first

LICENSE

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+The CogVLM License
+
+1. Definitions
+
+“Licensor” means the CogVLM Model Team that distributes its Software.
+
+“Software” means the CogVLM model parameters made available under this license.
+
+2. License Grant
+
+Under the terms and conditions of this license, the Licensor hereby grants you a non-exclusive, worldwide, non-transferable, non-sublicensable, revocable, royalty-free copyright license.
+This license permits you to use all open-source models in this repository for academic research free. Users who wish to use the models for commercial purposes must register [here](https://open.bigmodel.cn/mla/form).
+Registered users may use the models for commercial activities free of charge, but must comply with all terms and conditions of this license.
+The license notice shall be included in all copies or substantial portions of the Software.
+
+3. Restriction
+
+You will not use, copy, modify, merge, publish, distribute, reproduce, or create derivative works of the Software, in whole or in part, for any military, or illegal purposes.
+
+You will not use the Software for any act that may undermine China's national security and national unity, harm the public interest of society, or infringe upon the rights and interests of human beings.
+
+4. Disclaimer
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+5. Limitation of Liability
+
+EXCEPT TO THE EXTENT PROHIBITED BY APPLICABLE LAW, IN NO EVENT AND UNDER NO LEGAL THEORY, WHETHER BASED IN TORT, NEGLIGENCE, CONTRACT, LIABILITY, OR OTHERWISE WILL ANY LICENSOR BE LIABLE TO YOU FOR ANY DIRECT, INDIRECT, SPECIAL, INCIDENTAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES, OR ANY OTHER COMMERCIAL LOSSES, EVEN IF THE LICENSOR HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES.
+
+6. Dispute Resolution
+
+This license shall be governed and construed in accordance with the laws of People’s Republic of China. Any dispute arising from or in connection with this License shall be submitted to Haidian District People's Court in Beijing.
+
+Note that the license is subject to update to a more comprehensive version.  For any questions related to the license and copyright, please contact us at license@zhipuai.cn.
+
+7. Llama3 and EVA-CLIP2 License
+
+For the CogVLM2 open source model based on the LLama3 series model as the base model, the Llama3 license conditions (https://llama.meta.com/llama3/license/, a copy of this repository license conditions) and the EVA-CLIP2 license conditions (MIT , https://github.com/baaivision/EVA/blob/master/LICENSE) for model weights.
+
+1. 定义
+
+“许可方”是指分发其软件的 CogVLM 模型团队。
+
+“软件”是指根据本许可提供的 CogVLM 模型参数。
+
+2. 许可授予
+
+根据本许可的条款和条件，许可方特此授予您非排他性、全球性、不可转让、不可再许可、可撤销、免版税的版权许可。
+本许可允许您免费使用本仓库中的所有开源模型进行学术研究，对于希望将模型用于商业目的的用户，需在[这里](https://open.bigmodel.cn/mla/form)完成登记。
+经过登记的用户可以免费使用本模型进行商业活动，但必须遵守本许可的所有条款和条件。
+上述版权声明和本许可声明应包含在本软件的所有副本或重要部分中。
+
+3.限制
+
+您不得出于任何军事或非法目的使用、复制、修改、合并、发布、分发、复制或创建本软件的全部或部分衍生作品。
+
+您不得利用本软件从事任何危害国家安全和国家统一、危害社会公共利益、侵犯人身权益的行为。
+
+4.免责声明
+
+本软件“按原样”提供，不提供任何明示或暗示的保证，包括但不限于对适销性、特定用途的适用性和非侵权性的保证。 在任何情况下，作者或版权持有人均不对任何索赔、损害或其他责任负责，无论是在合同诉讼、侵权行为还是其他方面，由软件或软件的使用或其他交易引起、由软件引起或与之相关 软件。
+
+5. 责任限制
+
+除适用法律禁止的范围外，在任何情况下且根据任何法律理论，无论是基于侵权行为、疏忽、合同、责任或其他原因，任何许可方均不对您承担任何直接、间接、特殊、偶然、示范性、 或间接损害，或任何其他商业损失，即使许可人已被告知此类损害的可能性。
+
+6.争议解决
+
+本许可受中华人民共和国法律管辖并按其解释。 因本许可引起的或与本许可有关的任何争议应提交北京市海淀区人民法院。
+
+请注意，许可证可能会更新到更全面的版本。 有关许可和版权的任何问题，请���过 license@zhipuai.cn 与我们联系。
+
+7. Llama3 和 EVA-CLIP2 许可
+
+针对基于以 LLama3 系列模型作为基座模型的 CogVLM2 开源模型， Llama3 许可条件 (https://llama.meta.com/llama3/license/ ，本仓库副本一份许可条件) 和 EVA-CLIP2 许可条件 (MIT, https://github.com/baaivision/EVA/blob/master/LICENSE) 适用于模型权重。

