Add MiniCPM-Llama3-V-2_5

README.md

@@ -25,6 +25,7 @@ English | [<a href="README_zh-CN.md">简体中文</a>]
 
 ## 🆕 News
 
+- **[2024-05-21]** We have supported [**MiniCPM-Llama3-V2.5**](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5), the latest model in the MiniCPM-V series with a significant performance improvement 🔥🔥🔥
 - **[2024-05-15]** We have supported [**PaliGemma-3B**](https://huggingface.co/google/paligemma-3b-pt-448), a versatile and lightweight vision-language model released by Google 🔥🔥🔥
 - **[2024-05-14]** We have supported [**GPT-4o**](https://openai.com/index/hello-gpt-4o/) 🔥🔥🔥
 - **[2024-05-07]** We have supported [**XVERSE-V-13B**](https://github.com/xverse-ai/XVERSE-V-13B/blob/main/vxverse/models/vxverse.py), thanks to [**YJY123**](https://github.com/YJY123) 🔥🔥🔥
@@ -34,7 +35,6 @@ English | [<a href="README_zh-CN.md">简体中文</a>]
 - **[2024-04-28]** We have supported [**POPE**](https://github.com/AoiDragon/POPE), a benchmark for object hallucination evaluation 🔥🔥🔥
 - **[2024-04-25]** We have supported [**Reka API**](https://www.reka.ai), the API model ranked first in [**Vision-Arena**](https://huggingface.co/spaces/WildVision/vision-arena) 🔥🔥🔥
 - **[2024-04-21]** We have noticed a minor issue with the MathVista evaluation script (which may negatively affect the performance). We have fixed it and updated the leaderboard accordingly
-- **[2024-04-17]** We have supported [**InternVL-Chat-V1.5**](https://github.com/OpenGVLab/InternVL/) 🔥🔥🔥
 
 ## 📊 Datasets, Models, and Evaluation Results
 
@@ -82,7 +82,7 @@ VLMEvalKit will use an **judge LLM** to extract answer from the output if you se
 | [**VisualGLM-6B**](https://huggingface.co/THUDM/visualglm-6b)🚅 | [**InternLM-XComposer-7B**](https://huggingface.co/internlm/internlm-xcomposer-7b)🚅🎞️ | [**ShareGPT4V-[7B/13B]**](https://sharegpt4v.github.io)🚅     | [**TransCore-M**](https://github.com/PCIResearch/TransCore-M) |
 | [**LLaVA (XTuner)**](https://huggingface.co/xtuner/llava-internlm-7b)🚅 | [**CogVLM-17B-Chat**](https://huggingface.co/THUDM/cogvlm-chat-hf)🚅 | [**SharedCaptioner**](https://huggingface.co/spaces/Lin-Chen/Share-Captioner)🚅 | [**CogVLM-Grounding-Generalist**](https://huggingface.co/THUDM/cogvlm-grounding-generalist-hf)🚅 |
 | [**Monkey**](https://github.com/Yuliang-Liu/Monkey)🚅         | [**EMU2-Chat**](https://github.com/baaivision/Emu)🚅🎞️         | [**Yi-VL-[6B/34B]**](https://huggingface.co/01-ai/Yi-VL-6B)  | [**MMAlaya**](https://huggingface.co/DataCanvas/MMAlaya)🚅    |
-| [**InternLM-XComposer2-[1.8B/7B]**](https://huggingface.co/internlm/internlm-xcomposer2-vl-7b)🚅🎞️ | [**MiniCPM-[V1/V2]**](https://huggingface.co/openbmb/MiniCPM-V)🚅 | [**OmniLMM-12B**](https://huggingface.co/openbmb/OmniLMM-12B) | [**InternVL-Chat Series**](https://github.com/OpenGVLab/InternVL)🚅 |
+| [**InternLM-XComposer2-[1.8B/7B]**](https://huggingface.co/internlm/internlm-xcomposer2-vl-7b)🚅🎞️ | [**MiniCPM-[V1/V2/V2.5]**](https://huggingface.co/openbmb/MiniCPM-V)🚅 | [**OmniLMM-12B**](https://huggingface.co/openbmb/OmniLMM-12B) | [**InternVL-Chat Series**](https://github.com/OpenGVLab/InternVL)🚅 |
 | [**DeepSeek-VL**](https://github.com/deepseek-ai/DeepSeek-VL/tree/main)🎞️ | [**LLaVA-NeXT**](https://llava-vl.github.io/blog/2024-01-30-llava-next/)🚅 | [**Bunny-Llama3**](https://huggingface.co/BAAI/Bunny-Llama-3-8B-V)🚅 | [**XVERSE-V-13B**](https://github.com/xverse-ai/XVERSE-V-13B/blob/main/vxverse/models/vxverse.py) |
 | [**PaliGemma-3B**](https://huggingface.co/google/paligemma-3b-pt-448) 🚅 | | |
 
