspecified, it defaults to 1.
+                `short_factor` (`list[float]`, *optional*):
+                    Only used with 'longrope'. The scaling factor to be applied to short contexts (<
+                    `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
+                    size divided by the number of attention heads divided by 2
+                `long_factor` (`list[float]`, *optional*):
+                    Only used with 'longrope'. The scaling factor to be applied to long contexts (<
+                    `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
+                    size divided by the number of attention heads divided by 2
+                `low_freq_factor` (`float`, *optional*):
+                    Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
+                `high_freq_factor` (`float`, *optional*):
+                    Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
+        attention_bias (`bool`, defaults to `False`, *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.
+        moe_intermediate_size (`int`, *optional*, defaults to 1280):
+            Intermediate size of the routed expert.
+        num_experts_per_tok (`int`, *optional*, defaults to 8):
+            number of experts per token.
+        n_shared_experts (`int`, *optional*, defaults to 1):
+            Number of shared experts.
+        n_routed_experts (`int`, *optional*, defaults to 128):
+            Number of routed experts.
+        routed_scaling_factor (`float`, *optional*, defaults to 1.0):
+            Scaling factor or routed experts.
+        n_group (`int`, *optional*, defaults to 1):
+            Number of groups for routed experts.
+        topk_group (`int`, *optional*, defaults to 1):
+            Number of selected groups for each token(for each token, ensuring the selected experts is only within `topk_group` groups).
+        first_k_dense_replace (`int`, *optional*, defaults to 0):
+            Number of dense layers in shallow layers(embed->dense->dense->...->dense->moe->moe...->lm_head).
+                                                            \--k dense layers--/
+        norm_topk_prob (`bool`, *optional*, defaults to `True`):
+            Whether to normalize the topk probabilities.
+        use_qk_norm (`bool`, *optional*, defaults to `False`):
+            Whether to use query-key normalization in the attention
+    ```python
+    >>> from transformers import SolarOpenModel, SolarOpenConfig
+
+    >>> # Initializing a SolarOpen style configuration
+    >>> configuration = SolarOpenConfig()
+
+    >>> # Initializing a model from the SolarOpen style configuration
+    >>> model = SolarOpenModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "solar_open"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    # Default tensor parallel plan for base model `SolarOpen`
+    base_model_tp_plan = {
+        "layers.*.self_attn.q_proj": "colwise",
+        "layers.*.self_attn.k_proj": "colwise",
+        "layers.*.self_attn.v_proj": "colwise",
+        "layers.*.self_attn.o_proj": "rowwise",
+        "layers.*.mlp.experts.*.gate_proj": "colwise",
+        "layers.*.mlp.experts.*.up_proj": "colwise",
+        "layers.*.mlp.experts.*.down_proj": "rowwise",
+        "layers.*.mlp.gate_proj": "colwise",
+        "layers.*.mlp.up_proj": "colwise",
+        "layers.*.mlp.down_proj": "rowwise",
+    }
+    base_model_pp_plan = {
+        "embed_tokens": (["input_ids"], ["inputs_embeds"]),
+        "layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
+        "norm": (["hidden_states"], ["hidden_states"]),
+    }
+
+    def __init__(
+        self,
+        vocab_size=196608,
+        hidden_size=4096,
+        intermediate_size=10240,
+        num_hidden_layers=48,
+        num_attention_heads=64,
+        partial_rotary_factor=1.0,
+        num_key_value_heads=8,
+        hidden_act="silu",
+        max_position_embeddings=131072,
+        initializer_range=0.02,
+        rms_norm_eps=1e-5,
+        use_cache=True,
+        tie_word_embeddings=False,
+        rope_theta=1000000.0,
+        rope_scaling=None,
+        attention_bias=False,
+        attention_dropout=0.0,
+        moe_intermediate_size=1280,
+        num_experts_per_tok=8,
+        n_shared_experts=1,
+        n_routed_experts=128,
+        routed_scaling_factor=1.0,
+        n_group=1,
+        topk_group=1,
+        first_k_dense_replace=0,
+        norm_topk_prob=True,
+        use_qk_norm=False,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.partial_rotary_factor = partial_rotary_factor
+
+        self.num_key_value_heads = num_key_value_heads
+        self.hidden_act = hidden_act
+        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.attention_bias = attention_bias
+        self.attention_dropout = attention_dropout
+        # Validate the correctness of rotary position embeddings parameters
+        # BC: if there is a 'type' field, move it to 'rope_type'.