LLAMA3_LICENSE

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+META LLAMA 3 COMMUNITY LICENSE AGREEMENT
+Meta Llama 3 Version Release Date: April 18, 2024
+
+“Agreement” means the terms and conditions for use, reproduction, distribution and modification of the
+Llama Materials set forth herein.
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+
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+
+“Meta Llama 3” means the foundational large language models and software and algorithms, including
+machine-learning model code, trained model weights, inference-enabling code, training-enabling code,
+fine-tuning enabling code and other elements of the foregoing distributed by Meta at
+https://llama.meta.com/llama-downloads.
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+exclusive jurisdiction of any dispute arising out of this Agreement.

README.md

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+---
+license: other
+license_name: cogvlm2
+license_link: https://huggingface.co/THUDM/cogvlm2-llama3-chat-19B-tgi/blob/main/LICENSE
+ 
+language:
+- en
+pipeline_tag: text-generation
+tags:
+- chat
+- cogvlm2
+
+inference: false
+---
+
+# CogVLM2 
+
+<div align="center">
+<img src=https://raw.githubusercontent.com/THUDM/CogVLM2/60907317370f38b73c5ef89c36dfea4d6d4c6b88/resources/logo.svg width="40%"/>
+</div>
+<p align="center">
+   👋 Join us on <a href="https://github.com/THUDM/CogVLM2/blob/main/resources/WECHAT.md" target="_blank">WeChat</a>
+</p>
+<p align="center">
+📍Experience the larger-scale CogVLM model on the <a href="https://open.bigmodel.cn/dev/api#super-humanoid">ZhipuAI Open Platform</a>.
+</p>
+
+
+## Model introduction
+
+We launch a new generation of **CogVLM2** series of models and open source two models built with [Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct). Compared with the previous generation of CogVLM open source models.
+
+**This is a [TGI](https://huggingface.co/docs/text-generation-inference/en/index) format model.**
+## Quick Start
+
+here is a simple example of how to use the model to chat with the CogVLM2 TGI model.
+```python
+import requests
+import json
+import base64
+import os
+
+
+requests.packages.urllib3.disable_warnings()
+BAD_RESPONSE = "<error></error>"
+def image_to_base64(image_path):
+    with open(image_path, "rb") as image_file:
+        encoded_string = base64.b64encode(image_file.read())
+        return encoded_string.decode('utf-8')
+
+def history_to_prompt(query):
+    answer_format = 'Answer:'
+    prompt = ''
+    prompt += 'Question: {} {}'.format(query, answer_format)
+    return prompt
+
+def get_response(image_path, question):
+    image_extension = os.path.splitext(image_path)[1][1:]
+    
+    base64_img = image_to_base64(image_path)
+    
+    url = 'http://127.0.0.1:8080'
+    headers = {
+        'Content-Type': 'application/json',
+    }
+
+    prompt = history_to_prompt(question)
+    payload = {
+        "inputs": f"![](data:image/{image_extension};base64,{base64_img}){prompt}",
+        "stream": False,
+        "parameters": {
+            "best_of": 1,
+            "decoder_input_details": False,
+            "details": False,
+            "repetition_penalty": 1.1,
+            "do_sample": True,
+            "max_new_tokens": 1000,
+            "return_full_text": False,
+            "temperature": 0.8,
+            "top_p": 0.4,
+            "top_k": 1
+        }
+    }
+    try_times = 0
+    while try_times < 3:
+        try_times += 1
+        try:
+            response = requests.post(url, headers=headers, stream=False, data=json.dumps(payload), verify=False)
+            if response.status_code == 200:
+                try:
+                    output = response.json()[0]["generated_text"].strip()
+                    return output
+                except Exception as e:
+                    pass
+            else:
+                print(f"Received bad status code: {response.status_code}")
+        except requests.exceptions.ConnectionError as errc:
+            print("Error Connecting:", errc)
+        except requests.exceptions.Timeout as errt:
+            print("Timeout Error:", errt)
+        except requests.exceptions.RequestException as err:
+            print("Something Else:", err)
+    return BAD_RESPONSE
+if __name__ == "__main__":
+    from glob import glob
+    files = glob("demo.jpeg")
+    for file in files:
+        print(file)
+        print(get_response(
+            image_path=file,
+            question="who is this",
+        ))
+```
+
+
+## License
+
+This model is released under the CogVLM2 [LICENSE](./LICENSE). For models built with Meta Llama 3, please also adhere to the [LLAMA3_LICENSE](./LLAMA3_LICENSE).
+
+## Citation
+
+If you find our work helpful, please consider citing the following papers
+
+```
+@misc{wang2023cogvlm,
+      title={CogVLM: Visual Expert for Pretrained Language Models}, 
+      author={Weihan Wang and Qingsong Lv and Wenmeng Yu and Wenyi Hong and Ji Qi and Yan Wang and Junhui Ji and Zhuoyi Yang and Lei Zhao and Xixuan Song and Jiazheng Xu and Bin Xu and Juanzi Li and Yuxiao Dong and Ming Ding and Jie Tang},
+      year={2023},
+      eprint={2311.03079},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
+}
+```
+

