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README.md

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+---
+license: apache-2.0
+language:
+- en
+pipeline_tag: image-text-to-text
+---
+
+# Cerule - A <span style="color: #4285F4;">Tiny</span> <span style="color: #DB4437;">Mighty</span> <span style="color: #F4B400;">Vision</span> <span style="color: #0F9D58;">Model</span>
+### Based on Google's - <span style="color: #D56c76;">Gemma-2b + SigLIP</span>
+
+
+
+```
+
+
+ ██████╗███████╗██████╗ ██╗   ██╗██╗     ███████╗
+██╔════╝██╔════╝██╔══██╗██║   ██║██║     ██╔════╝
+██║     █████╗  ██████╔╝██║   ██║██║     █████╗  
+██║     ██╔══╝  ██╔══██╗██║   ██║██║     ██╔══╝  
+╚██████╗███████╗██║  ██║╚██████╔╝███████╗███████╗
+ ╚═════╝╚══════╝╚═╝  ╚═╝ ╚═════╝ ╚══════╝╚══════╝
+                                                 
+
+
+                                                                
+                                                                
+ ```                                                               
+
+
+                        
+
+
+We train and release "Cerule", a tiny yet powerful Vision Lanuage Model based on the newly released Google's [Gemma-2b](https://huggingface.co/google/gemma-2b) and Google's [SigLIP](https://huggingface.co/google/siglip-so400m-patch14-384). 
+
+We utilise highly efficient data selection techniques with:
+```
+- Pretraining stage : 650K images (A LAION 2M Subset)
+- Finetuning stage : 695K images (SVIT-mix-665K modified for finetuning(Dataset SOON!))
+```
+The training setup was `4xA100's 80GB` and took ~6 hours to pretrain and ~13 hours to finetune. We modify and adapt the training code from [LLaVA](https://github.com/haotian-liu/LLaVA). 
+
+🚨 Training code, Data and more details to release soon!
+
+
+---
+| Image | Example |
+|-------|---------|
+| ![astronaut](examples/astronaut.png) | **Describe the image**<br>The image is a playful and surreal depiction of a man in a space suit, sitting on a chair and holding a green beer bottle. The man is wearing a white space suit, complete with a helmet and gloves. His feet are clad in black and white shoes, and he is placed on a sandy surface. The background features a large, blue planet, with a moon and a star visible in the sky. |
+| ![mario](examples/mario.png) | **Who are the characters in the image?**<br>The image features three characters, two of them are Mario and Luigi, and the third one is Yoshi.<br><br>**Describe the actions of the characters**<br>The Mario and Luigi characters are holding their arms out, as if they are waving. Yoshi is standing on its own, with its arms folded. |
+| ![extreme_ironing](examples/extreme_ironing.jpg) | **What's funny about this image?**<br>The image is quite humorous as it depicts a man ironing clothes on the back of a yellow taxi cab. This is not a typical sight you'd expect to see in everyday life. |
+---
+
+
+## Training and Inference:
+We will release the training code in some time.
+
+### Inference:
+**Please note that running the inference code at this stage may result in errors**. The proper code for training and inference shall be released soon!
+Before running the snippet, you need to install the following dependencies:
+
+```shell
+pip install torch transformers accelerate pillow
+```
+
+```python
+import torch
+import transformers
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from PIL import Image
+import warnings
+
+transformers.logging.set_verbosity_error()
+transformers.logging.disable_progress_bar()
+warnings.filterwarnings('ignore')
+
+torch.set_default_device('cuda')  # or 'cpu'
+
+model = AutoModelForCausalLM.from_pretrained(
+    'Tensoic/Cerule',
+    torch_dtype=torch.float16,
+    device_map='auto',
+    trust_remote_code=True)
+tokenizer = AutoTokenizer.from_pretrained(
+    'Tensoic/Cerule',
+    trust_remote_code=True)
+
+# text prompt
+prompt = 'Who are these charecters?'