+        if self.rope_scaling is not None and "type" in self.rope_scaling:
+            self.rope_scaling["rope_type"] = self.rope_scaling["type"]
+        rope_config_validation(self)
+
+        # MoE arguments
+        self.moe_intermediate_size = moe_intermediate_size
+        self.num_experts_per_tok = num_experts_per_tok
+        self.n_group = n_group
+        self.topk_group = topk_group
+        self.n_shared_experts = n_shared_experts
+        self.n_routed_experts = n_routed_experts
+        self.routed_scaling_factor = routed_scaling_factor
+        self.first_k_dense_replace = first_k_dense_replace
+        self.norm_topk_prob = norm_topk_prob
+        self.use_qk_norm = use_qk_norm
+
+        super().__init__(
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
+
+
+__all__ = ["SolarOpenConfig"]
\ No newline at end of file
diff --git a/generation_config.json b/generation_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..47a0bc010c2ecc42d24b625afd643b6407bc988a
--- /dev/null
+++ b/generation_config.json
@@ -0,0 +1,14 @@
+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "do_sample": true,
+  "eos_token_id": [
+    2,
+    24,
+    25
+  ],
+  "pad_token_id": 2,
+  "temperature": 0.8,
+  "top_p": 0.95,
+  "transformers_version": "4.57.6"
+}
diff --git a/model-00001-of-00042.safetensors b/model-00001-of-00042.safetensors
new file mode 100644
index 0000000000000000000000000000000000000000..fa176586f25a9b60f563bf50b40a2e81986d3790
--- /dev/null
+++ b/model-00001-of-00042.safetensors
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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diff --git a/modeling_solar_open.py b/modeling_solar_open.py
new file mode 100644
index 0000000000000000000000000000000000000000..c214d828216e22400f2a821a2b83af3f161a42a4
--- /dev/null
+++ b/modeling_solar_open.py
@@ -0,0 +1,605 @@
+# coding=utf-8
+# Copyright 2025 Upstage AI.
+# Copyright 2025 The GLM4 & ZhipuAI team and HuggingFace Inc. team.
+#
+# 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.
+#
+# This file has been modified by Upstage AI including:
+# - Hybrid MoE Architecture: Replaced the standard dense structure with a depth-dependent Hybrid MoE, adding `SolarOpenMoE` and `SolarOpenTopkRouter` classes.
+# - RoPE Strategy: Changed the rotary position embedding strategy from GLM4's interleaved rotation to Llama-style block rotation (via modified `rotate_half`).
+# - Normalization Logic: Simplified the layer normalization structure by removing GLM4's extra post-operation norms and adding optional Query-Key Normalization (`use_qk_norm`).
+#
+# Based on code from: https://github.com/huggingface/transformers/blob/main/src/transformers/models/glm4/modeling_glm4.py
+
+from typing import Callable, Optional, Union
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+
+from transformers.activations import ACT2FN
+from transformers.cache_utils import Cache, DynamicCache
+from transformers.generation import GenerationMixin
+from transformers.integrations import use_kernel_forward_from_hub
+from transformers.masking_utils import create_causal_mask
+from transformers.modeling_flash_attention_utils import FlashAttentionKwargs
+from transformers.modeling_layers import GradientCheckpointingLayer
+from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
+from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
+from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
+from transformers.processing_utils import Unpack
+from transformers.utils import TransformersKwargs, auto_docstring, can_return_tuple
+from transformers.utils.deprecation import deprecate_kwarg
+from transformers.utils.generic import check_model_inputs
+from .configuration_solar_open import SolarOpenConfig
+
+
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+    """
+    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+    """
+    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+    if n_rep == 1:
+        return hidden_states
+    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
+    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+def eager_attention_forward(
+    module: nn.Module,
+    query: torch.Tensor,
+    key: torch.Tensor,
+    value: torch.Tensor,
+    attention_mask: Optional[torch.Tensor],
+    scaling: float,
+    dropout: float = 0.0,
+    **kwargs: Unpack[TransformersKwargs],
+):
+    key_states = repeat_kv(key, module.num_key_value_groups)
+    value_states = repeat_kv(value, module.num_key_value_groups)
+
+    attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
+    if attention_mask is not None:
+        causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
+        attn_weights = attn_weights + causal_mask
+
+    attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
+    attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
+    attn_output = torch.matmul(attn_weights, value_states)
+    attn_output = attn_output.transpose(1, 2).contiguous()
+
+    return attn_output, attn_weights
+
+
+def rotate_half(x):
+    """Rotates half the hidden dims of the input."""
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2 :]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
+    """Applies Rotary Position Embedding to the query and key tensors.
+
+    Args:
+        q (`torch.Tensor`): The query tensor.
