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@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+asagi-1.9B-F16.gguf filter=lfs diff=lfs merge=lfs -text
+mmproj-model-f16.gguf filter=lfs diff=lfs merge=lfs -text

LICENSE

@@ -0,0 +1,201 @@
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README.md

@@ -1,3 +1,210 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+language:
+- ja
+base_model:
+- llm-jp/llm-jp-3-1.8b-instruct
+pipeline_tag: image-text-to-text
+library_name: transformers
+tags:
+- llava
+---
+
+## Model Details
+
+### Model Description
+
+This repository provides Asagi-2B, a large-scale Japanese Vision & Language Model (VLM).
+Asagi-2B has been trained on an extensive Japanese dataset, incorporating a diverse range of data sources.
+
+A significant portion of the training data is synthesized using models such as the Japanese large language model ([CALM3-22B-Chat](https://huggingface.co/cyberagent/calm3-22b-chat)) and the English Vision & Language Model ([Phi3.5-vision-instruct](https://huggingface.co/microsoft/Phi-3.5-vision-instruct)).
+
+Importantly, we do not use LLMs that restrict the usage of their outputs in the license terms (e.g., GPT-4) to synthesize the training data.
+
+
+|Model components|Model / Architecture|Parameters|
+|:---:|:---:|:---:|
+|Vision encoder|[siglip-so400m-patch14-384](https://huggingface.co/google/siglip-so400m-patch14-384)|428M|
+|Projector|2-layer MLP|64M|
+|LLM|[llm-jp-3-1.8b-instruct](https://huggingface.co/llm-jp/llm-jp-3-1.8b-instruct)|1.8B|
+
+
+## Usage
+
+### Requirements
+
+```txt
+transformers==4.45.1
+accelerate==0.34.2
+torch==2.4.0
+torchvision==0.19.0
+```
+
+### How to use
+```python
+import requests
+import torch
+import transformers
+from PIL import Image
+from transformers import AutoModel, AutoProcessor, GenerationConfig
+
+transformers.set_seed(42)
+model_path = "MIL-UT/Asagi-2B"
+processor = AutoProcessor.from_pretrained(model_path)
+model = AutoModel.from_pretrained(
+    model_path, trust_remote_code=True,
+    torch_dtype=torch.bfloat16,
+    device_map="auto"
+)
+
+generation_config = GenerationConfig(
+    do_sample=True,
+    num_beams=5,
+    max_new_tokens=256,
+    temperature=0.7,
+    repetition_penalty=1.5
+)
+
+prompt = ("以下は、タスクを説明する指示です。要求を適切に満たす応答を書きなさい。\n\n"
+            "### 指示:\n<image>\nこの画像を見て、次の質問に詳細かつ具体的に答えてください。この写真はどこで撮影されたものか教えてください。また、画像の内容についても詳しく説明してください。\n\n### 応答:\n")
+
+# sample image
+sample_image_url = "https://raw.githubusercontent.com/uehara-mech/uehara-mech.github.io/refs/heads/master/images/shibuya.jpg"
+image = Image.open(requests.get(sample_image_url, stream=True).raw)
+
+inputs = processor(
+    text=prompt, images=image, return_tensors="pt"
+)
+inputs_text = processor.tokenizer(prompt, return_tensors="pt")
+inputs['input_ids'] = inputs_text['input_ids']
+inputs['attention_mask'] = inputs_text['attention_mask']
+for k, v in inputs.items():
+    if v.dtype == torch.float32:
+        inputs[k] = v.to(model.dtype)
+inputs = {k: inputs[k].to(model.device) for k in inputs if k != "token_type_ids"}
+
+generate_ids = model.generate(
+    **inputs,
+    generation_config=generation_config
+)
+generated_text = processor.batch_decode(
+    generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False
+)[0]
+
+# do not print the prompt
+if "<image>" in prompt:
+    prompt = prompt.replace("<image>", " ")
+generated_text = generated_text.replace(prompt, "")
+
+print(f"Generated text: {generated_text}")
+
+# >>> Generated text:  この写真は渋谷の交差点で撮影されたものです。写真には、夜の渋谷の街並みが写っており、多くの高層ビルが立ち並んでいます。特に目を引くのは、中央に位置する大きな白いビルで、その周囲には小さなビルが立ち並んでいます。ビルの窓からは街の明かりが漏れ、街全体が活気に満ちている様子が伝わってきます。
+
+```
+
+### Example
+<img src="https://raw.githubusercontent.com/uehara-mech/uehara-mech.github.io/refs/heads/master/images/shibuya.jpg" width="350">
+
+| Prompt | Output |
+|:---|:---|
+| この画像を見て、次の質問に詳細かつ具体的に答えてください。この写真はどこで撮影されたものか教えてください。また、画像の内容についても詳しく説明してください。 |この写真は渋谷の交差点で撮影されたものです。写真には、夜の渋谷の街並みが写っており、多くの高層ビルが立ち並んでいます。特に目を引くのは、中央に位置する大きな白いビルで、その周囲には小さなビルが立ち並んでいます。ビルの窓からは街の明かりが漏れ、街全体が活気に満ちている様子が伝わってきます。|