+text = f"A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: <image>\n{prompt} ASSISTANT:"
+text_chunks = [tokenizer(chunk).input_ids for chunk in text.split('<image>')]
+input_ids = torch.tensor(text_chunks[0] + [-200] + text_chunks[1], dtype=torch.long).unsqueeze(0)
+
+image = Image.open('examples/mario.png')
+image_tensor = model.process_images([image], model.config).to(dtype=model.dtype)
+
+# generate
+output_ids = model.generate(
+    input_ids,
+    images=image_tensor,
+    max_new_tokens=100,
+    use_cache=False)[0] #keep use_cache=False or else it might run into some torch dim error
+
+print(tokenizer.decode(output_ids[input_ids.shape[1]:], skip_special_tokens=False).strip())
+```
+
+## License
+Apache 2.0? Maybe... idk

__init__.py

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+from .configuration_cerule_gemma import CeruleGemmaConfig
+from .modeling_cerule_gemma import CeruleGemmaForCausalLM
+
+from transformers import AutoConfig, AutoModelForCausalLM
+
+AutoConfig.register("cerule-gemma", CeruleGemmaConfig)
+AutoModelForCausalLM.register(CeruleGemmaConfig, CeruleGemmaForCausalLM)

config.json

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+{
+  "_name_or_path": "Tensoic/Cerule",
+  "architectures": [
+    "CeruleGemmaForCausalLM"
+  ],
+  "auto_map": {
+    "AutoConfig": "configuration_gemma.CeruleGemmaConfig",
+    "AutoModelForCausalLM": "modeling_cerule_gemma.CeruleGemmaForCausalLM"
+  },
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 2,
+  "eos_token_id": 1,
+  "freeze_mm_mlp_adapter": false,
+  "head_dim": 256,
+  "hidden_act": "gelu",
+  "hidden_size": 2048,
+  "image_aspect_ratio": "pad",
+  "initializer_range": 0.02,
+  "intermediate_size": 16384,
+  "max_position_embeddings": 8192,
+  "mm_hidden_size": 1152,
+  "mm_projector_lr": null,
+  "mm_projector_type": "mlp2x_gelu",
+  "mm_vision_tower": "google/siglip-so400m-patch14-384",
+  "model_type": "cerule-gemma",
+  "num_attention_heads": 8,
+  "num_hidden_layers": 18,
+  "num_key_value_heads": 1,
+  "pad_token_id": 0,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 10000.0,
+  "tokenizer_model_max_length": 2048,
+  "tokenizer_padding_side": "right",
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.39.0.dev0",
+  "tune_mm_mlp_adapter": false,
+  "use_cache": true,
+  "use_mm_proj": true,
+  "vocab_size": 256000
+}

configuration_gemma.py

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+# coding=utf-8
+# Copyright 2023 Microsoft and 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.
+
+""" Gemma model configuration"""
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+logger = logging.get_logger(__name__)
+
+GEMMA_PRETRAINED_CONFIG_ARCHIVE_MAP = {
+    "google/gemma-2b": "https://huggingface.co/google/gemma-2b/resolve/main/config.json",
+}
+
+
+class GemmaConfig(PretrainedConfig):
+    model_type = "gemma"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+            self,
+            vocab_size=51200,
+            hidden_size=2048,
+            intermediate_size=8192,
+            num_hidden_layers=24,
+            num_attention_heads=32,
+            num_key_value_heads=None,
+            resid_pdrop=0.0,
+            embd_pdrop=0.0,
+            attention_dropout=0.0,
+            hidden_act="gelu_new",
+            max_position_embeddings=2048,
+            initializer_range=0.02,
+            layer_norm_eps=1e-5,
+            use_cache=True,
+            tie_word_embeddings=False,
+            rope_theta=10000.0,
+            rope_scaling=None,
+            partial_rotary_factor=0.5,
+            qk_layernorm=False,
+            bos_token_id=1,
+            eos_token_id=2,
+            **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+
+        self.num_key_value_heads = num_key_value_heads
+        self.resid_pdrop = resid_pdrop
+        self.embd_pdrop = embd_pdrop
+        self.attention_dropout = attention_dropout
+        self.hidden_act = hidden_act
+        self.max_position_embeddings = max_position_embeddings
+        self.initializer_range = initializer_range
+        self.layer_norm_eps = layer_norm_eps
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
+        self.partial_rotary_factor = partial_rotary_factor
+        self.qk_layernorm = qk_layernorm
+        self._rope_scaling_validation()
+
+        super().__init__(
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
+        
+    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}")
+
+
+from typing import Union
+from transformers import PretrainedConfig
+import os
+
+
+class SigLipVisionConfig(PretrainedConfig):
+    model_type = "siglip_vision_model"
+
+    def __init__(
+            self,
+            hidden_size=1152,
+            image_mean=(0.5, 0.5, 0.5),
+            intermediate_size=4304,
+            num_hidden_layers=27,
+            num_attention_heads=16,
+            num_channels=3,
+            image_size=384,
+            patch_size=14,
+            hidden_act="gelu_pytorch_tanh",
+            layer_norm_eps=1e-6,
+            attention_dropout=0.0,
+            **kwargs,
+    ):
+        super().__init__(**kwargs)
+
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.num_channels = num_channels
+        self.patch_size = patch_size
+        self.image_size = image_size
+        self.attention_dropout = attention_dropout
+        self.layer_norm_eps = layer_norm_eps
+        self.hidden_act = hidden_act
+        self.image_mean = image_mean
+
+    @classmethod
+    def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
+        cls._set_token_in_kwargs(kwargs)
+
+        config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
+
+        # get the vision config dict if we are loading from SigLipConfig
+        if config_dict.get("model_type") == "siglip":
+            config_dict = config_dict["vision_config"]
+
+        if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
+            logger.warning(
+                f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
+                f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
+            )
+
+        return cls.from_dict(config_dict, **kwargs)
+
+
+class CeruleGemmaConfig(GemmaConfig):
+    model_type = "cerule-gemma"
+    
+    def __init__(self, **kwargs):
+        self.gemma_config = GemmaConfig(**kwargs)
+        super().__init__(**kwargs)

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 2,
+  "eos_token_id": 1,
+  "pad_token_id": 0,
+  "transformers_version": "4.39.0.dev0"
+}

model-00001-of-00002.safetensors

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special_tokens_map.json

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tokenizer_config.json

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