@@ -97,7 +97,7 @@ Note that some VLMs may not be able to run under certain transformer versions, w
 - **Please use** `transformers==4.33.0` **for**: `Qwen series`, `Monkey series`, `InternLM-XComposer Series`, `mPLUG-Owl2`, `OpenFlamingo v2`, `IDEFICS series`, `VisualGLM`, `MMAlaya`, `SharedCaptioner`, `MiniGPT-4 series`, `InstructBLIP series`, `PandaGPT`, `VXVERSE`.
 - **Please use** `transformers==4.37.0` **for**: `LLaVA series`, `ShareGPT4V series`, `TransCore-M`, `LLaVA (XTuner)`, `CogVLM Series`, `EMU2 Series`, `Yi-VL Series`, `MiniCPM-V series`, `OmniLMM-12B`, `DeepSeek-VL series`, `InternVL series`.
 - **Please use** `transformers==4.39.0` **for**: `LLaVA-Next series`.
-- **Please use** `transformers==4.40.0` **for**: `IDEFICS2`, `Bunny-Llama3`.
+- **Please use** `transformers==4.40.0` **for**: `IDEFICS2`, `Bunny-Llama3`, `MiniCPM-Llama3-V2.5`.
 
 ```python
 # Demo

README_zh-CN.md

@@ -23,6 +23,7 @@
 
 ## 🆕 更新
 
+- **[2024-05-21]** 支持了 [**MiniCPM-Llama3-V2.5**](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5), MiniCPM-V 系列最新的模型，拥有较大的性能提升。🔥🔥🔥
 - **[2024-05-15]** 支持了 [**PaliGemma-3B**](https://huggingface.co/google/paligemma-3b-pt-448), 一个谷歌开源的 3B 多模态模型。 🔥🔥🔥
 - **[2024-05-14]** 支持了 [**GPT-4o**](https://openai.com/index/hello-gpt-4o/) 🔥🔥🔥
 - **[2024-05-07]** 支持了 [**XVERSE-V-13B**](https://github.com/xverse-ai/XVERSE-V-13B/blob/main/vxverse/models/vxverse.py), 感谢 [**YJY123**](https://github.com/YJY123) 🔥🔥🔥
@@ -32,7 +33,6 @@
 - **[2024-04-28]** 支持 [**POPE**](https://github.com/AoiDragon/POPE), 这是一个目标幻觉问题检测的数据集 🔥🔥🔥
 - **[2024-04-25]** 支持了 [**Reka**](https://www.reka.ai), 这个 API 模型在 [**Vision-Arena**](https://huggingface.co/spaces/WildVision/vision-arena) 排名第一 🔥🔥🔥
 - **[2024-04-21]** 修复了 MathVista 评估脚本的一个小问题（可能会对性能产生较小的负面影响），并相应更新了排行榜
-- **[2024-04-17]** 支持 [**InternVL-Chat-V1.5**](https://github.com/OpenGVLab/InternVL/) 🔥🔥🔥
 
 ## 📊 评测结果，支持的数据集和模型 <a id="data-model-results"></a>
 ### 评测结果
@@ -80,7 +80,7 @@
 | [**VisualGLM-6B**](https://huggingface.co/THUDM/visualglm-6b)🚅 | [**InternLM-XComposer-7B**](https://huggingface.co/internlm/internlm-xcomposer-7b)🚅🎞️ | [**ShareGPT4V-[7B/13B]**](https://sharegpt4v.github.io)🚅     | [**TransCore-M**](https://github.com/PCIResearch/TransCore-M) |
 | [**LLaVA (XTuner)**](https://huggingface.co/xtuner/llava-internlm-7b)🚅 | [**CogVLM-17B-Chat**](https://huggingface.co/THUDM/cogvlm-chat-hf)🚅 | [**SharedCaptioner**](https://huggingface.co/spaces/Lin-Chen/Share-Captioner)🚅 | [**CogVLM-Grounding-Generalist**](https://huggingface.co/THUDM/cogvlm-grounding-generalist-hf)🚅 |
 | [**Monkey**](https://github.com/Yuliang-Liu/Monkey)🚅         | [**EMU2-Chat**](https://github.com/baaivision/Emu)🚅🎞️         | [**Yi-VL-[6B/34B]**](https://huggingface.co/01-ai/Yi-VL-6B)  | [**MMAlaya**](https://huggingface.co/DataCanvas/MMAlaya)🚅    |
-| [**InternLM-XComposer2-[1.8B/7B]**](https://huggingface.co/internlm/internlm-xcomposer2-vl-7b)🚅🎞️ | [**MiniCPM-[V1/V2]**](https://huggingface.co/openbmb/MiniCPM-V)🚅 | [**OmniLMM-12B**](https://huggingface.co/openbmb/OmniLMM-12B) | [**InternVL-Chat Series**](https://github.com/OpenGVLab/InternVL)🚅 |
+| [**InternLM-XComposer2-[1.8B/7B]**](https://huggingface.co/internlm/internlm-xcomposer2-vl-7b)🚅🎞️ | [**MiniCPM-[V1/V2/V2.5]**](https://huggingface.co/openbmb/MiniCPM-V)🚅 | [**OmniLMM-12B**](https://huggingface.co/openbmb/OmniLMM-12B) | [**InternVL-Chat Series**](https://github.com/OpenGVLab/InternVL)🚅 |
 | [**DeepSeek-VL**](https://github.com/deepseek-ai/DeepSeek-VL/tree/main)🎞️ | [**LLaVA-NeXT**](https://llava-vl.github.io/blog/2024-01-30-llava-next/)🚅 | [**Bunny-Llama3**](https://huggingface.co/BAAI/Bunny-Llama-3-8B-V)🚅 | [**XVERSE-V-13B**](https://github.com/xverse-ai/XVERSE-V-13B/blob/main/vxverse/models/vxverse.py) |