+        k (`torch.Tensor`): The key tensor.
+        cos (`torch.Tensor`): The cosine part of the rotary embedding.
+        sin (`torch.Tensor`): The sine part of the rotary embedding.
+        position_ids (`torch.Tensor`, *optional*):
+            Deprecated and unused.
+        unsqueeze_dim (`int`, *optional*, defaults to 1):
+            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
+            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
+            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
+            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
+            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
+            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
+    Returns:
+        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
+    """
+    cos = cos.unsqueeze(unsqueeze_dim)
+    sin = sin.unsqueeze(unsqueeze_dim)
+
+    # Keep half or full tensor for later concatenation
+    rotary_dim = cos.shape[-1]
+    q_rot, q_pass = q[..., :rotary_dim], q[..., rotary_dim:]
+    k_rot, k_pass = k[..., :rotary_dim], k[..., rotary_dim:]
+
+    # Apply rotary embeddings on the first half or full tensor
+    q_embed = (q_rot * cos) + (rotate_half(q_rot) * sin)
+    k_embed = (k_rot * cos) + (rotate_half(k_rot) * sin)
+
+    # Concatenate back to full shape
+    q_embed = torch.cat([q_embed, q_pass], dim=-1)
+    k_embed = torch.cat([k_embed, k_pass], dim=-1)
+    return q_embed, k_embed
+
+
+class SolarOpenAttention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    def __init__(self, config: SolarOpenConfig, layer_idx: Optional[int] = None):
+        super().__init__()
+        self.config = config
+        self.layer_idx = layer_idx
+        self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
+        self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
+        self.scaling = self.head_dim**-0.5
+        self.rope_scaling = config.rope_scaling
+        self.attention_dropout = config.attention_dropout
+        self.is_causal = True
+
+        self.q_proj = nn.Linear(
+            config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.attention_bias
+        )
+        self.k_proj = nn.Linear(
+            config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
+        )
+        self.v_proj = nn.Linear(
+            config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
+        )
+        self.o_proj = nn.Linear(config.num_attention_heads * self.head_dim, config.hidden_size, bias=False)
+        self.use_qk_norm = config.use_qk_norm
+        if self.use_qk_norm:
+            self.q_norm = SolarOpenRMSNorm(self.head_dim, eps=config.rms_norm_eps)
+            self.k_norm = SolarOpenRMSNorm(self.head_dim, eps=config.rms_norm_eps)
+
+    @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        position_embeddings: tuple[torch.Tensor, torch.Tensor],
+        attention_mask: Optional[torch.Tensor],
+        past_key_values: Optional[Cache] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        **kwargs: Unpack[FlashAttentionKwargs],
+    ) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
+        input_shape = hidden_states.shape[:-1]
+        hidden_shape = (*input_shape, -1, self.head_dim)
+
+        query_states = self.q_proj(hidden_states).view(hidden_shape)
+        key_states = self.k_proj(hidden_states).view(hidden_shape)
+        value_states = self.v_proj(hidden_states).view(hidden_shape)
+
+        if self.use_qk_norm:  # main diff from Llama
+            query_states = self.q_norm(query_states)
+            key_states = self.k_norm(key_states)
+
+        query_states = query_states.transpose(1, 2)
+        key_states = key_states.transpose(1, 2)
+        value_states = value_states.transpose(1, 2)
+
+        cos, sin = position_embeddings
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+
+        if past_key_values is not None:
+            # sin and cos are specific to RoPE models; position_ids needed for the static cache
+            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
+            key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+        attention_interface: Callable = eager_attention_forward
+        if self.config._attn_implementation != "eager":
+            attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
+
+        attn_output, attn_weights = attention_interface(
+            self,
+            query_states,
+            key_states,
+            value_states,
+            attention_mask,
+            dropout=0.0 if not self.training else self.attention_dropout,