config.json

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+{
+  "architectures": [
+    "CogVLMForCausalLM"
+  ],
+  "auto_map": {
+    "AutoConfig": "configuration_cogvlm.Cogvlm2"
+  },
+  "model_type": "cogvlm2",
+  "vision_config": {
+    "dropout_prob": 0.0,
+    "hidden_act": "gelu",
+    "in_channels": 3,
+    "num_hidden_layers": 63,
+    "hidden_size": 1792,
+    "patch_size": 14,
+    "num_heads": 16,
+    "intermediate_size": 15360,
+    "layer_norm_eps": 1e-06,
+    "num_positions": 9217,
+    "image_size": 1344,
+    "model_type": "eva2clip_vision_model"
+  },
+  "hidden_size": 4096,
+  "intermediate_size": 14336,
+  "num_attention_heads": 32,
+  "num_key_value_heads": 8,
+  "num_image_tokens": 2306,
+  "max_position_embeddings": 8192,
+  "rms_norm_eps": 1e-05,
+  "template_version": "chat",
+  "initializer_range": 0.02,
+  "bos_token_id": 128000,
+  "eos_token_id": [128001, 128009],
+  "pad_token_id": 128002,
+  "vocab_size": 128256,
+  "num_hidden_layers": 32,
+  "hidden_act": "silu",
+  "use_cache": true
+}

configuration_cogvlm.py

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+from typing import Literal
+from transformers import PretrainedConfig
+
+
+class Cogvlm2(PretrainedConfig):
+    _auto_class = "AutoConfig"
+
+    def __init__(
+            self,
+            vocab_size=128256,
+            hidden_size=4096,
+            intermediate_size=14336,
+            num_hidden_layers=32,
+            num_attention_heads=32,
+            num_multi_query_heads=8,
+            hidden_act='silu',
+            max_position_embeddings=8192,
+            initializer_range=0.02,
+            rms_norm_eps=1e-05,
+            template_version: Literal["base", "chat"] = "chat",
+            bos_token_id=128000,
+            eos_token_id=128001,
+            tie_word_embeddings=False,
+            use_cache=True,
+            **kwargs,
+    ):
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_attention_heads = num_attention_heads
+        self.num_multi_query_heads = num_multi_query_heads
+        self.max_position_embeddings = max_position_embeddings
+        self.rms_norm_eps = rms_norm_eps
+        self.initializer_range = initializer_range
+        self.vocab_size = vocab_size
+        self.num_hidden_layers = num_hidden_layers
+        self.hidden_act = hidden_act
+        self.template_version = template_version
+        self.use_cache = use_cache
+        super().__init__(
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )

generation_config.json

@@ -0,0 +1,13 @@
+{
+  "_from_model_config": true,
+  "bos_token_id": 128000,
+  "eos_token_id": [128001, 128009],
+  "pad_token_id": 128002,
+  "do_sample": true,
+  "temperature": 0.8,
+  "max_length": 4096,
+  "top_p": 0.4,
+  "top_k": 1,
+  "repetition_penalty": 1.1,
+  "transformers_version": "4.40.2"
+}

