初始化项目，由ModelHub XC社区提供模型
Model: TIGER-Lab/Mantis-llava-7b Source: Original Platform
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README.md
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README.md
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---
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tags:
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- Mantis
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- VLM
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- LMM
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- Multimodal LLM
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- llava
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base_model: llava-hf/llava-1.5-7b-hf
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model-index:
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- name: Mantis-llava-7b
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results: []
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license: apache-2.0
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language:
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- en
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---
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# Mantis: Interleaved Multi-Image Instruction Tuning (Deprecated)
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**Mantis** is a multimodal conversational AI model that can chat with users about images and text. It's optimized for multi-image reasoning, where interleaved text and images can be used to generate responses.
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**Note that this is an older version of Mantis**, please refer to our newest version at [mantis-Siglip-llama3](https://huggingface.co/TIGER-Lab/Mantis-8B-siglip-llama3). The newer version improves significantly over both multi-image and single-image tasks.
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Mantis is trained on the newly curated dataset **Mantis-Instruct**, a large-scale multi-image QA dataset that covers various multi-image reasoning tasks.
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|[Demo](https://huggingface.co/spaces/TIGER-Lab/Mantis) | [Github](https://github.com/TIGER-AI-Lab/Mantis) | [Models](https://huggingface.co/collections/TIGER-Lab/mantis-6619b0834594c878cdb1d6e4) |
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## Inference
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You can install Mantis's GitHub codes as a Python package
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```bash
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pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
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```
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then run inference with codes here: [examples/run_mantis.py](https://github.com/TIGER-AI-Lab/Mantis/blob/main/examples/run_mantis_hf.py)
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```python
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from mantis.models.mllava import chat_mllava
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from PIL import Image
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import torch
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image1 = "image1.jpg"
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image2 = "image2.jpg"
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images = [Image.open(image1), Image.open(image2)]
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# load processor and model
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from mantis.models.mllava import MLlavaProcessor, LlavaForConditionalGeneration
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processor = MLlavaProcessor.from_pretrained("TIGER-Lab/Mantis-bakllava-7b")
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model = LlavaForConditionalGeneration.from_pretrained("TIGER-Lab/Mantis-bakllava-7b", device_map="auto", torch_dtype=torch.bfloat16, attn_implementation="flash_attention_2")
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# chat
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text = "<image> <image> What's the difference between these two images? Please describe as much as you can."
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response, history = chat_mllava(text, images, model, processor)
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print("USER: ", text)
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print("ASSISTANT: ", response)
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# The image on the right has a larger number of wallets displayed compared to the image on the left. The wallets in the right image are arranged in a grid pattern, while the wallets in the left image are displayed in a more scattered manner. The wallets in the right image have various colors, including red, purple, and brown, while the wallets in the left image are primarily brown.
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text = "How many items are there in image 1 and image 2 respectively?"
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response, history = chat_mllava(text, images, model, processor, history=history)
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print("USER: ", text)
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print("ASSISTANT: ", response)
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# There are two items in image 1 and four items in image 2.
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```
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Or, you can run the model without relying on the mantis codes, using pure hugging face transformers. See [examples/run_mantis_hf.py](https://github.com/TIGER-AI-Lab/Mantis/blob/main/examples/run_mantis_hf.py) for details.
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## Training
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Training codes will be released soon.
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|
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}
|
||||
}
|
||||
45
preprocessor_config.json
Normal file
45
preprocessor_config.json
Normal file
@@ -0,0 +1,45 @@
|
||||
{
|
||||
"_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": 336,
|
||||
"width": 336
|
||||
},
|
||||
"do_center_crop": true,
|
||||
"do_convert_rgb": true,
|
||||
"do_normalize": true,
|
||||
"do_rescale": true,
|
||||
"do_resize": true,
|
||||
"image_mean": [
|
||||
0.48145466,
|
||||
0.4578275,
|
||||
0.40821073
|
||||
],
|
||||
"image_processor_type": "CLIPImageProcessor",
|
||||
"image_std": [
|
||||
0.26862954,
|
||||
0.26130258,
|
||||
0.27577711
|
||||
],
|
||||
"processor_class": "LlavaProcessor",
|
||||
"resample": 3,
|
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"rescale_factor": 0.00392156862745098,
|
||||
"size": {
|
||||
"shortest_edge": 336
|
||||
}
|
||||
}
|
||||
30
special_tokens_map.json
Normal file
30
special_tokens_map.json
Normal file
@@ -0,0 +1,30 @@
|
||||
{
|
||||
"bos_token": {
|
||||
"content": "<s>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
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"single_word": false
|
||||
},
|
||||
"eos_token": {
|
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"content": "</s>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": {
|
||||
"content": "<pad>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"unk_token": {
|
||||
"content": "<unk>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
93409
tokenizer.json
Normal file
93409
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
BIN
tokenizer.model
(Stored with Git LFS)
Normal file
BIN
tokenizer.model
(Stored with Git LFS)
Normal file
Binary file not shown.
59
tokenizer_config.json
Normal file
59
tokenizer_config.json
Normal file
@@ -0,0 +1,59 @@
|
||||
{
|
||||
"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
|
||||
},
|
||||
"32000": {
|
||||
"content": "<image>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32001": {
|
||||
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|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
}
|
||||
},
|
||||
"bos_token": "<s>",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "</s>",
|
||||
"legacy": false,
|
||||
"model_max_length": 1000000000000000019884624838656,
|
||||
"pad_token": "<pad>",
|
||||
"padding_side": "left",
|
||||
"processor_class": "LlavaProcessor",
|
||||
"sp_model_kwargs": {},
|
||||
"tokenizer_class": "LlamaTokenizer",
|
||||
"trust_remote_code": false,
|
||||
"unk_token": "<unk>",
|
||||
"use_default_system_prompt": false
|
||||
}
|
||||
3
training_args.bin
Normal file
3
training_args.bin
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:a6e568e96a05f08864a3ca9357e9ddcdea5573bac563ec46259b292d2ebd25ce
|
||||
size 6264
|
||||
Reference in New Issue
Block a user