+            scaling=self.scaling,
+            **kwargs,
+        )
+
+        attn_output = attn_output.reshape(*input_shape, -1).contiguous()
+        attn_output = self.o_proj(attn_output)
+        return attn_output, attn_weights
+
+
+class SolarOpenMLP(nn.Module):
+    def __init__(self, config, hidden_size=None, intermediate_size=None):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size if hidden_size is None else hidden_size
+        self.intermediate_size = config.intermediate_size if intermediate_size is None else intermediate_size
+
+        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
+        self.act_fn = ACT2FN[config.hidden_act]
+
+    def forward(self, x):
+        down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+        return down_proj
+
+
+class SolarOpenTopkRouter(nn.Module):
+    def __init__(self, config: SolarOpenConfig):
+        super().__init__()
+        self.config = config
+        self.top_k = config.num_experts_per_tok
+        self.n_routed_experts = config.n_routed_experts
+        self.routed_scaling_factor = config.routed_scaling_factor
+        self.n_group = config.n_group
+        self.topk_group = config.topk_group
+        self.norm_topk_prob = config.norm_topk_prob
+
+        self.weight = nn.Parameter(torch.empty((self.n_routed_experts, config.hidden_size)))
+        self.e_score_correction_bias = nn.Parameter(
+            torch.zeros((self.n_routed_experts), dtype=torch.float32))
+
+    @torch.no_grad()
+    def get_topk_indices(self, scores):
+        scores_for_choice = scores.view(-1, self.n_routed_experts) + self.e_score_correction_bias.unsqueeze(0)
+        group_scores = (
+            scores_for_choice.view(-1, self.n_group, self.n_routed_experts // self.n_group)
+            .topk(2, dim=-1)[0]
+            .sum(dim=-1)
+        )
+        group_idx = torch.topk(group_scores, k=self.topk_group, dim=-1, sorted=False)[1]
+        group_mask = torch.zeros_like(group_scores)
+        group_mask.scatter_(1, group_idx, 1)
+        score_mask = (
+            group_mask.unsqueeze(-1)
+            .expand(-1, self.n_group, self.n_routed_experts // self.n_group)
+            .reshape(-1, self.n_routed_experts)
+        )
+        scores_for_choice = scores_for_choice.masked_fill(~score_mask.bool(), 0.0)
+        topk_indices = torch.topk(scores_for_choice, k=self.top_k, dim=-1, sorted=False)[1]
+        return topk_indices
+
+    def forward(self, hidden_states):
+        hidden_states = hidden_states.view(-1, self.config.hidden_size)
+        router_logits = F.linear(hidden_states.type(torch.float32), self.weight.type(torch.float32))
+        scores = router_logits.sigmoid()
+        topk_indices = self.get_topk_indices(scores)
+        topk_weights = scores.gather(1, topk_indices)
+        if self.norm_topk_prob:
+            denominator = topk_weights.sum(dim=-1, keepdim=True) + 1e-20
+            topk_weights /= denominator
+        topk_weights = topk_weights * self.routed_scaling_factor
+        return topk_indices, topk_weights
+
+
+@use_kernel_forward_from_hub("RMSNorm")
+class SolarOpenRMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-6):
+        """
+        SolarOpenRMSNorm is equivalent to T5LayerNorm
+        """
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states):
+        input_dtype = hidden_states.dtype
+        hidden_states = hidden_states.to(torch.float32)
+        variance = hidden_states.pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+        return self.weight * hidden_states.to(input_dtype)
+
+    def extra_repr(self):
+        return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
+
+
+class SolarOpenMoE(nn.Module):
+    """
+    A mixed expert module containing shared experts.
+    """
+
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.experts = nn.ModuleList(
+            [
+                SolarOpenMLP(config, intermediate_size=config.moe_intermediate_size)
+                for _ in range(config.n_routed_experts)
+            ]
+        )
+        self.gate = SolarOpenTopkRouter(config)
+        self.shared_experts = SolarOpenMLP(
+            config=config, intermediate_size=config.moe_intermediate_size * config.n_shared_experts
+        )
+
+    @torch.compiler.disable()
+    def moe(self, hidden_states: torch.Tensor, topk_indices: torch.Tensor, topk_weights: torch.Tensor):
+        r"""
+        MoE forward pass that only executes selected experts.
+        Uses @torch.compiler.disable() to allow dynamic shape operations.
+        Requires --enforce-eager flag when serving with vLLM.
