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README.md

@@ -13,7 +13,7 @@ tags:
 
 Meet Keye-VL-671B-A37B — the most powerful multi-modal language model in the Keye series to date.
 
-As one of the largest and most capable MLLMs currently in existence, Keye-VL 671B demonstrates achieved top-tier and in some cases even leading performance in text understanding and generation, complex visual perception and reasoning, comprehensive video understanding, and Olympic-level mathematical reasoning.
+As one of the largest and most capable MLLMs currently in existence, Keye-VL-671B-A37B demonstrates top-tier and in some cases even leading performance in text understanding and generation, complex visual perception and reasoning, comprehensive video understanding, and Olympic-level mathematical reasoning.
 
 #### Key Enhancements:
 
@@ -32,36 +32,9 @@ As one of the largest and most capable MLLMs currently in existence, Keye-VL 671
 
 ## Model Performance
 
-![Performance Comparison](figures/radar.png)
-
-|                       | Benchmarks     | Seed1.5-VL thinking | dots.vlm1 | Qwen3-VL-235B-A22B thinking | Keye-VL-671B-A37B |
-| --------------------- | -------------- | :-----------------: | :-------: | :-------------------------: | :-------------------: |
-| STEM/Reasoning        | MMMU_VAL       |        77.9         |   80.11   |            80.6             |       **83.78**       |
-|                       | MMMU_Pro       |        67.6         |   70.11   |            69.3             |       **72.49**       |
-|                       | MathVision     |        68.7         |   69.64   |          **74.6**           |         69.11         |
-|                       | MathVista      |        85.6         |   85.0    |            85.8             |       **86.2**        |
-|                       | OlympiadBench  |        65.0         |     -     |              -              |       **74.92**       |
-|                       | VisuLogic      |        35.0         |   32.2    |            34.4             |       **35.4**        |
-| General VQA           | RealWorldQA    |        78.4         |   79.08   |            81.3             |       **86.54**       |
-|                       | MMStar         |        77.8         |   76.67   |            78.7             |       **86.67**       |
-|                       | MMBench-en     |        89.9         |   89.32   |            90.6             |       **95.74**       |
-|                       | MMbench-cn     |        89.1         |   88.24   |              -              |       **94.27**       |
-|                       | MMVP           |        69.3         |   72.0    |              -              |       **88.0**        |
-|                       | V*             |        89.0         |     -     |              -              |       **90.05**       |
-|                       | HallusionBench |        60.3         |   64.83   |            66.7             |       **72.3**        |
-| Video                 | VideoMME       |        77.9         |     -     |          **79.0**           |       **79.0**        |
-|                       | LongVideoBench |        74.0         |     -     |         65.2 (fp8)          |       **79.0**        |
-|                       | MMVU           |        70.1         |     -     |         78.4 (fp8)          |       **86.6**        |
-|                       | TempCompass    |      **83.7**       |     -     |         81.03 (fp8)         |         77.75         |
-| Text Recog./Doc/chart | TextVQA        |      **81.8**       |     -     |              -              |         76.21         |
-|                       | DocVQA_VAL     |      **96.9**       |   96.52   |            96.5             |         95.39         |
-|                       | ChartQA_TEST   |      **89.1**       |   87.68   |              -              |         86.68         |
-|                       | InfoVQA        |      **91.2**       |     -     |            89.5             |         86.93         |
-|                       | CharXiv (RQ)   |        60.2         |   64.4    |            66.1             |       **79.4**        |
-|                       | CharXiv (DQ)   |        92.6         |   92.1    |              -              |       **94.5**        |
-|                       | AI2D_TEST      |        87.3         |   88.37   |            89.2             |       **91.19**       |
-| Pure Text             | AIME2025       |          -          |   85.83   |          **89.7**           |         83.3          |
-|                       | GPQA           |          -          | **72.78** |              -              |         71.21         |
+![Performance Comparison](https://github.com/Kwai-Keye/Keye/blob/main/asset/radar.png)
+
+![Performance on Public Benchmarks](https://github.com/Kwai-Keye/Keye/blob/main/asset/performance.png)
 
 ## Quickstart
 

config.json

@@ -5,7 +5,7 @@
   "attention_bias": false,
   "attention_dropout": 0.0,
   "auto_map": {
-    "AutoConfig": "configuration_deepseek.DeepseekR1Config"
+    "AutoConfig": "configuration_deepseek.KeyeVLMoeConfig"
   },
   "bos_token_id": 0,
   "dtype": "bfloat16",
@@ -244,8 +244,7 @@
     ],
     "attention_dropout": 0.0,
     "auto_map": {
-      "AutoConfig": "configuration_deepseek.KeyeVisionConfig",
-      "AutoModel": "modeling_deepseek.SiglipVisionModel"
+      "AutoConfig": "configuration_deepseek.KeyeVisionConfig"
     },
     "has_learnable_position_embedding": true,
     "hidden_act": "gelu_pytorch_tanh",

configuration_deepseek.py

@@ -60,7 +60,7 @@ class KeyeVisionConfig(PretrainedConfig):
         self.tokens_per_second = tokens_per_second
 
 
-class DeepseekR1Config(PretrainedConfig):
+class KeyeVLMoeConfig(PretrainedConfig):
     r"""
     This is the configuration class to store the configuration of a [`KeyeModel`]. It is used to instantiate a
     KeyeVLMoeForConditionalGeneration model according to the specified arguments, defining the model architecture.
@@ -164,7 +164,7 @@ class DeepseekR1Config(PretrainedConfig):
     >>> configuration = model.config
     ```"""
 
-    model_type = "deepseek_r1"
+    model_type = "KeyeVL"
     sub_configs = {"vision_config": KeyeVisionConfig}
     keys_to_ignore_at_inference = ["past_key_values"]
 
@@ -263,4 +263,4 @@ class DeepseekR1Config(PretrainedConfig):
             **kwargs,
         )
 
-__all__ = ["DeepseekR1Config"]
+__all__ = ["KeyeVLMoeConfig"]