image_processing_cogvlm2.py

@@ -0,0 +1,120 @@
+# coding=utf-8
+# Copyright 2022 The HuggingFace Inc. 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.
+"""Image processor class for Idefics."""
+
+from typing import Callable, Dict, List, Optional, Union
+
+from PIL import Image
+
+
+from transformers.image_processing_utils import BaseImageProcessor, BatchFeature
+
+
+from transformers.image_utils import (
+    ImageInput
+)
+from transformers.utils import TensorType, is_torch_available
+
+from torchvision import transforms
+
+
+
+
+class Cogvlm2ImageProcessor(BaseImageProcessor):
+    r"""
+    Constructs a Idefics image processor.
+
+    Args:
+        image_size (`int`, *optional*, defaults to 224):
+            Resize to image size
+        image_mean (`float` or `List[float]`, *optional*, defaults to `IDEFICS_STANDARD_MEAN`):
+            Mean to use if normalizing the image. This is a float or list of floats the length of the number of
+            channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method. Can be
+            overridden by the `image_mean` parameter in the `preprocess` method.
+        image_std (`float` or `List[float]`, *optional*, defaults to `IDEFICS_STANDARD_STD`):
+            Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
+            number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method.
+            Can be overridden by the `image_std` parameter in the `preprocess` method.
+        image_num_channels (`int`, *optional*, defaults to 3):
+            Number of image channels.
+    """
+
+    model_input_names = ["pixel_values"]
+
+    def __init__(
+        self,
+        image_size: int = 1344,
+        image_mean: Optional[Union[float, List[float]]] = None,
+        image_std: Optional[Union[float, List[float]]] = None,
+        image_num_channels: Optional[int] = 3,
+        **kwargs,
+    ) -> None:
+        super().__init__(**kwargs)
+
+        self.image_size = image_size
+        self.image_num_channels = image_num_channels
+        self.image_mean = image_mean
+        self.image_std = image_std
+
+    def preprocess(
+        self,
+        images: ImageInput,
+        return_tensors: Optional[Union[str, TensorType]] = TensorType.PYTORCH,
+    ) -> TensorType:
+        """
+        Preprocess a batch of images.
+
+        Args:
+            images (`ImageInput`):
+                A list of images to preprocess.
+            image_size (`int`, *optional*, defaults to `self.image_size`):
+                Resize to image size
+            image_num_channels (`int`, *optional*, defaults to `self.image_num_channels`):
+                Number of image channels.
+            image_mean (`float` or `List[float]`, *optional*, defaults to `IDEFICS_STANDARD_MEAN`):
+                Mean to use if normalizing the image. This is a float or list of floats the length of the number of
+                channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method. Can
+                be overridden by the `image_mean` parameter in the `preprocess` method.
+            image_std (`float` or `List[float]`, *optional*, defaults to `IDEFICS_STANDARD_STD`):
+                Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
+                number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess`
+                method. Can be overridden by the `image_std` parameter in the `preprocess` method.
+            transform (`Callable`, *optional*, defaults to `None`):
+                A custom transform function that accepts a single image can be passed for training. For example,
+                `torchvision.Compose` can be used to compose multiple transforms. If `None` - an inference mode is
+                assumed - and then a preset of inference-specific transforms will be applied to the images
+
+        Returns:
+            a PyTorch tensor of the processed images
+
+        """
+        
+        # For training a user needs to pass their own set of transforms as a Callable.
+        # For reference this is what was used in the original IDEFICS training:
+        transform = transforms.Compose(
+                [
+                    transforms.Resize(
+                        (self.image_size, self.image_size), interpolation=transforms.InterpolationMode.BICUBIC
+                    ),
+                    transforms.ToTensor(),
+                    transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)),
+                ]
+            )
+
+        images = transform(images).unsqueeze(0)
+
+        images = BatchFeature(data={"pixel_values": images}, tensor_type=return_tensors)
+
+        return images

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+    "image_size": 1344,
+    "auto_map": {
+        "AutoImageProcessor": "image_processing_cogvlm2.Cogvlm2ImageProcessor"
+      }
+    
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128251": {
+      "content": "<|reserved_special_token_246|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128252": {
+      "content": "<|reserved_special_token_247|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128253": {
+      "content": "<|reserved_special_token_248|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128254": {
+      "content": "<|reserved_special_token_249|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128255": {
+      "content": "<|reserved_special_token_250|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<|begin_of_text|>",
+  "chat_template": "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}{% if add_generation_prompt %}{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}{% else %}{{ eos_token }}{% endif %}",
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "<|end_of_text|>",
+  "model_input_names": [
+    "input_ids",
+    "attention_mask"
+  ],
+  "model_max_length": 1000000000000000019884624838656,
+  "tokenizer_class": "PreTrainedTokenizerFast"
+}