+
+
+### More Examples
+<details>
+<summary>Click to expand</summary>
+<img src="https://raw.githubusercontent.com/uehara-mech/uehara-mech.github.io/refs/heads/master/images/bridge.jpg" width="300">
+
+| Prompt | Output |
+|:---|:---|
+| この画像を見て、次の指示に詳細かつ具体的に答えてください。この写真の内容について詳しく教えてく��さい。 |この写真は、東京のレインボーブリッジを撮影したものです。橋は白く塗装されており、水の上に架かっています。背景には高層ビルが立ち並ぶ都市景観が広がっており、空は澄み切った青色で、白い雲が浮かんでいます。|
+
+<img src="https://raw.githubusercontent.com/uehara-mech/uehara-mech.github.io/refs/heads/master/images/tower.jpg" width="300">
+
+| Prompt | Output |
+|:---|:---|
+| この画像を見て、次の質問に詳細かつ具体的に答えてください。この写真について評価してください。 |この写真は非常に低い解像度と品質のため、詳細な説明が困難です。|
+
+<img src="https://raw.githubusercontent.com/uehara-mech/uehara-mech.github.io/refs/heads/master/images/shrine.jpg" width="300">
+
+| Prompt | Output |
+|:---|:---|
+| この画像を見て、次の質問に詳細かつ具体的に答えてください。この写真の神社について、細かいところまで詳しく説明してください。 |この写真の神社は、日本の伝統的な神社建築様式を反映しています。白い壁と赤い屋根のコントラストが美しく、神聖な雰囲気を醸し出しています。入り口には朱塗りの鳥居が立ち、神聖な空間への入り口を象徴しています。また、神社の前には石畳の道が続いており、参拝者を神聖な場所へと導いています。|
+
+
+<img src="https://raw.githubusercontent.com/uehara-mech/uehara-mech.github.io/refs/heads/master/images/garden.jpg" width="300">
+
+| Prompt | Output |
+|:---|:---|
+| この画像を見て、次の指示に詳細かつ具体的に答えてください。これは日本庭園の中でも、どのような形式に分類される庭園ですか？また、その理由は何ですか？ |この日本庭園は、伝統的な日本庭園の形式に基づいており、自然との調和を重視した設計が特徴です。池や岩、石灯籠などが配置され、静かで落ち着いた雰囲気を醸し出しています。このような庭園は、日本の伝統的な美意識である「わびさび」を体現しており、自然と人間の調和を大切にする精神を体現しています。|
+
+<img src="https://raw.githubusercontent.com/uehara-mech/uehara-mech.github.io/refs/heads/master/images/slope.jpg" width="200">
+
+| Prompt | Output |
+|:---|:---|
+| この画像を見て、次の質問に詳細に答えてください。この画像の場所を舞台とした小説のあらすじを書いてください。 |この画像の場所を舞台にした小説は、日本の伝統的な街並みを舞台にしています。石畳の道を進むと、古い木造建築が立ち並び、歴史を感じさせる風景が広がっています。石畳の道沿いには、緑豊かな植物が生い茂り、自然との調和を大切にする日本の美意識が感じられます。この物語は、都会の喧騒を離れ、自然と一体になれるような、静かで穏やかな雰囲気を醸し出しています。|
+
+</details>
+
+
+## Training Details
+
+### Training Data
+
+| Dataset               | Preprocess     | Stage1 | Stage2 | Size      |
+|:--------------------------|:-------------:|:-------:|:------:|:---------:|
+| ROIS (Ours)                | Synthesized    | ✓      | ✓      | 8.4M |
+| Japanese image text pairs  | Synthesized    | ✓      | ✓      | 4.4M |
+| Wikipedia                  | Synthesized    | ✓      | ✓      | 2.5M |
+| Open Images                | Translated        | ✓      | ✓      | 680K   |
+| DCI                        | Translated        | ✓      | ✓      | 7K     |
+| CommonCatalog CC-BY        | Translated        | ✓      | ✓      | 3.5M |
+| LLaVA-Pretrain-JA          |             | ✓      | ✓      | 550K   |
+| STAIR Captions             |             | ✓      | ✓      | 410K   |
+| Flickr-JP                  |             | ✓      | ✓      | 160K   |
+| YJ Captions                |             | ✓      | ✓      | 130K   |
+| Japanese Pascal            |             | ✓      | ✓      | 5K     |
+| ArtBench                   | Synthesized    |       | ✓      | 100K   |
+| GQA                        | Translated        |       | ✓      | 1.9M |
+| VQA v2                     | Translated        |       | ✓      | 880K   |
+| A-OKVQA                    | Translated        |       | ✓      | 34K    |
+| OK-VQA                     | Translated        |       | ✓      | 18K    |
+| Japanese Visual Genome     | Translated        |       | ✓      | 1.6M |
+| PangeaInstruct             |             |       | ✓      | 93K    |
+
+Note: ROIS (Ours) is a newly collected dataset crawled from the web specifically for this project.
+The dataset consists of image and raw text pairs, which are used to synthesize the training data.
+
+## Evaluation
+
+We evaluated our model using Heron-Bench, JA-VLM-Bench-in-the-Wild, and JA-VG-VQA-500.
+We used eval-mm library for this evaluation.
+
+Here, models with "†" are not trained with GPT-generated data.
+Bold numbers indicate the best performance among all models, and underlined numbers indicate the best performance among models not trained with GPT-generated data.
