支持stablelm

README.md

@@ -55,7 +55,7 @@ PaddleFormers 是基于百度深度学习框架 PaddlePaddle 搭建的 Transform
   <tbody>
     <!-- LLM 分类 - 跨行合并开始 -->
     <tr>
-      <td rowspan="18" style="vertical-align: top;">LLM</td>
+      <td rowspan="20" style="vertical-align: top;">LLM</td>
       <td>DeepSeekv3</td>
       <td>deepseek-ai/DeepSeek-V3-Base、deepseek-ai/DeepSeek-V3、deepseek-ai/DeepSeek-V3-0324</td>
       <td>deepseek3</td>
@@ -65,6 +65,11 @@ PaddleFormers 是基于百度深度学习框架 PaddlePaddle 搭建的 Transform
       <td>CohereForAI/aya-expanse-8b、CohereForAI/aya-expanse-32b</td>
       <td>aya</td>
     </tr>
+    <tr>
+      <td>StableLM</td>
+      <td>stabilityai/stable-code-3b</td>
+      <td>-（基础代码补全模型）</td>
+    </tr>
     <tr>
       <td>DeepSeekv3.2</td>
       <td>deepseek-ai/DeepSeek-V3.2</td>

docs/zh/model_capability.md

@@ -2,6 +2,7 @@
 |模型|PT / CPT|SFT|SFT-LoRA|DPO|DPO-LoRA|
 |-|-|-|-|-|-|
 |Cohere|✓|✓|✓|✓|✓|
+|StableLM|✓|✓|✓|✓|✓|
 |DeepSeekv3|✓|✓|✓|✓|✓|
 |🏛️ERNIE-4.5|✓|✓|✓|✓|✓|
 |Gemma3|✓|✓|✓|✓|✓|

paddleformers/cli/utils/llm_utils.py

@@ -508,6 +508,16 @@ def get_lora_target_modules(model):
             "model.visual.blocks.*mlp.up_proj.*",
             "model.visual.blocks.*mlp.down_proj.*",
         ]
+    elif model.config.model_type == "stablelm":
+        target_modules = [
+            ".*q_proj.*",
+            ".*k_proj.*",
+            ".*v_proj.*",
+            ".*o_proj.*",
+            ".*gate_proj.*",
+            ".*up_proj.*",
+            ".*down_proj.*",
+        ]
     elif model.config.model_type == "internlm2":
         # Covers both InternLM2 2.0 and 2.5: both route through the unified
         # `intern/` proxy with `model_type = "internlm2"` and share the same

paddleformers/transformers/__init__.py

@@ -485,6 +485,17 @@
     ],
     "glm_ocr.processor": ["Glm46VProcessor"],
     "glm_ocr.image_processor": ["Glm46VImageProcessor"],
+    "stablelm": [],
+    "stablelm.configuration": ["StableLmConfig"],
+    "stablelm.modeling": [
+        "StableLmModel",
+        "StableLmForCausalLM",
+        "StableLmForCausalLMPipe",
+        "StableLmPretrainedModel",
+        "StableLmRotaryEmbedding",
+        "StableLmDecoderLayer",
+        "StableLmAttention",
+    ],
     "olmo2.configuration": ["Olmo2Config"],
     "olmo2.modeling": ["Olmo2DecoderLayer", "Olmo2Model", "Olmo2ForCausalLM", "Olmo2ForCausalLMPipe"],
     "olmo2": [],
@@ -612,6 +623,7 @@
     from .gemma3 import *
     from .shieldgemma2 import *
     from .glm_ocr import *
+    from .stablelm import *
     from .molmo import *
     from .olmo2 import *
     from .intern_lm3 import *

paddleformers/transformers/auto/configuration.py

@@ -83,6 +83,7 @@
         ("molmo", "MolmoConfig"),
         ("qwen3_5", "Qwen3_5Config"),
         ("qwen3_5_moe", "Qwen3_5MoEConfig"),
+        ("stablelm", "StableLmConfig"),
         ("olmo2", "Olmo2Config"),
         ("internlm3", "InternLM3Config"),
         ("internlm2", "InternLM2Config"),
@@ -139,6 +140,7 @@
         ("qwen3_5_moe", "Qwen3_5MoEForConditionalGeneration"),
         ("qwen3_5", "Qwen3_5ForConditionalGeneration"),
         ("minicpm3", "MiniCPM3Model"),
+        ("stablelm", "StableLmForCausalLM"),
         ("olmo2", "Olmo2ForCausalLM"),
         ("internlm3", "InternLM3ForCausalLM"),
         ("internlm2", "InternLM2"),

paddleformers/transformers/auto/modeling.py

@@ -99,6 +99,7 @@
         ("Gemma4Moe", "gemma4_moe"),
         ("Glm4vMoe", "glm4v_moe"),
         ("GlmOcr", "glm_ocr"),
+        ("StableLm", "stablelm"),
         ("Molmo", "molmo"),
         ("Olmo2", "olmo2"),
         ("InternLM3", "intern_lm3"),

