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config.json

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+{
+  "_name_or_path": "openbmb/MiniCPM-SALA",
+  "architectures": [
+    "MiniCPMSALAForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "attn_use_rope": false,
+  "auto_map": {
+    "AutoConfig": "configuration_minicpm_sala.MiniCPMSALAConfig",
+    "AutoModel": "modeling_minicpm_sala.MiniCPMSALAModel",
+    "AutoModelForCausalLM": "modeling_minicpm_sala.MiniCPMSALAForCausalLM",
+    "AutoModelForSeq2SeqLM": "modeling_minicpm_sala.MiniCPMSALAForCausalLM",
+    "AutoModelForSequenceClassification": "modeling_minicpm_sala.MiniCPMSALAForSequenceClassification"
+  },
+  "bos_token_id": 1,
+  "eos_token_id": [
+    2,
+    73440
+  ],
+  "pad_token_id": 2,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.1,
+  "intermediate_size": 16384,
+  "lightning_head_dim": 128,
+  "lightning_nh": 32,
+  "lightning_nkv": 32,
+  "lightning_scale": "1/sqrt(d)",
+  "lightning_use_rope": true,
+  "max_position_embeddings": 524288,
+  "model_type": "minicpm_sala",
+  "mixer_types": [
+    "minicpm4",
+    "lightning-attn",
+    "lightning-attn",
+    "lightning-attn",
+    "lightning-attn",
+    "lightning-attn",
+    "lightning-attn",
+    "lightning-attn",
+    "lightning-attn",
+    "minicpm4",
+    "lightning-attn",
+    "lightning-attn",
+    "lightning-attn",
+    "lightning-attn",
+    "lightning-attn",
+    "lightning-attn",
+    "minicpm4",
+    "minicpm4",
+    "lightning-attn",
+    "lightning-attn",
+    "lightning-attn",
+    "lightning-attn",
+    "minicpm4",
+    "lightning-attn",
+    "lightning-attn",
+    "lightning-attn",
+    "lightning-attn",
+    "lightning-attn",
+    "lightning-attn",
+    "minicpm4",
+    "minicpm4",
+    "minicpm4"
+  ],
+  "sparse_config": {
+    "kernel_size": 32,
+    "kernel_stride": 16,
+    "init_blocks": 1,
+    "block_size": 64,
+    "window_size": 2048,
+    "topk": 64,
+    "use_nope": false,
+    "dense_len": 8192
+  },
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 2,
+  "qk_norm": true,
+  "rand_init": false,
+  "rms_norm_eps": 1e-06,
+  "torch_dtype": "bfloat16",
+  "dtype": "bfloat16",
+  "transformers_version": "4.46.3",
+  "use_cache": true,
+  "vocab_size": 73448,
+  "rope_theta": 10000.0,
+  "scale_emb": 12,
+  "scale_depth": 1.4,
+  "mup_denominator": 32,
+  "dim_model_base": 256,
+  "tie_word_embeddings": false,
+  "use_output_gate": true,
+  "use_output_norm": true,
+  "attn_use_output_gate": true
+}

configuration_minicpm_sala.py

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+# coding=utf-8
+# Copyright 2025 The OpenBMB 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.
+"""MiniCPMSALA model configuration"""
+
+from typing import List, Optional
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+logger = logging.get_logger(__name__)
+
+MINICPMSALA_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
+
+
+class MiniCPMSALAConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`MiniCPMSALAModel`]. It is used to instantiate an MiniCPMSALA
+    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 32000):
+            Vocabulary size of the MiniCPMSALA model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`MiniCPMSALAModel`]
+        hidden_size (`int`, *optional*, defaults to 4096):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 11008):
+            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`.
+        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 2048):
+            The maximum sequence length that this model might ever be used with. MiniCPMSALA supports up to 524288 tokens.
+        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-06):
+            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`.
+        pad_token_id (`int`, *optional*):
+            Padding token id.
+        bos_token_id (`int`, *optional*, defaults to 1):
+            Beginning of stream token id.
+        eos_token_id (`int`, *optional*, defaults to 2):
+            End of stream token id.
+        pretraining_tp (`int`, *optional*, defaults to 1):
+            Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
+            document](https://huggingface.co/docs/transformers/parallelism) to understand more about it. This value is
+            necessary to ensure exact reproducibility of the pretraining results. Please refer to [this
+            issue](https://github.com/pytorch/pytorch/issues/76232).