+
+
+| Model                          | LM Size | Heron-Bench (LLM (%)) | JA-VLM-Bench-In-the-Wild (ROUGE-L) | JA-VLM-Bench-In-the-Wild (LLM (/5.0)) | JA-VG-VQA-500 (ROUGE-L) | JA-VG-VQA-500 (LLM (/5.0)) |
+|:-------------------------------|:--------:|:--------------------:|:---------------------------------:|:-----------------------------------:|:---------------------:|:-----------------------:|
+| Japanese InstructBLIP Alpha†   | 7B  | 14.0 | 20.8 | 2.42 | - | - |
+| Japanese Stable VLM†           | 7B  | 24.2 | 23.3 | 2.47 | - | - |
+| LLaVA-CALM2-SigLIP†            | 7B  | 43.3 | 47.2 | 3.15 | 17.4 | 3.21 |
+| Llama-3-EvoVLM-JP-v2           | 8B  | 39.3 | 41.4 | 2.92 | 23.5 | 2.96 |
+| VILA-jp                        | 13B |**57.2**|**52.3**| **3.69**| 16.2 | 3.62 |
+| Asagi-2B†                      | 1.8B | 44.7 | 48.8 | 3.26 | 53.7 | 3.69 |
+| Asagi-4B†                      | 3.7B | 49.3 | 49.6 | 3.38 | 55.6 | 3.78 |
+| Asagi-8B†                      | 7.2B | 54.7 | 49.4 | <u>3.45</u> | 56.43 | <u>**3.84**</u> |
+| Asagi-14B†                     | 13B | <u>55.8</u> | <u>50.8</u> | 3.44 | <u>**56.8**</u> | <u>**3.84**</u> |
+| GPT-4o                         | - | 87.6 | 37.6 | 3.85 | 12.1 | 3.58 |
+
+
+
+
+## Risks and Limitations
+
+The models released here are in the early stages of our research and development and have not been tuned to ensure outputs align with human intent and safety considerations.
+
+
+## Model Card Authors
+
+Kohei Uehara

asagi-1.9B-F16.gguf

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config.json

@@ -0,0 +1,52 @@
+{
+  "_name_or_path": "",
+  "architectures": [
+    "LlavaForConditionalGeneration"
+  ],
+  "auto_map": {
+    "AutoConfig": "configuration_llava.LlavaConfig",
+    "AutoModel": "modeling_llava.LlavaForConditionalGeneration"
+  },
+  "ignore_index": -100,
+  "image_seq_length": 576,
+  "image_token_index": 99574,
+  "model_type": "llava",
+  "pad_token_id": 99575,
+  "projector_hidden_act": "gelu",
+  "projector_hidden_size": 4096,
+  "text_config": {
+    "_name_or_path": "llm-jp/llm-jp-3-1.8b-instruct",
+    "architectures": [
+      "LlamaForCausalLM"
+    ],
+    "hidden_size": 2048,
+    "intermediate_size": 7168,
+    "max_position_embeddings": 4096,
+    "model_type": "llama",
+    "num_attention_heads": 16,
+    "num_hidden_layers": 24,
+    "num_key_value_heads": 16,
+    "rms_norm_eps": 1e-05,
+    "torch_dtype": "bfloat16",
+    "vocab_size": 99584
+  },
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.45.2",
+  "vision_config": {
+    "hidden_act": "gelu_pytorch_tanh",
+    "hidden_size": 1152,
+    "image_size": 384,
+    "intermediate_size": 4304,
+    "layer_norm_eps": 1e-06,
+    "model_type": "siglip_vision_model",
+    "num_attention_heads": 16,
+    "num_hidden_layers": 27,
+    "patch_size": 14,
+    "projection_dim": 768,
+    "vocab_size": 99584
+  },
+  "vision_feature_layer": -2,
+  "vision_feature_select_strategy": "default",
+  "vocab_size": 99584
+}

configuration_llava.py

@@ -0,0 +1,133 @@
+# coding=utf-8
+# Copyright 2023 Microsoft Research & University of Wisconsin-Madison 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.
+"""Llava model configuration"""
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.models.auto import CONFIG_MAPPING
+from transformers.utils import logging
+
+logger = logging.get_logger(__name__)
+
+
+class LlavaConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`LlavaForConditionalGeneration`].
+    It is used to instantiate an
+    Llava model according to the specified arguments, defining the model architecture. Instantiating a configuration
+    with the defaults will yield a similar configuration to that of the Llava-9B.
+
+    e.g. [llava-hf/llava-9b](https://huggingface.co/llava-hf/llava-9b)
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+    Args:
+        vision_config (`Union[AutoConfig, dict]`,  *optional*, defaults to `CLIPVisionConfig`):
+            The config object or dictionary of the vision backbone.
+        text_config (`Union[AutoConfig, dict]`, *optional*, defaults to `LlamaConfig`):
+            The config object or dictionary of the text backbone.
+        ignore_index (`int`, *optional*, defaults to -100):
+            The ignore index for the loss function.
+        image_token_index (`int`, *optional*, defaults to 32000):
+            The image token index to encode the image prompt.
+        projector_hidden_act (`str`, *optional*, defaults to `"gelu"`):
+            The activation function used by the multimodal projector.
+        vision_feature_select_strategy (`str`, *optional*, defaults to `"default"`):
+            The feature selection strategy used to select the vision feature from the vision backbone.
+            Can be one of `"default"` or `"full"`.
+        vision_feature_layer (`int`, *optional*, defaults to -2):
+            The index of the layer to select the vision feature.