paddleformers/transformers/stablelm/__init__.py

@@ -0,0 +1,52 @@
+# Copyright (c) 2026 PaddlePaddle Authors. 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.
+
+from typing import TYPE_CHECKING
+
+from ...utils.lazy_import import _LazyModule
+
+import_structure = {
+    "configuration": ["StableLmConfig"],
+    "modeling": [
+        "StableLmModel",
+        "StableLmForCausalLM",
+        "StableLmForCausalLMPipe",
+        "StableLmPretrainedModel",
+        "StableLmRotaryEmbedding",
+        "StableLmDecoderLayer",
+        "StableLmAttention",
+    ],
+}
+
+if TYPE_CHECKING:
+    from .configuration import StableLmConfig
+    from .modeling import (
+        StableLmAttention,
+        StableLmDecoderLayer,
+        StableLmForCausalLM,
+        StableLmForCausalLMPipe,
+        StableLmModel,
+        StableLmPretrainedModel,
+        StableLmRotaryEmbedding,
+    )
+
+else:
+    import sys
+
+    sys.modules[__name__] = _LazyModule(
+        __name__,
+        globals()["__file__"],
+        import_structure,
+        module_spec=__spec__,
+    )

paddleformers/transformers/stablelm/configuration.py

@@ -0,0 +1,132 @@
+# Copyright (c) 2026 PaddlePaddle Authors. 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.
+
+import math
+
+from ..configuration_utils import PretrainedConfig
+from ..modeling_rope_utils import rope_config_validation, standardize_rope_params
+
+
+class StableLmConfig(PretrainedConfig):
+    model_type = "stablelm"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=50304,
+        intermediate_size=6912,
+        hidden_size=2560,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=32,
+        hidden_act="silu",
+        max_position_embeddings=4096,
+        initializer_range=0.02,
+        layer_norm_eps=1.0e-5,
+        use_cache=True,
+        tie_word_embeddings=False,
+        rope_theta=10000.0,
+        partial_rotary_factor=0.25,
+        rope_parameters=None,
+        use_qkv_bias=False,
+        qk_layernorm=False,
+        use_parallel_residual=False,
+        hidden_dropout=0.0,
+        attention_dropout=0.0,
+        bos_token_id=0,
+        eos_token_id=0,
+        pad_token_id=None,
+        **kwargs,
+    ):
+        for name, value in {
+            "vocab_size": vocab_size,
+            "hidden_size": hidden_size,
+            "intermediate_size": intermediate_size,
+            "num_hidden_layers": num_hidden_layers,
+            "num_attention_heads": num_attention_heads,
+            "num_key_value_heads": num_key_value_heads,
+            "max_position_embeddings": max_position_embeddings,
+        }.items():
+            if value <= 0:
+                raise ValueError(f"{name} must be positive, got {value!r}")
+        if hidden_size % num_attention_heads:
+            raise ValueError("hidden_size must be divisible by num_attention_heads")
+        if num_attention_heads % num_key_value_heads:
+            raise ValueError("num_attention_heads must be divisible by num_key_value_heads")
+        for name, value in {"hidden_dropout": hidden_dropout, "attention_dropout": attention_dropout}.items():
+            if not isinstance(value, (int, float)) or not math.isfinite(value) or not 0 <= value < 1:
+                raise ValueError(f"{name} must be in [0, 1), got {value!r}")
+        if not isinstance(partial_rotary_factor, (int, float)) or not 0 < partial_rotary_factor <= 1:
+            raise ValueError("partial_rotary_factor must be in (0, 1]")
+        rotary_dim = int(hidden_size // num_attention_heads * partial_rotary_factor)
+        if rotary_dim <= 0 or rotary_dim % 2:
+            raise ValueError("partial RoPE dimension must be positive and even")
+        for name, value in {
+            "layer_norm_eps": layer_norm_eps,
+            "initializer_range": initializer_range,
+            "rope_theta": rope_theta,
+        }.items():
+            if not isinstance(value, (int, float)) or not math.isfinite(value) or value <= 0:
+                raise ValueError(f"{name} must be positive and finite")
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.max_position_embeddings = max_position_embeddings
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.num_key_value_heads = num_key_value_heads
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.layer_norm_eps = layer_norm_eps
+        self.use_cache = use_cache
+        self.tie_word_embeddings = tie_word_embeddings
+        self.use_qkv_bias = use_qkv_bias
+        self.qk_layernorm = qk_layernorm
+        self.use_parallel_residual = use_parallel_residual
+        self.hidden_dropout = hidden_dropout
+        self.attention_dropout = attention_dropout
+
+        self.rope_theta = rope_theta
+        self.partial_rotary_factor = partial_rotary_factor
+        rope_scaling = kwargs.pop("rope_scaling", None)
+        if rope_parameters is None:
+            rope_parameters = rope_scaling
+        if rope_parameters is not None and not isinstance(rope_parameters, dict):
+            raise ValueError("rope_parameters and rope_scaling must be dicts or None")
+        self.rope_parameters = dict(rope_parameters) if rope_parameters is not None else None
+        standardize_rope_params(self, rope_theta=self.rope_theta)
+        effective_rope_theta = self.rope_parameters["rope_theta"]
+        if (
+            not isinstance(effective_rope_theta, (int, float))
+            or not math.isfinite(effective_rope_theta)
+            or effective_rope_theta <= 0
+        ):
+            raise ValueError("rope_parameters.rope_theta must be positive and finite")
+        rope_config_validation(self, ignore_keys={"partial_rotary_factor"})
+
+        self.norm_eps = self.layer_norm_eps
+        self.mlp_bias = False
+
+        if kwargs.get("architectures") is None:
+            kwargs["architectures"] = ["StableLmForCausalLM"]
+        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,
+        )
+        if getattr(self, "head_dim", None) not in (None, hidden_size // num_attention_heads):
+            raise ValueError("StableLM head_dim must equal hidden_size // num_attention_heads")
+        self.head_dim = hidden_size // num_attention_heads