+        """
+        final_hidden_states = torch.zeros_like(hidden_states)
+
+        for expert_idx in range(len(self.experts)):
+            expert = self.experts[expert_idx]
+
+            # Find positions where this expert was selected
+            batch_idx, topk_pos = torch.where(topk_indices == expert_idx)
+
+            if batch_idx.numel() == 0:
+                continue
+
+            # Extract only the tokens routed to this expert
+            expert_input = hidden_states[batch_idx]
+            expert_output = expert(expert_input)
+
+            # Apply weights and accumulate results
+            weights = topk_weights[batch_idx, topk_pos].unsqueeze(-1)
+            final_hidden_states.index_add_(0, batch_idx, (expert_output * weights).to(hidden_states.dtype))
+
+        return final_hidden_states
+
+    def forward(self, hidden_states):
+        residuals = hidden_states
+        orig_shape = hidden_states.shape
+        topk_indices, topk_weights = self.gate(hidden_states)
+        hidden_states = hidden_states.view(-1, hidden_states.shape[-1])
+        hidden_states = self.moe(hidden_states, topk_indices, topk_weights).view(*orig_shape)
+        hidden_states = hidden_states + self.shared_experts(residuals)
+        return hidden_states
+
+
+class SolarOpenDecoderLayer(GradientCheckpointingLayer):
+    def __init__(self, config: SolarOpenConfig, layer_idx: int):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+
+        self.self_attn = SolarOpenAttention(config=config, layer_idx=layer_idx)
+
+        if layer_idx >= config.first_k_dense_replace:
+            self.mlp = SolarOpenMoE(config)
+        else:
+            self.mlp = SolarOpenMLP(config)
+
+        self.input_layernorm = SolarOpenRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = SolarOpenRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Cache] = None,
+        use_cache: Optional[bool] = False,
+        cache_position: Optional[torch.LongTensor] = None,
+        position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None,  # necessary, but kept here for BC
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> torch.Tensor:
+        residual = hidden_states
+        hidden_states = self.input_layernorm(hidden_states)
+        # Self Attention
+        hidden_states, _ = self.self_attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            use_cache=use_cache,
+            cache_position=cache_position,
+            position_embeddings=position_embeddings,
+            **kwargs,
+        )
+        hidden_states = residual + hidden_states
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+        return hidden_states
+
+
+@auto_docstring
+class SolarOpenPreTrainedModel(PreTrainedModel):
+    config: SolarOpenConfig
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["SolarOpenDecoderLayer"]
+    _skip_keys_device_placement = ["past_key_values"]
+    _supports_flash_attn = True
+    _supports_sdpa = True
+    _supports_flex_attn = True
+    _can_compile_fullgraph = False
+    _supports_attention_backend = True
+    _can_record_outputs = {
+        "hidden_states": SolarOpenDecoderLayer,
+        "attentions": SolarOpenAttention,
+    }
+
+    def _init_weights(self, module):
+        super()._init_weights(module)
+        if isinstance(module, SolarOpenTopkRouter):
+            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+
+
+class SolarOpenRotaryEmbedding(nn.Module):
+    inv_freq: torch.Tensor  # fix linting for `register_buffer`
+
+    def __init__(self, config: SolarOpenConfig, device=None):
+        super().__init__()
+        # BC: "rope_type" was originally "type"
+        if hasattr(config, "rope_scaling") and isinstance(config.rope_scaling, dict):
+            self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
+        else:
+            self.rope_type = "default"
+        self.max_seq_len_cached = config.max_position_embeddings
+        self.original_max_seq_len = config.max_position_embeddings
+
+        self.config = config
+        self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
+
+        inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+        self.original_inv_freq = self.inv_freq
+
+    @torch.no_grad()
+    @dynamic_rope_update  # power user: used with advanced RoPE types (e.g. dynamic rope)
+    def forward(self, x, position_ids):
+        inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
+        position_ids_expanded = position_ids[:, None, :].float()
+
+        device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
+        with torch.autocast(device_type=device_type, enabled=False):  # Force float32