+
+    Example:
+
+    ```python
+    >>> from transformers import LlavaForConditionalGeneration, LlavaConfig, CLIPVisionConfig, LlamaConfig
+
+    >>> # Initializing a CLIP-vision config
+    >>> vision_config = CLIPVisionConfig()
+
+    >>> # Initializing a Llama config
+    >>> text_config = LlamaConfig()
+
+    >>> # Initializing a Llava llava-1.5-7b style configuration
+    >>> configuration = LlavaConfig(vision_config, text_config)
+
+    >>> # Initializing a model from the llava-1.5-7b style configuration
+    >>> model = LlavaForConditionalGeneration(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "llava"
+    is_composition = False
+
+    def __init__(
+        self,
+        vision_config=None,
+        text_config=None,
+        ignore_index=-100,
+        image_token_index=32000,
+        projector_hidden_act="gelu",
+        vision_feature_select_strategy="default",
+        vision_feature_layer=-2,
+        projector_hidden_size=None,
+        **kwargs,
+    ):
+        self.ignore_index = ignore_index
+        self.image_token_index = image_token_index
+        self.projector_hidden_act = projector_hidden_act
+
+        if vision_feature_select_strategy not in ["default", "full"]:
+            raise ValueError(
+                "vision_feature_select_strategy should be one of 'default', 'full'."
+                f"Got: {vision_feature_select_strategy}"
+            )
+
+        self.vision_feature_select_strategy = vision_feature_select_strategy
+        self.vision_feature_layer = vision_feature_layer
+
+        if isinstance(vision_config, dict):
+            vision_config["model_type"] = (
+                vision_config["model_type"] if "model_type" in vision_config else "clip_vision_model"
+            )
+            vision_config = CONFIG_MAPPING[vision_config["model_type"]](**vision_config)
+        elif vision_config is None:
+            vision_config = CONFIG_MAPPING["clip_vision_model"](
+                intermediate_size=4096,
+                hidden_size=1024,
+                patch_size=14,
+                image_size=336,
+                num_hidden_layers=24,
+                num_attention_heads=16,
+                vocab_size=32000,
+                projection_dim=768,
+            )
+
+        self.vision_config = vision_config
+
+        if isinstance(text_config, dict):
+            text_config["model_type"] = text_config["model_type"] if "model_type" in text_config else "llama"
+            text_config = CONFIG_MAPPING[text_config["model_type"]](**text_config)
+        elif text_config is None:
+            text_config = CONFIG_MAPPING["llama"]()
+
+        self.text_config = text_config
+
+        if projector_hidden_size is None:
+            projector_hidden_size = text_config.hidden_size
+        self.projector_hidden_size = projector_hidden_size
+
+        super().__init__(**kwargs)

generation_config.json

@@ -0,0 +1,7 @@
+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "pad_token_id": 99575,
+  "transformers_version": "4.45.2"
+}

mmproj-model-f16.gguf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:eddf029d95058851c07b04d676d0b77038726033fde1a64ff58a5a9f50c85cac
+size 860986368

modeling_llava.py

@@ -0,0 +1,585 @@
+# coding=utf-8
+# Copyright 2023 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.
+"""PyTorch Llava model."""
+
+from dataclasses import dataclass
+from typing import List, Optional, Tuple, Union
+
+import torch
+import torch.utils.checkpoint
+from torch import nn
+from transformers import PreTrainedModel
+from transformers.activations import ACT2FN
+from transformers.cache_utils import Cache
+from transformers.modeling_outputs import ModelOutput
+from transformers.models.auto import AutoModel, AutoModelForCausalLM
+from transformers.utils import (
+    add_start_docstrings,
+    add_start_docstrings_to_model_forward,
+    logging,
+    replace_return_docstrings,
+)
+
+from .configuration_llava import LlavaConfig
+
+logger = logging.get_logger(__name__)
+
+_CONFIG_FOR_DOC = "LlavaConfig"
+
+# Base docstring
+_CHECKPOINT_FOR_DOC = "llava-hf/llava-1.5-7b-hf"
+
+
+@dataclass
+# Copied from transformers.models.idefics.modeling_idefics.IdeficsCausalLMOutputWithPast with Idefics->Llava
+class LlavaCausalLMOutputWithPast(ModelOutput):
+    """
+    Base class for Llava causal language model (or autoregressive) outputs.
+
+    Args:
+        loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
+            Language modeling loss (for next-token prediction).
+        logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
+            Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
+        past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed
+            or when `config.use_cache=True`):
+            Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
+            `(batch_size, num_heads, sequence_length, embed_size_per_head)`)
+
+            Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
+            `past_key_values` input) to speed up sequential decoding.
+        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed
+            or when `config.output_hidden_states=True`):
+            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
+            one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
+
+            Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
+        attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed
+            or when `config.output_attentions=True`):
+            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
+            sequence_length)`.
+
+            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
+            heads.
+        image_hidden_states (`tuple(torch.FloatTensor)`, *optional*):
+            Tuple of `torch.FloatTensor` (one for the output of the image embeddings, `(batch_size, num_images,
+            sequence_length, hidden_size)`.