 | [**PaliGemma-3B**](https://huggingface.co/google/paligemma-3b-pt-448) 🚅 | | |
 
@@ -97,7 +97,7 @@
 - **请用** `transformers==4.33.0` **来运行**: `Qwen series`, `Monkey series`, `InternLM-XComposer Series`, `mPLUG-Owl2`, `OpenFlamingo v2`, `IDEFICS series`, `VisualGLM`, `MMAlaya`, `SharedCaptioner`, `MiniGPT-4 series`, `InstructBLIP series`, `PandaGPT`, `VXVERSE`.
 - **请用** `transformers==4.37.0 ` **来运行**: `LLaVA series`, `ShareGPT4V series`, `TransCore-M`, `LLaVA (XTuner)`, `CogVLM Series`, `EMU2 Series`, `Yi-VL Series`, `MiniCPM-V series`, `OmniLMM-12B`, `DeepSeek-VL series`, `InternVL series`.
 - **请用** `transformers==4.39.0 ` **来运行**: `LLaVA-Next series`.
-- **请用** `transformers==4.40.0 ` **来运行**: `IDEFICS2`, `Bunny-Llama3`.
+- **请用** `transformers==4.40.0 ` **来运行**: `IDEFICS2`, `Bunny-Llama3`, `MiniCPM-Llama3-V2.5`.
 
 **如何测试一个 VLM 是否可以正常运行:**
 

vlmeval/config.py

@@ -23,6 +23,7 @@
     'MMAlaya':partial(MMAlaya, model_path='DataCanvas/MMAlaya'),
     'MiniCPM-V':partial(MiniCPM_V, model_path='openbmb/MiniCPM-V'),
     'MiniCPM-V-2':partial(MiniCPM_V, model_path='openbmb/MiniCPM-V-2'),
+    'MiniCPM-Llama3-V-2_5':partial(MiniCPM_Llama3_V, model_path='openbmb/MiniCPM-Llama3-V-2_5'),
     'OmniLMM_12B':partial(OmniLMM12B, model_path='openbmb/OmniLMM-12B', root=OmniLMM_ROOT),
     'MGM_7B':partial(Mini_Gemini, model_path='YanweiLi/MGM-7B-HD', root=Mini_Gemini_ROOT),
     'Bunny-llama3-8B': partial(BunnyLLama3, model_path='BAAI/Bunny-Llama-3-8B-V'),

vlmeval/vlm/__init__.py

@@ -8,7 +8,7 @@
 from .idefics import IDEFICS, IDEFICS2
 from .instructblip import InstructBLIP
 from .llava import LLaVA, LLaVA_Next, LLaVA_XTuner
-from .minicpm_v import MiniCPM_V
+from .minicpm_v import MiniCPM_V, MiniCPM_Llama3_V
 from .minigpt4 import MiniGPT4
 from .mmalaya import MMAlaya
 from .monkey import Monkey, MonkeyChat

vlmeval/vlm/minicpm_v.py

@@ -83,3 +83,133 @@ def generate_inner(self, message, dataset=None):
             **default_kwargs
         )
         return res
+
+
+class MiniCPM_Llama3_V(BaseModel):
+    INSTALL_REQ = False
+    INTERLEAVE = True
+
+    def __init__(self, model_path='openbmb/MiniCPM-Llama3-V-2_5', **kwargs):
+        assert model_path is not None
+        self.model_path = model_path
+        print(f'load from {self.model_path}')
+        self.model = AutoModel.from_pretrained(self.model_path, trust_remote_code=True)
+        self.model = self.model.to(dtype=torch.float16)
+        self.model.eval().cuda()
+        self.kwargs = kwargs
+        self.tokenizer = AutoTokenizer.from_pretrained(self.model_path, trust_remote_code=True)
+        torch.cuda.empty_cache()
+        self.num_beams = 1 if self.model_path == 'openbmb/MiniCPM-V' else 3
+        self.options_system_prompt = ('Carefully read the following question and select the letter corresponding '
+                                      'to the correct answer. Highlight the applicable choices without giving '
+                                      'explanations.')
+        self.wo_options_system_prompt = 'Carefully read the following question Answer the question directly.'
+        self.detail_system_prompt = 'Answer this question in detail.'
+        self.vqa_prompt = 'Answer the question using a single word or phrase.'
+
+    def use_custom_prompt(self, dataset):
+        if listinstr(['multi-choice', 'VQA'], DATASET_TYPE(dataset)):
+            return True
+        elif dataset is not None and listinstr(['HallusionBench'], dataset):
+            return True
+        return False
+
+    def build_prompt(self, line, dataset=None):
+        if dataset is None:
+            dataset = self.dataset
+
+        if isinstance(line, int):
+            line = self.data.iloc[line]
+
+        tgt_path = self.dump_image(line, dataset)
+        system_prompt = ''
+
+        question = line['question']
+        if DATASET_TYPE(dataset) == 'multi-choice':
+            options = {
+                cand: line[cand]
+                for cand in string.ascii_uppercase
+                if cand in line and not pd.isna(line[cand])
+            }
+            options_prompt = 'Options:\n'
+            for key, item in options.items():
+                options_prompt += f'{key}. {item}\n'
+            hint = line['hint'] if ('hint' in line and not pd.isna(line['hint'])) else None
+            prompt = ''
+            if hint is not None:
+                prompt += f'Hint: {hint}\n'
+            prompt += f'Question: {question}\n'
+            if len(options):
+                prompt += options_prompt
+                system_prompt = self.options_system_prompt + '\nPlease just indicate your choice.'
+            else:
+                system_prompt = self.wo_options_system_prompt
+            if 'MMMU' in dataset:  # Corner Case
+                prompt = system_prompt + '\n' + prompt
+                system_prompt = ''
+        elif dataset is not None and listinstr(['HallusionBench'], dataset):
+            question = line['question'] + ' Yes or No?'
+            prompt = question
+        elif dataset is not None and listinstr(['MME'], dataset):
+            question = line['question'] + ' Yes or No?'
+            prompt = question
+        elif dataset is not None and listinstr(['OCRBench'], dataset):
+            system_prompt = self.vqa_prompt
+            question = line['question']
+            prompt = question
+        elif DATASET_TYPE(dataset) == 'VQA':
+            if listinstr(['LLaVABench'], dataset):
+                system_prompt = ''
+                prompt = question
+            elif listinstr(['MMVet'], dataset):
+                system_prompt = self.detail_system_prompt
+                prompt = question
+            else:
+                system_prompt = self.vqa_prompt
+                prompt = question
+
+        msgs = []
+        if system_prompt:
+            msgs.append(dict(type='text', value=system_prompt))
+        if isinstance(tgt_path, list):
+            msgs.extend([dict(type='image', value=p) for p in tgt_path])
+        else:
+            msgs = [dict(type='image', value=tgt_path)]
+        msgs.append(dict(type='text', value=prompt))
+        return msgs
+
+    def generate_inner(self, message, dataset=None):
+        if DATASET_TYPE(dataset) == 'multi-choice':
+            max_new_tokens = 200
+        elif DATASET_TYPE(dataset) == 'Y/N':
+            max_new_tokens = 3
+        else:
+            max_new_tokens = 1024
+
+        default_kwargs = dict(
+            max_new_tokens=max_new_tokens,
+            sampling=False,
+            num_beams=self.num_beams,
+        )
+        default_kwargs.update(self.kwargs)
+
+        content = []
+        for x in message:
+            if x['type'] == 'text':
+                content.append(x['value'])
+            elif x['type'] == 'image':
+                image = Image.open(x['value']).convert('RGB')
+                content.append(image)
+        msgs = [{'role': 'user', 'content': content}]
+
+        res = self.model.chat(
+            msgs=msgs,
+            context=None,
+            image=None,
+            tokenizer=self.tokenizer,
+            **default_kwargs
+        )
+
+        if isinstance(res, tuple) and len(res) > 0:
+            res = res[0]
+        return res