+            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            emb = torch.cat((freqs, freqs), dim=-1)
+            cos = emb.cos() * self.attention_scaling
+            sin = emb.sin() * self.attention_scaling
+
+        return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
+
+
+@auto_docstring
+class SolarOpenModel(SolarOpenPreTrainedModel):
+    _keys_to_ignore_on_load_unexpected = [r"model\.layers\.92.*", r"model\.layers\.46.*"]
+
+    def __init__(self, config: SolarOpenConfig):
+        super().__init__(config)
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+
+        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
+        self.layers = nn.ModuleList(
+            [SolarOpenDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+        )
+        self.norm = SolarOpenRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.rotary_emb = SolarOpenRotaryEmbedding(config=config)
+        self.gradient_checkpointing = False
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    @check_model_inputs()
+    @auto_docstring
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Cache] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> BaseModelOutputWithPast:
+        if (input_ids is None) ^ (inputs_embeds is not None):
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+        if inputs_embeds is None:
+            inputs_embeds: torch.Tensor = self.embed_tokens(input_ids)
+
+        if use_cache and past_key_values is None:
+            past_key_values = DynamicCache(config=self.config)
+
+        if cache_position is None:
+            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+            cache_position: torch.Tensor = torch.arange(
+                past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
+            )
+
+        if position_ids is None:
+            position_ids = cache_position.unsqueeze(0)
+
+        causal_mask = create_causal_mask(
+            config=self.config,
+            input_embeds=inputs_embeds,
+            attention_mask=attention_mask,
+            cache_position=cache_position,
+            past_key_values=past_key_values,
+            position_ids=position_ids,
+        )
+
+        hidden_states = inputs_embeds
+        position_embeddings = self.rotary_emb(hidden_states, position_ids)
+
+        for decoder_layer in self.layers[: self.config.num_hidden_layers]:
+            hidden_states = decoder_layer(
+                hidden_states,
+                attention_mask=causal_mask,
+                position_ids=position_ids,
+                past_key_values=past_key_values,
+                cache_position=cache_position,
+                position_embeddings=position_embeddings,
+                **kwargs,
+            )
+
+        hidden_states = self.norm(hidden_states)
+        return BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=past_key_values,
+        )
+
+
+@auto_docstring
+class SolarOpenForCausalLM(SolarOpenPreTrainedModel, GenerationMixin):
+    _tied_weights_keys = ["lm_head.weight"]
+    _tp_plan = {"lm_head": "colwise_rep"}
+    _pp_plan = {"lm_head": (["hidden_states"], ["logits"])}
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = SolarOpenModel(config)
+        self.vocab_size = config.vocab_size
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    @can_return_tuple
+    @auto_docstring
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Cache] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        logits_to_keep: Union[int, torch.Tensor] = 0,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> CausalLMOutputWithPast:
+
+        outputs: BaseModelOutputWithPast = self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            cache_position=cache_position,
+            **kwargs,
+        )
+
+        hidden_states = outputs.last_hidden_state
+        # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
+        slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
+        logits = self.lm_head(hidden_states[:, slice_indices, :])
+
+        loss = None
+        if labels is not None:
+            loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs)
+
+        return CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+__all__ = ["SolarOpenPreTrainedModel", "SolarOpenModel", "SolarOpenForCausalLM"]
\ No newline at end of file
diff --git a/special_tokens_map.json b/special_tokens_map.json
new file mode 100644
index 0000000000000000000000000000000000000000..a506572e61096d1d324556cf55f38babc0bf14e2
--- /dev/null
+++ b/special_tokens_map.json
@@ -0,0 +1,4006 @@
+{
+  "additional_special_tokens": [
+    "<unk>",
+    "<|startoftext|>",