+
+            image_hidden_states of the model produced by the vision encoder, and optionally by the perceiver
+    """
+
+    loss: Optional[torch.FloatTensor] = None
+    logits: torch.FloatTensor = None
+    past_key_values: Optional[List[torch.FloatTensor]] = None
+    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
+    attentions: Optional[Tuple[torch.FloatTensor]] = None
+    image_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
+
+
+class LlavaMultiModalProjector(nn.Module):
+    def __init__(self, config: LlavaConfig):
+        super().__init__()
+
+        self.linear_1 = nn.Linear(config.vision_config.hidden_size, config.projector_hidden_size, bias=True)
+        self.act = ACT2FN[config.projector_hidden_act]
+        self.linear_2 = nn.Linear(config.projector_hidden_size, config.text_config.hidden_size, bias=True)
+
+    def forward(self, image_features):
+        hidden_states = self.linear_1(image_features)
+        hidden_states = self.act(hidden_states)
+        hidden_states = self.linear_2(hidden_states)
+        return hidden_states
+
+
+LLAVA_START_DOCSTRING = r"""
+    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
+    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
+    etc.)
+
+    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
+    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
+    and behavior.
+
+    Parameters:
+        config ([`LlavaConfig`] or [`LlavaVisionConfig`]):
+            Model configuration class with all the parameters of the model. Initializing with a config file does not
+            load the weights associated with the model, only the configuration. Check out the
+            [`~PreTrainedModel.from_pretrained`] method to load the model weights.
+"""
+
+
+@add_start_docstrings(
+    "The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
+    LLAVA_START_DOCSTRING,
+)
+class LlavaPreTrainedModel(PreTrainedModel):
+    config_class = LlavaConfig
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["LlavaVisionAttention"]
+    _skip_keys_device_placement = "past_key_values"
+    _supports_flash_attn_2 = True
+    _supports_cache_class = True
+
+    def _init_weights(self, module):
+        # important: this ported version of Llava isn't meant for training from scratch - only
+        # inference and fine-tuning - so the proper init weights code has been removed - the original codebase
+        # https://github.com/haotian-liu/LLaVA/tree/main/llava should serve for that purpose
+        std = (
+            self.config.initializer_range
+            if hasattr(self.config, "initializer_range")
+            else self.config.text_config.initializer_range
+        )
+
+        if hasattr(module, "class_embedding"):
+            module.class_embedding.data.normal_(mean=0.0, std=std)
+
+        if isinstance(module, (nn.Linear, nn.Conv2d)):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+
+    @property
+    def _supports_sdpa(self):
+        """
+        Retrieve language_model's attribute to check whether the model supports
+        SDPA or not.
+        """
+        return self.language_model._supports_sdpa
+
+
+LLAVA_INPUTS_DOCSTRING = r"""
+    Args:
+        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
+            it.
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            [What are input IDs?](../glossary#input-ids)
+        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)):
+            The tensors corresponding to the input images. Pixel values can be obtained using
+            [`AutoImageProcessor`]. See [`CLIPImageProcessor.__call__`] for details ([]`LlavaProcessor`] uses
+            [`CLIPImageProcessor`] for processing images).
+        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+
+            [What are attention masks?](../glossary#attention-mask)
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
+            `past_key_values`).
+
+            If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
+            and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
+            information on the default strategy.
+
+            - 1 indicates the head is **not masked**,
+            - 0 indicates the head is **masked**.
+        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+            config.n_positions - 1]`. [What are position IDs?](../glossary#position-ids)
+        past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed
+            or when `config.use_cache=True`):
+            Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
+            `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
+            `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
+
+            Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
+            blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
+
+            If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
+            don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
+            `decoder_input_ids` of shape `(batch_size, sequence_length)`.
+        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
+            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
+            model's internal embedding lookup matrix.
+        vision_feature_layer (`int`, *optional*, defaults to -2):
+            The index of the layer to select the vision feature.
+        vision_feature_select_strategy (`str`, *optional*, defaults to `"default"`):
+            The feature selection strategy used to select the vision feature from the vision backbone.
+            Can be one of `"default"` or `"full"`.
+        use_cache (`bool`, *optional*):
+            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
+            `past_key_values`).
+        output_attentions (`bool`, *optional*):
+            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+            tensors for more detail.
+        output_hidden_states (`bool`, *optional*):
+            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+            more detail.