+        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*):
+            Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
+            strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
+            `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
+            `max_position_embeddings` to the expected new maximum. See the following thread for more information on how
+            these scaling strategies behave:
+            https://www.reddit.com/r/LocalMiniCPM/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
+            experimental feature, subject to breaking API changes in future versions.
+        attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
+            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.
+
+    ```python
+    >>> from transformers import MiniCPMSALAModel, MiniCPMSALAConfig
+
+    >>> # Initializing a MiniCPMSALA style configuration
+    >>> configuration = MiniCPMSALAConfig()
+
+    >>> # Initializing a model from the minicpm_sala style configuration
+    >>> model = MiniCPMSALAModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "minicpm_sala"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=32000,
+        hidden_size=4096,
+        intermediate_size=11008,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=None,
+        hidden_act="silu",
+        max_position_embeddings=2048,
+        initializer_range=0.02,
+        rms_norm_eps=1e-6,
+        use_cache=True,
+        pad_token_id=None,
+        bos_token_id=1,
+        eos_token_id=2,
+        pretraining_tp=1,
+        tie_word_embeddings=True,
+        rope_theta=10000.0,
+        rope_scaling=None,
+        attention_bias=False,
+        attention_dropout=0.0,
+        scale_emb=1,
+        dim_model_base=1,
+        scale_depth=1,
+        mup_denominator=32,
+        sparse_config=None,
+        mixer_types: List[str] = ["minicpm4"],
+        head_dim: Optional[int] = None,
+        use_output_gate: bool = False,
+        use_output_norm: bool = False,
+        lightning_use_rope: bool = True,
+        lightning_nkv: Optional[int] = None,
+        lightning_nh: Optional[int] = None,
+        qk_norm: bool = False,
+        lightning_head_dim: Optional[int] = None,
+        rand_init: bool = False,
+        train_mlp: bool = True,
+        attn_use_rope: bool = True,
+        lightning_scale: str = "1/sqrt(d)",
+        shift_labels: bool = True,
+        attn_use_output_gate: bool = 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
+
+        # for backward compatibility
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+
+        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.pretraining_tp = pretraining_tp
+        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
+        self.scale_emb = scale_emb
+        self.dim_model_base = dim_model_base
+        self.scale_depth = scale_depth
+        # only used for Eagle Head
+        self.mup_denominator = mup_denominator
+
+        # sparse config
+        self.sparse_config = sparse_config
+
+        self.mixer_types = mixer_types
+        if self.mixer_types is None or len(self.mixer_types) == 0:
+            # Default to MiniCPMSALA4 (full attention) for all layers
+            self.mixer_types = ["minicpm4"] * self.num_hidden_layers
+        elif len(self.mixer_types) < self.num_hidden_layers:
+            self.mixer_types = (mixer_types * self.num_hidden_layers)[
+                : self.num_hidden_layers
+            ]
+        elif len(self.mixer_types) == self.num_hidden_layers:
+            self.mixer_types = mixer_types
+        else:
+            raise ValueError(f"Invalid number of mixer types: {len(self.mixer_types)}")
+        assert len(self.mixer_types) == self.num_hidden_layers
+
+        # for Lightning
+        if head_dim is None:
+            head_dim = self.hidden_size // self.num_attention_heads
+        self.head_dim = head_dim
+        self.use_output_norm = use_output_norm
+        self.use_output_gate = use_output_gate
+        self.lightning_use_rope = lightning_use_rope
+        self.qk_norm = qk_norm
+        self.lightning_nkv = (
+            lightning_nkv if lightning_nkv is not None else self.num_key_value_heads
+        )
+        self.lightning_nh = (
+            lightning_nh if lightning_nh is not None else self.num_attention_heads
+        )
+        self.lightning_head_dim = (
+            lightning_head_dim if lightning_head_dim is not None else self.head_dim
+        )
+        self.lightning_scale = lightning_scale
+        self.attn_use_rope = attn_use_rope
+        self.shift_labels = shift_labels
+        self.attn_use_output_gate = attn_use_output_gate
+
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
+        try:
+            import flash_attn
+
+            self._attn_implementation = "flash_attention_2"
+        except ImportError:
+            pass
+
+    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) != 2:
+            raise ValueError(
+                "`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, "
+                f"got {self.rope_scaling}"
+            )
+        rope_scaling_type = self.rope_scaling.get("type", None)
+        rope_scaling_factor = self.rope_scaling.get("factor", None)
+        if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
+            raise ValueError(
+                f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
+            )
+        if (
+            rope_scaling_factor is None
+            or not isinstance(rope_scaling_factor, float)
+            or rope_scaling_factor <= 1.0
+        ):
+            raise ValueError(
+                f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}"
+            )

generation_config.json

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+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": [
+    2,
+    73440
+  ],
+  "pad_token_id": 2,
+  "transformers_version": "4.56.1"
+}

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+  "use_default_system_prompt": false,
+  "chat_template": "{%- if tools %}\n    {%- set tool_definitions %}\n        {{- \"# Tools\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n        {%- for tool in tools %}\n            {{- \"\\n\" }}\n            {{- tool | tojson(ensure_ascii=False) }}\n        {%- endfor %}\n        {{- '\\n</tools>\\n\\nTool usage guidelines:\\n- You may call zero or more functions. If no function calls are needed, just answer normally and do not include any <function ... </function>.\\n- When calling a function, return an XML object within <function ... </function> using:\\n<function name=\"function-name\"><param name=\"param-name\">param-value</param></function>\\n- param-value may be multi-line. If it contains <, & or newline characters, wrap it in a CDATA block: <param name=\"param-name\"><![CDATA[...multi-line value...]]></param>' }}\n    {%- endset %}\n    \n    {{- '<|im_start|>system\\n' }}\n    {%- if messages[0].role == 'system' %}\n        {%- if '<tool_def_sep>' in messages[0].content %}\n            {{- messages[0].content.replace('<tool_def_sep>', tool_definitions) }}\n        {%- else %}\n            {{- messages[0].content + '\\n\\n' + tool_definitions }}\n        {%- endif %}\n    {%- else %}\n        {{- tool_definitions.lstrip() }}\n    {%- endif %}\n    {{- '<|im_end|>\\n' }}\n{%- else %}\n    {%- if messages[0].role == 'system' %}\n        {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n    {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for message in messages[::-1] %}\n    {%- set index = (messages|length - 1) - loop.index0 %}\n    {%- if ns.multi_step_tool and message.role == \"user\" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}\n        {%- set ns.multi_step_tool = false %}\n        {%- set ns.last_query_index = index %}\n    {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n    {%- if message.content is string %}\n        {%- set content = message.content %}\n    {%- else %}\n        {%- set content = '' %}\n    {%- endif %}\n    {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n        {{- '<|im_start|>' + message.role + '\\n' + content + '<|im_end|>' + '\\n' }}\n    {%- elif message.role == \"assistant\" %}\n        {%- set reasoning_content = '' %}\n        {%- if message.reasoning_content is string %}\n            {%- set reasoning_content = message.reasoning_content %}\n        {%- else %}\n            {%- if '</think>' in content %}\n                {%- set reasoning_content = content.split('</think>')[0].rstrip('\\n').split('<think>')[-1].lstrip('\\n') %}\n                {%- set content = content.split('</think>')[-1].lstrip('\\n') %}\n            {%- endif %}\n        {%- endif %}\n        \n        {%- if message.tool_calls %}\n            {%- set content_parts = content.split('<tool_sep>') %}\n            {%- set processed_content = content_parts[0] %}\n            {%- set tool_calls_count = message.tool_calls|length %}\n            {%- set tool_sep_count = content_parts|length - 1 %}\n            {%- set min_count = [tool_calls_count, tool_sep_count]|min %}\n            \n            {%- for i in range(1, content_parts|length) %}\n                {%- set tool_index = i - 1 %}\n                {%- if tool_index < tool_calls_count %}\n                    {%- set tool_call = message.tool_calls[tool_index] %}\n                    {%- if tool_call.function %}\n                        {%- set tool_call = tool_call.function %}\n                    {%- endif %}\n                    {%- set single_tool_xml %}\n                        {{- '<function name=\"' ~ tool_call.name ~ '\">' }}\n                        {%- if tool_call.arguments %}\n                            {%- set args_dict = tool_call.arguments %}\n                            {%- for param_name, param_value in args_dict.items() %}\n                                {{- '<param name=\"' ~ param_name ~ '\">' }}\n                                {%- if param_value is string and ('<' in param_value or '&' in param_value or '\\n' in param_value) %}\n                                    {{- '<![CDATA[' + param_value + ']]>' }}\n                                {%- else %}\n                                    {{- param_value }}\n                                {%- endif %}\n                                {{- '</param>' }}\n                            {%- endfor %}\n                        {%- endif %}\n                        {{- '</function>' }}\n                    {%- endset %}\n                    {%- set processed_content = processed_content + single_tool_xml + content_parts[i] %}\n                {%- else %}\n                    {%- set processed_content = processed_content + content_parts[i] %}\n                {%- endif %}\n            {%- endfor %}\n            \n            {%- if tool_calls_count > tool_sep_count %}\n                {%- for remaining_index in range(tool_sep_count, tool_calls_count) %}\n                    {%- set tool_call = message.tool_calls[remaining_index] %}\n                    {%- if tool_call.function %}\n                        {%- set tool_call = tool_call.function %}\n                    {%- endif %}\n                    {%- set remaining_tool_xml %}\n                        {{- '<function name=\"' ~ tool_call.name ~ '\">' }}\n                        {%- if tool_call.arguments %}\n                            {%- set args_dict = tool_call.arguments %}\n                            {%- for param_name, param_value in args_dict.items() %}\n                                {{- '<param name=\"' ~ param_name ~ '\">' }}\n                                {%- if param_value is string and ('<' in param_value or '&' in param_value or '\\n' in param_value) %}\n                                    {{- '<![CDATA[' + param_value + ']]>' }}\n                                {%- else %}\n                                    {{- param_value }}\n                                {%- endif %}\n                                {{- '</param>' }}\n                            {%- endfor %}\n                        {%- endif %}\n                        {{- '</function>' }}\n                    {%- endset %}\n                    {%- set processed_content = processed_content + remaining_tool_xml %}\n                {%- endfor %}\n            {%- endif %}\n            \n            {%- set content = processed_content %}\n        {%- endif %}\n        \n        {%- if loop.index0 > ns.last_query_index %}\n            {%- if reasoning_content %}\n                {{- '<|im_start|>' + message.role + '\\n<think>\\n' + reasoning_content.strip('\\n') + '\\n</think>\\n\\n' + content.lstrip('\\n') }}\n            {%- else %}\n                {{- '<|im_start|>' + message.role + '\\n' + content }}\n            {%- endif %}\n        {%- else %}\n            {{- '<|im_start|>' + message.role + '\\n' + content }}\n        {%- endif %}\n        \n        {%- if message.tool_calls and not has_tool_sep %}\n            {%- for tool_call in message.tool_calls %}\n                {%- if (loop.first and content) or (not loop.first) %}\n                    {{- '\\n' }}\n                {%- endif %}\n                {%- if tool_call.function %}\n                    {%- set tool_call = tool_call.function %}\n                {%- endif %}\n                {{- '<function name=\"' ~ tool_call.name ~ '\">' }}\n                {%- if tool_call.arguments %}\n                    {%- set args_dict = tool_call.arguments %}\n                    {%- for param_name, param_value in args_dict.items() %}\n                        {{- '<param name=\"' ~ param_name ~ '\">' }}\n                        {%- if param_value is string and ('<' in param_value or '&' in param_value or '\\n' in param_value) %}\n                            {{- '<![CDATA[' + param_value + ']]>' }}\n                        {%- else %}\n                            {{- param_value }}\n                        {%- endif %}\n                        {{- '</param>' }}\n                    {%- endfor %}\n                {%- endif %}\n                {{- '</function>' }}\n            {%- endfor %}\n        {%- endif %}\n        {{- '<|im_end|>\\n' }}\n    {%- elif message.role == \"tool\" %}\n        {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n            {{- '<|im_start|>user' }}\n        {%- endif %}\n        {{- '\\n<tool_response>\\n' }}\n        {%- if message.content is string %}\n            {{- content }}\n        {%- else %}\n            {{- message.content | tojson(ensure_ascii=False) }}\n        {%- endif %}\n        {{- '\\n</tool_response>' }}\n        {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n            {{- '<|im_end|>\\n' }}\n        {%- endif %}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|im_start|>assistant\\n' }}\n{%- endif %}"
+}