+    "<|endoftext|>",
+    "<|fim_prefix|>",
+    "<|fim_middle|>",
+    "<|fim_suffix|>",
+    "<|special_6|>",
+    "<|special_7|>",
+    "<|special_8|>",
+    "<|special_9|>",
+    "<|special_10|>",
+    "<|special_11|>",
+    "<|special_12|>",
+    "<|special_13|>",
+    "<|special_14|>",
+    "<|special_15|>",
+    "<|special_16|>",
+    "<|special_17|>",
+    "<|special_18|>",
+    "<|special_19|>",
+    "<|flush|>",
+    "<|calls|>",
+    "<|tools:begin|>",
+    "<|tools:end|>",
+    "<|tool:begin|>",
+    "<|tool:end|>",
+    "<|tool_response|>",
+    "<|tool_response:begin|>",
+    "<|tool_response:end|>",
+    "<|tool_response:name|>",
+    "<|tool_response:result|>",
+    "<|special_40|>",
+    "<|special_41|>",
+    "<|special_42|>",
+    "<|special_43|>",
+    "<|special_44|>",
+    "<|special_45|>",
+    "<|special_46|>",
+    "<|special_47|>",
+    "<|special_48|>",
+    "<|special_49|>",
+    "<|special_50|>",
+    "<|special_51|>",
+    "<|special_52|>",
+    "<|special_53|>",
+    "<|special_54|>",
+    "<|special_55|>",
+    "<|special_56|>",
+    "<|special_57|>",
+    "<|special_58|>",
+    "<|special_59|>",
+    "<|special_60|>",
+    "<|special_61|>",
+    "<|special_62|>",
+    "<|special_63|>",
+    "<|special_64|>",
+    "<|special_65|>",
+    "<|special_66|>",
+    "<|special_67|>",
+    "<|special_68|>",
+    "<|special_69|>",
+    "<|special_70|>",
+    "<|special_71|>",
+    "<|special_72|>",
+    "<|special_73|>",
+    "<|special_74|>",
+    "<|special_75|>",
+    "<|special_76|>",
+    "<|special_77|>",
+    "<|special_78|>",
+    "<|special_79|>",
+    "<|special_80|>",
+    "<|special_81|>",
+    "<|special_82|>",
+    "<|special_83|>",
+    "<|special_84|>",
+    "<|special_85|>",
+    "<|special_86|>",
+    "<|special_87|>",
+    "<|special_88|>",
+    "<|special_89|>",
+    "<|special_90|>",
+    "<|special_91|>",
+    "<|special_92|>",
+    "<|special_93|>",
+    "<|special_94|>",
+    "<|special_95|>",
+    "<|special_96|>",
+    "<|special_97|>",
+    "<|special_98|>",
+    "<|special_99|>",
+    "<|special_100|>",
+    "<|special_101|>",
+    "<|special_102|>",
+    "<|special_103|>",
+    "<|special_104|>",
+    "<|special_105|>",
+    "<|special_106|>",
+    "<|special_107|>",
+    "<|special_108|>",
+    "<|special_109|>",
+    "<|special_110|>",
+    "<|special_111|>",
+    "<|special_112|>",
+    "<|special_113|>",
+    "<|special_114|>",
+    "<|special_115|>",
+    "<|special_116|>",
+    "<|special_117|>",
+    "<|special_118|>",
+    "<|special_119|>",
+    "<|special_120|>",
+    "<|special_121|>",
+    "<|special_122|>",
+    "<|special_123|>",
+    "<|special_124|>",
+    "<|special_125|>",
+    "<|special_126|>",
+    "<|special_127|>",
+    "<|special_128|>",
+    "<|special_129|>",
+    "<|special_130|>",
+    "<|special_131|>",
+    "<|special_132|>",
+    "<|special_133|>",
+    "<|special_134|>",
+    "<|special_135|>",
+    "<|special_136|>",
+    "<|special_137|>",
+    "<|special_138|>",
+    "<|special_139|>",
+    "<|special_140|>",
+    "<|special_141|>",
+    "<|special_142|>",
+    "<|special_143|>",
+    "<|special_144|>",
+    "<|special_145|>",
+    "<|special_146|>",
+    "<|special_147|>",
+    "<|special_148|>",
+    "<|special_149|>",
+    "<|special_150|>",
+    "<|special_151|>",
+    "<|special_152|>",
+    "<|special_153|>",
+    "<|special_154|>",
+    "<|special_155|>",
+    "<|special_156|>",
+    "<|special_157|>",
+    "<|special_158|>",
+    "<|special_159|>",
+    "<|special_160|>",
+    "<|special_161|>",
+    "<|special_162|>",
+    "<|special_163|>",
+    "<|special_164|>",
+    "<|special_165|>",
+    "<|special_166|>",
+    "<|special_167|>",
+    "<|special_168|>",
+    "<|special_169|>",
+    "<|special_170|>",
+    "<|special_171|>",
+    "<|special_172|>",
+    "<|special_173|>",
+    "<|special_174|>",
+    "<|special_175|>",
+    "<|special_176|>",
+    "<|special_177|>",
+    "<|special_178|>",
+    "<|special_179|>",
+    "<|special_180|>",
+    "<|special_181|>",
+    "<|special_182|>",
+    "<|special_183|>",
+    "<|special_184|>",
+    "<|special_185|>",
+    "<|special_186|>",
+    "<|special_187|>",
+    "<|special_188|>",
+    "<|special_189|>",
+    "<|special_190|>",
+    "<|special_191|>",
+    "<|special_192|>",
+    "<|special_193|>",
+    "<|special_194|>",
+    "<|special_195|>",
+    "<|special_196|>",
+    "<|special_197|>",
+    "<|special_198|>",
+    "<|special_199|>",
+    "<|special_200|>",
+    "<|special_201|>",
+    "<|special_202|>",
+    "<|special_203|>",
+    "<|special_204|>",
+    "<|special_205|>",
+    "<|special_206|>",
+    "<|special_207|>",