+        return_dict (`bool`, *optional*):
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+"""
+
+
+@add_start_docstrings(
+    """The LLAVA model which consists of a vision backbone and a language model.""",
+    LLAVA_START_DOCSTRING,
+)
+class LlavaForConditionalGeneration(LlavaPreTrainedModel):
+    def __init__(self, config: LlavaConfig):
+        super().__init__(config)
+        self.vision_tower = AutoModel.from_config(config.vision_config)
+
+        self.multi_modal_projector = LlavaMultiModalProjector(config)
+        self.vocab_size = config.text_config.vocab_size
+        self.language_model = AutoModelForCausalLM.from_config(
+            config.text_config, attn_implementation=config._attn_implementation
+        )
+        self.pad_token_id = self.config.pad_token_id if self.config.pad_token_id is not None else -1
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.language_model.get_input_embeddings()
+
+    def set_input_embeddings(self, value):
+        self.language_model.set_input_embeddings(value)
+
+    def get_output_embeddings(self):
+        return self.language_model.get_output_embeddings()
+
+    def set_output_embeddings(self, new_embeddings):
+        self.language_model.set_output_embeddings(new_embeddings)
+
+    def set_decoder(self, decoder):
+        self.language_model.set_decoder(decoder)
+
+    def get_decoder(self):
+        return self.language_model.get_decoder()
+
+    def tie_weights(self):
+        return self.language_model.tie_weights()
+
+    def resize_token_embeddings(self, new_num_tokens: Optional[int] = None, pad_to_multiple_of=None) -> nn.Embedding:
+        model_embeds = self.language_model.resize_token_embeddings(new_num_tokens, pad_to_multiple_of)
+        # update vocab size
+        self.config.text_config.vocab_size = model_embeds.num_embeddings
+        self.vocab_size = model_embeds.num_embeddings
+        return model_embeds
+
+    def _merge_input_ids_with_image_features(self, image_features, inputs_embeds, input_ids, attention_mask, labels):
+        num_images, num_image_patches, embed_dim = image_features.shape
+        batch_size, sequence_length = input_ids.shape
+        left_padding = not torch.sum(input_ids[:, -1] == torch.tensor(self.pad_token_id))
+        # 1. Create a mask to know where special image tokens are
+        special_image_token_mask = input_ids == self.config.image_token_index
+        num_special_image_tokens = torch.sum(special_image_token_mask, dim=-1)
+        # Compute the maximum embed dimension
+        max_embed_dim = (num_special_image_tokens.max() * (num_image_patches - 1)) + sequence_length
+        batch_indices, non_image_indices = torch.where(input_ids != self.config.image_token_index)
+
+        # 2. Compute the positions where text should be written
+        # Calculate new positions for text tokens in merged image-text sequence.
+        # `special_image_token_mask` identifies image tokens. Each image token will be replaced
+        # by `nb_text_tokens_per_images - 1` text tokens.
+        # `torch.cumsum` computes how each image token shifts subsequent text token positions.
+        # - 1 to adjust for zero-based indexing, as `cumsum` inherently increases indices by one.
+        new_token_positions = torch.cumsum((special_image_token_mask * (num_image_patches - 1) + 1), -1) - 1
+        nb_image_pad = max_embed_dim - 1 - new_token_positions[:, -1]
+        if left_padding:
+            new_token_positions += nb_image_pad[:, None]  # offset for left padding
+        text_to_overwrite = new_token_positions[batch_indices, non_image_indices]
+
+        # 3. Create the full embedding, already padded to the maximum position
+        final_embedding = torch.zeros(
+            batch_size, max_embed_dim, embed_dim, dtype=inputs_embeds.dtype, device=inputs_embeds.device
+        )
+        final_attention_mask = torch.zeros(
+            batch_size, max_embed_dim, dtype=attention_mask.dtype, device=inputs_embeds.device
+        )
+        if labels is not None:
+            final_labels = torch.full(
+                (batch_size, max_embed_dim), self.config.ignore_index, dtype=input_ids.dtype, device=input_ids.device
+            )
+        # In case the Vision model or the Language model has been offloaded to CPU, we need to manually
+        # set the corresponding tensors into their correct target device.
+        target_device = inputs_embeds.device
+        batch_indices, non_image_indices, text_to_overwrite = (
+            batch_indices.to(target_device),
+            non_image_indices.to(target_device),
+            text_to_overwrite.to(target_device),
+        )
+        attention_mask = attention_mask.to(target_device)
+
+        # 4. Fill the embeddings based on the mask. If we have ["hey" "<image>", "how", "are"]
+        # we need to index copy on [0, 577, 578, 579] for the text and [1:576] for the image features
+        final_embedding[batch_indices, text_to_overwrite] = inputs_embeds[batch_indices, non_image_indices]
+        final_attention_mask[batch_indices, text_to_overwrite] = attention_mask[batch_indices, non_image_indices]
+        if labels is not None:
+            final_labels[batch_indices, text_to_overwrite] = labels[batch_indices, non_image_indices]
+
+        # 5. Fill the embeddings corresponding to the images.
+        # Anything that is not `text_positions` needs filling (#29835)
+        image_to_overwrite = torch.full(
+            (batch_size, max_embed_dim), True, dtype=torch.bool, device=inputs_embeds.device
+        )
+        image_to_overwrite[batch_indices, text_to_overwrite] = False
+        image_to_overwrite &= image_to_overwrite.cumsum(-1) - 1 >= nb_image_pad[:, None].to(target_device)
+
+        if image_to_overwrite.sum() != image_features.shape[:-1].numel():
+            raise ValueError(
+                f"The input provided to the model are wrong. "
+                f"The number of image tokens is {torch.sum(special_image_token_mask)} while"
+                f" the number of image given to the model is {num_images}. "
+                f"This prevents correct indexing and breaks batch generation."
+            )
+
+        final_embedding[image_to_overwrite] = image_features.contiguous().reshape(-1, embed_dim).to(target_device)
+        final_attention_mask |= image_to_overwrite
+        position_ids = (final_attention_mask.cumsum(-1) - 1).masked_fill_((final_attention_mask == 0), 1)
+
+        # 6. Mask out the embedding at padding positions, as we later use the past_key_value value
+        # to determine the non-attended tokens.
+        batch_indices, pad_indices = torch.where(input_ids == self.pad_token_id)
+        indices_to_mask = new_token_positions[batch_indices, pad_indices]
+
+        final_embedding[batch_indices, indices_to_mask] = 0
+
+        if labels is None:
+            final_labels = None
+
+        return final_embedding, final_attention_mask, final_labels, position_ids
+
+    @add_start_docstrings_to_model_forward(LLAVA_INPUTS_DOCSTRING)
+    @replace_return_docstrings(output_type=LlavaCausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        pixel_values: torch.FloatTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        vision_feature_layer: Optional[int] = None,
+        vision_feature_select_strategy: Optional[str] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, LlavaCausalLMOutputWithPast]:
+        r"""
+        Args:
+            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+                config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+                (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+        Returns:
+
+        Example:
+
+        ```python
+        >>> from PIL import Image
+        >>> import requests
+        >>> from transformers import AutoProcessor, LlavaForConditionalGeneration
+
+        >>> model = LlavaForConditionalGeneration.from_pretrained("llava-hf/llava-1.5-7b-hf")
+        >>> processor = AutoProcessor.from_pretrained("llava-hf/llava-1.5-7b-hf")
+
+        >>> prompt = "USER: <image>\nWhat's the content of the image? ASSISTANT:"
+        >>> url = "https://www.ilankelman.org/stopsigns/australia.jpg"
+        >>> image = Image.open(requests.get(url, stream=True).raw)
+
+        >>> inputs = processor(text=prompt, images=image, return_tensors="pt")
+
+        >>> # Generate
+        >>> generate_ids = model.generate(**inputs, max_new_tokens=15)
+        >>> processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+        "USER:  \nWhat's the content of the image?
+        ASSISTANT: The image features a busy city street with a stop sign prominently displayed"
+        ```"""
+
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        vision_feature_layer = (
+            vision_feature_layer if vision_feature_layer is not None else self.config.vision_feature_layer
+        )
+        vision_feature_select_strategy = (
+            vision_feature_select_strategy
+            if vision_feature_select_strategy is not None
+            else self.config.vision_feature_select_strategy
+        )
+
+        if inputs_embeds is None:
+            # 1. Extra the input embeddings
+            inputs_embeds = self.get_input_embeddings()(input_ids)
+
+            # 2. Merge text and images
+            if pixel_values is not None and input_ids.shape[1] != 1:
+                image_outputs = self.vision_tower(pixel_values, output_hidden_states=True)
+                # this is not memory efficient at all (output_hidden_states=True) will save all the hidden stated.
+                selected_image_feature = image_outputs.hidden_states[vision_feature_layer]
+
+                if vision_feature_select_strategy == "default":
+                    selected_image_feature = selected_image_feature[:, 1:]
+                elif vision_feature_select_strategy == "full":
+                    selected_image_feature = selected_image_feature
+                else:
+                    raise ValueError(
+                        f"Unexpected select feature strategy: {self.config.vision_feature_select_strategy}"
+                    )
+
+                image_features = self.multi_modal_projector(selected_image_feature)
+                inputs_embeds = inputs_embeds.to(image_features.dtype)
+                inputs_embeds, attention_mask, labels, position_ids = self._merge_input_ids_with_image_features(
+                    image_features, inputs_embeds, input_ids, attention_mask, labels
+                )
+
+            # In case input_ids.shape[1] == 1 & pixel_values==None & past_key_values != None, we are in the case of
+            # generation with cache
+            elif past_key_values is not None and pixel_values is not None and input_ids.shape[1] == 1:
+                # Retrieve the first layer to inspect the logits and mask out the hidden states
+                # that are set to 0
+                first_layer_past_key_value = past_key_values[0][0][:, :, :, 0]
+
+                # Sum all dimensions of head_dim (-2) to avoid random errors such as:
+                # https://github.com/huggingface/transformers/pull/28032#issuecomment-1863691941
+                batch_index, non_attended_tokens = torch.where(first_layer_past_key_value.float().sum(-2) == 0)
+
+                # Get the target length
+                target_length = input_ids.shape[1]
+                past_length = first_layer_past_key_value.shape[-1]
+
+                extended_attention_mask = torch.ones(
+                    (attention_mask.shape[0], past_length),
+                    dtype=attention_mask.dtype,
+                    device=attention_mask.device,
+                )
+
+                # Filter out only the tokens that can be un-attended, this can happen
+                # if one uses Llava + Fused modules where the cache on the
+                # first iteration is already big enough, or if one passes custom cache
+                valid_indices = non_attended_tokens < extended_attention_mask.size(-1)
+                new_batch_index = batch_index[valid_indices]
+                new_non_attended_tokens = non_attended_tokens[valid_indices]
+
+                # Zero-out the places where we don't need to attend
+                extended_attention_mask[new_batch_index, new_non_attended_tokens] = 0
+
+                attention_mask = torch.cat((extended_attention_mask, attention_mask[:, -target_length:]), dim=1)
+                position_ids = torch.sum(attention_mask, dim=1).unsqueeze(-1) - 1
+
+        outputs = self.language_model(
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        logits = outputs[0]
+
+        loss = None
+        if labels is not None:
+            # Shift so that tokens < n predict n
+            if attention_mask is not None:
+                shift_attention_mask = attention_mask[..., 1:]
+                shift_logits = logits[..., :-1, :][shift_attention_mask.to(logits.device) != 0].contiguous()
+                shift_labels = labels[..., 1:][shift_attention_mask.to(labels.device) != 0].contiguous()
+            else:
+                shift_logits = logits[..., :-1, :].contiguous()
+                shift_labels = labels[..., 1:].contiguous()
+            # Flatten the tokens
+            loss_fct = nn.CrossEntropyLoss()
+            loss = loss_fct(
+                shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1).to(shift_logits.device)
+            )
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return (loss,) + output if loss is not None else output
+
+        return LlavaCausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+    def prepare_inputs_for_generation(
+        self, input_ids, past_key_values=None, inputs_embeds=None, pixel_values=None, attention_mask=None, **kwargs
+    ):
+        if past_key_values is not None:
+            if isinstance(past_key_values, Cache):
+                cache_length = past_key_values.get_seq_length()
+                past_length = past_key_values.seen_tokens
+            else:
+                cache_length = past_length = past_key_values[0][0].shape[2]
+
+            # Keep only the unprocessed tokens:
+            # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
+            # some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as
+            # input)
+            if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
+                input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
+            # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
+            # input_ids based on the past_length.