+    "<|special_208|>",
+    "<|special_209|>",
+    "<|special_210|>",
+    "<|special_211|>",
+    "<|special_212|>",
+    "<|special_213|>",
+    "<|special_214|>",
+    "<|special_215|>",
+    "<|special_216|>",
+    "<|special_217|>",
+    "<|special_218|>",
+    "<|special_219|>",
+    "<|special_220|>",
+    "<|special_221|>",
+    "<|special_222|>",
+    "<|special_223|>",
+    "<|special_224|>",
+    "<|special_225|>",
+    "<|special_226|>",
+    "<|special_227|>",
+    "<|special_228|>",
+    "<|special_229|>",
+    "<|special_230|>",
+    "<|special_231|>",
+    "<|special_232|>",
+    "<|special_233|>",
+    "<|special_234|>",
+    "<|special_235|>",
+    "<|special_236|>",
+    "<|special_237|>",
+    "<|special_238|>",
+    "<|special_239|>",
+    "<|special_240|>",
+    "<|special_241|>",
+    "<|special_242|>",
+    "<|special_243|>",
+    "<|special_244|>",
+    "<|special_245|>",
+    "<|special_246|>",
+    "<|special_247|>",
+    "<|special_248|>",
+    "<|special_249|>",
+    "<|special_250|>",
+    "<|special_251|>",
+    "<|special_252|>",
+    "<|special_253|>",
+    "<|special_254|>",
+    "<|special_255|>",
+    "<|special_256|>",
+    "<|special_257|>",
+    "<|special_258|>",
+    "<|special_259|>",
+    "<|special_260|>",
+    "<|special_261|>",
+    "<|special_262|>",
+    "<|special_263|>",
+    "<|special_264|>",
+    "<|special_265|>",
+    "<|special_266|>",
+    "<|special_267|>",
+    "<|special_268|>",
+    "<|special_269|>",
+    "<|special_270|>",
+    "<|special_271|>",
+    "<|special_272|>",
+    "<|special_273|>",
+    "<|special_274|>",
+    "<|special_275|>",
+    "<|special_276|>",
+    "<|special_277|>",
+    "<|special_278|>",
+    "<|special_279|>",
+    "<|special_280|>",
+    "<|special_281|>",
+    "<|special_282|>",
+    "<|special_283|>",
+    "<|special_284|>",
+    "<|special_285|>",
+    "<|special_286|>",
+    "<|special_287|>",
+    "<|special_288|>",
+    "<|special_289|>",
+    "<|special_290|>",
+    "<|special_291|>",
+    "<|special_292|>",
+    "<|special_293|>",
+    "<|special_294|>",
+    "<|special_295|>",
+    "<|special_296|>",
+    "<|special_297|>",
+    "<|special_298|>",
+    "<|special_299|>",
+    "<|special_300|>",
+    "<|special_301|>",
+    "<|special_302|>",
+    "<|special_303|>",
+    "<|special_304|>",
+    "<|special_305|>",
+    "<|special_306|>",
+    "<|special_307|>",
+    "<|special_308|>",
+    "<|special_309|>",
+    "<|special_310|>",
+    "<|special_311|>",
+    "<|special_312|>",
+    "<|special_313|>",
+    "<|special_314|>",
+    "<|special_315|>",
+    "<|special_316|>",
+    "<|special_317|>",
+    "<|special_318|>",
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+    "<|special_320|>",
+    "<|special_321|>",
+    "<|special_322|>",
+    "<|special_323|>",
+    "<|special_324|>",
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+    "<|special_327|>",
+    "<|special_328|>",
+    "<|special_329|>",
+    "<|special_330|>",
+    "<|special_331|>",
+    "<|special_332|>",
+    "<|special_333|>",
+    "<|special_334|>",
+    "<|special_335|>",
+    "<|special_336|>",
+    "<|special_337|>",
+    "<|special_338|>",
+    "<|special_339|>",
+    "<|special_340|>",
+    "<|special_341|>",
+    "<|special_342|>",
+    "<|special_343|>",
+    "<|special_344|>",
+    "<|special_345|>",
+    "<|special_346|>",
+    "<|special_347|>",
+    "<|special_348|>",
+    "<|special_349|>",
+    "<|special_350|>",
+    "<|special_351|>",
+    "<|special_352|>",
+    "<|special_353|>",
+    "<|special_354|>",
+    "<|special_355|>",
+    "<|special_356|>",
+    "<|special_357|>",
+    "<|special_358|>",
+    "<|special_359|>",
+    "<|special_360|>",
+    "<|special_361|>",
+    "<|special_362|>",
+    "<|special_363|>",
+    "<|special_364|>",
+    "<|special_365|>",
+    "<|special_366|>",
+    "<|special_367|>",
+    "<|special_368|>",
+    "<|special_369|>",
+    "<|special_370|>",
+    "<|special_371|>",
+    "<|special_372|>",
+    "<|special_373|>",
+    "<|special_374|>",
+    "<|special_375|>",
+    "<|special_376|>",
+    "<|special_377|>",
+    "<|special_378|>",
+    "<|special_379|>",
+    "<|special_380|>",
+    "<|special_381|>",
+    "<|special_382|>",
+    "<|special_383|>",
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+    "<|special_385|>",
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+    "<|special_392|>",
+    "<|special_393|>",
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+    "<|special_402|>",
+    "<|special_403|>",
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+    "<|special_406|>",
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