+            elif past_length < input_ids.shape[1]:
+                input_ids = input_ids[:, past_length:]
+            # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
+            elif self.config.image_token_index in input_ids:
+                input_ids = input_ids[:, input_ids.shape[1] - 1 :]
+            # If the cache has seen more tokens than it can hold, then the cache has a size limit. Let's discard the
+            # older attention values, as their corresponding values are not part of the input.
+            if cache_length < past_length and attention_mask is not None:
+                attention_mask = attention_mask[:, -(cache_length + input_ids.shape[1]) :]
+
+        position_ids = kwargs.get("position_ids", None)
+        if attention_mask is not None and position_ids is None:
+            # create position_ids on the fly for batch generation
+            position_ids = attention_mask.long().cumsum(-1) - 1
+            position_ids.masked_fill_(attention_mask == 0, 1)
+            if past_key_values:
+                position_ids = position_ids[:, -input_ids.shape[1] :]
+
+        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
+        if inputs_embeds is not None and past_key_values is None:
+            model_inputs = {"inputs_embeds": inputs_embeds}
+        else:
+            model_inputs = {"input_ids": input_ids}
+
+        model_inputs.update(
+            {
+                "position_ids": position_ids,
+                "past_key_values": past_key_values,
+                "use_cache": kwargs.get("use_cache"),
+                "attention_mask": attention_mask,
+                "pixel_values": pixel_values,
+            }
+        )
+        return model_inputs
+
+    def _reorder_cache(self, *args, **kwargs):
+        return self.language_model._reorder_cache(*args, **kwargs)

preprocessor_config.json

@@ -0,0 +1,46 @@
+{
+  "_valid_processor_keys": [
+    "images",
+    "do_resize",
+    "size",
+    "resample",
+    "do_center_crop",
+    "crop_size",
+    "do_rescale",
+    "rescale_factor",
+    "do_normalize",
+    "image_mean",
+    "image_std",
+    "do_convert_rgb",
+    "return_tensors",
+    "data_format",
+    "input_data_format"
+  ],
+  "crop_size": {
+    "height": 384,
+    "width": 384
+  },
+  "do_center_crop": true,
+  "do_convert_rgb": true,
+  "do_normalize": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "image_mean": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "image_processor_type": "CLIPImageProcessor",
+  "image_std": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "processor_class": "LlavaProcessor",
+  "resample": 3,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "height": 384,
+    "width": 384
+  }
+}

special_tokens_map.json

@@ -0,0 +1,55 @@
+{
+  "additional_special_tokens": [
+    "<image>",
+    "<pad>"
+  ],
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "content": "<CLS|LLM-jp>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "<MASK|LLM-jp>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "<SEP|LLM-jp>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

text_config.json

@@ -0,0 +1,30 @@
+{
+  "_name_or_path": "llm-jp/llm-jp-3-1.8b-instruct",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 7168,
+  "max_position_embeddings": 4096,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "num_key_value_heads": 16,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 10000,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.45.2",
+  "use_cache": true,
+  "vocab_size": 99584
+}

tokenizer_config.json

@@ -0,0 +1,105 @@
+{
+  "add_bos_token": true,
+  "add_eos_token": false,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "<MASK|LLM-jp>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "4": {
+      "content": "<PAD|LLM-jp>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "5": {
+      "content": "<CLS|LLM-jp>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "6": {
+      "content": "<SEP|LLM-jp>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "7": {
+      "content": "<EOD|LLM-jp>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "99574": {
+      "content": "<image>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "99575": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "additional_special_tokens": [
+    "<image>",
+    "<pad>"
+  ],
+  "bos_token": "<s>",
+  "chat_template": "{{bos_token}}{% for message in messages %}{% if message['role'] == 'user' %}{{ '\\n\\n### 指示:\\n' + message['content'] }}{% elif message['role'] == 'system' %}{{ '以下は、タスクを説明する指示です。要求を適切に満たす応答を書きなさい。' }}{% elif message['role'] == 'assistant' %}{{ '\\n\\n### 応答:\\n' + message['content'] + eos_token }}{% endif %}{% if loop.last and add_generation_prompt %}{{ '\\n\\n### 応答:\\n' }}{% endif %}{% endfor %}",
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "<CLS|LLM-jp>",
+  "eod_token": "</s>",
+  "eos_token": "</s>",
+  "extra_ids": 0,
+  "mask_token": "<MASK|LLM-jp>",
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "<pad>",
+  "processor_class": "LlavaProcessor",
+  "sep_token": "<SEP|LLM-jp>",
+  "sp_model_kwargs": {},
+  "tokenizer_class": "PreTrainedTokenizerFast",
+  "unk_token": "<unk>"
+}