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Tongyi Qianwen RESEARCH LICENSE AGREEMENT
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Tongyi Qianwen Release Date: November 30, 2023
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By clicking to agree or by using or distributing any portion or element of the Tongyi Qianwen Materials, you will be deemed to have recognized and accepted the content of this Agreement, which is effective immediately.
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1. Definitions
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a. This Tongyi Qianwen RESEARCH LICENSE AGREEMENT (this "Agreement") shall mean the terms and conditions for use, reproduction, distribution and modification of the Materials as defined by this Agreement.
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216
README.md
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---
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language:
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- en
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license: other
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license_name: tongyi-qianwen-research
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license_link: LICENSE
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pipeline_tag: image-text-to-text
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tags:
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- vision
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- image-text-to-text
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---
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# LLaVA Interleave Model Card
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## Model Details
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**Model type:**
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LLaVA Interleave is an open-source chatbot trained by fine-tuning LLM on multimodal instruction-following data. It is an auto-regressive language model, based on the transformer architecture.
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Base LLM: [Qwen/Qwen1.5-7B-Chat](https://huggingface.co/Qwen/Qwen1.5-0.5B-Chat)
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**Paper or resources for more information:**
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https://llava-vl.github.io/
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**Primary intended uses:**
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The primary use of LLaVA-Next Interleave is research on large multimodal models and chatbots. This is only for research exploration, and prohibited for commercial usage.
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**Primary intended users:**
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The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.
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## How to use the model
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First, make sure to have `transformers >= 4.35.3`.
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The model supports multi-image and multi-prompt generation. Meaning that you can pass multiple images in your prompt. Make sure also to follow the correct prompt template (`USER: xxx\nASSISTANT:`) and add the token `<image>` to the location where you want to query images:
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### Using `pipeline`:
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Below we used [`"llava-hf/llava-interleave-qwen-0.5b-hf"`](https://huggingface.co/llava-hf/llava-interleave-qwen-0.5b-hf) checkpoint.
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```python
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from transformers import pipeline
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pipe = pipeline("image-text-to-text", model="llava-interleave-qwen-7b-hf")
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messages = [
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{
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"role": "user",
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"content": [
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{"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"},
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{"type": "text", "text": "What does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud"},
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],
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},
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]
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out = pipe(text=messages, max_new_tokens=20)
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print(out)
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>>> [{'input_text': [{'role': 'user', 'content': [{'type': 'image', 'url': 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg'}, {'type': 'text', 'text': 'What does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud'}]}], 'generated_text': 'Lava'}]
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```
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### Using pure `transformers`:
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Below is an example script to run generation in `float16` precision on a GPU device:
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```python
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import requests
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from PIL import Image
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import torch
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from transformers import AutoProcessor, LlavaForConditionalGeneration
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model_id = "llava-hf/llava-interleave-qwen-7b-hf"
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model = LlavaForConditionalGeneration.from_pretrained(
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model_id,
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torch_dtype=torch.float16,
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low_cpu_mem_usage=True,
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).to(0)
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processor = AutoProcessor.from_pretrained(model_id)
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# Define a chat history and use `apply_chat_template` to get correctly formatted prompt
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# Each value in "content" has to be a list of dicts with types ("text", "image")
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conversation = [
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{
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"role": "user",
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"content": [
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{"type": "text", "text": "What are these?"},
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{"type": "image"},
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],
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},
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]
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prompt = processor.apply_chat_template(conversation, add_generation_prompt=True)
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image_file = "http://images.cocodataset.org/val2017/000000039769.jpg"
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raw_image = Image.open(requests.get(image_file, stream=True).raw)
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inputs = processor(images=raw_image, text=prompt, return_tensors='pt').to(0, torch.float16)
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output = model.generate(**inputs, max_new_tokens=200, do_sample=False)
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print(processor.decode(output[0][2:], skip_special_tokens=True))
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```
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When prompting with videos/3D/multi-view input, prompt like following:
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```python
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# if you downsampled n frames from the input
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image_tokens = "<image>" * n
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prompt = f"<|im_start|>user {image_tokens}\nWhat are these?|im_end|><|im_start|>assistant"
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# With chat template if you sampled 5 frames you have to have 5 images in one conversation turn
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conversation = [
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{
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"role": "user",
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"content": [
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{"type": "text", "text": "What are these?"},
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{"type": "image"},
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{"type": "image"},
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{"type": "image"},
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{"type": "image"},
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{"type": "image"},
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],
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},
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]
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prompt = processor.apply_chat_template(conversation, add_generation_prompt=True)
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```
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When prompting with interleaved images and videos, prompt like following:
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```python
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# two interleaved images
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prompt = "<|im_start|>user <image><image>\nWhat is the difference between these two images?|im_end|><|im_start|>assistant"
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# two interleaved videos, if you downsampled n frames in total from both videos
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image_tokens = "<image>" * n
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prompt = f"<|im_start|>user {image_tokens}\nWhat are these?|im_end|><|im_start|>assistant"
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# chat template in interleaved format work same as in sampling videos. Just pass in as many images you want for a prompt
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conversation = [
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{
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"role": "user",
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"content": [
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{"type": "text", "text": "What is the difference between these two images?"},
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{"type": "image"},
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{"type": "image"},
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],
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},
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]
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```
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-----------
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From transformers>=v4.48, you can also pass image url or local path to the conversation history, and let the chat template handle the rest.
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Chat template will load the image for you and return inputs in `torch.Tensor` which you can pass directly to `model.generate()`
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```python
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messages = [
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{
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"role": "user",
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"content": [
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{"type": "image", "url": "https://www.ilankelman.org/stopsigns/australia.jpg"}
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{"type": "text", "text": "What is shown in this image?"},
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],
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},
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]
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inputs = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors"pt")
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output = model.generate(**inputs, max_new_tokens=50)
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```
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### Model optimization
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#### 4-bit quantization through `bitsandbytes` library
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First make sure to install `bitsandbytes`, `pip install bitsandbytes` and make sure to have access to a CUDA compatible GPU device. Simply change the snippet above with:
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```diff
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model = LlavaForConditionalGeneration.from_pretrained(
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model_id,
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torch_dtype=torch.float16,
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low_cpu_mem_usage=True,
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+ load_in_4bit=True
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)
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```
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#### Use Flash-Attention 2 to further speed-up generation
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First make sure to install `flash-attn`. Refer to the [original repository of Flash Attention](https://github.com/Dao-AILab/flash-attention) regarding that package installation. Simply change the snippet above with:
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```diff
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model = LlavaForConditionalGeneration.from_pretrained(
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model_id,
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torch_dtype=torch.float16,
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low_cpu_mem_usage=True,
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+ use_flash_attention_2=True
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).to(0)
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```
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||||||
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### License Notices
|
||||||
|
|
||||||
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This project utilizes certain datasets and checkpoints that are subject to their respective original licenses. Users must comply with all terms and conditions of these original licenses, including but not limited to the OpenAI Terms of Use for the dataset and the specific licenses for base language models for checkpoints trained using the dataset [Tongyi Qianwen LICENSE AGREEMENT](https://github.com/QwenLM/Qwen/blob/main/Tongyi%20Qianwen%20LICENSE%20AGREEMENT) and [META LLAMA 3 COMMUNITY LICENSE AGREEMENT](https://llama.meta.com/llama3/license/)). This project does not impose any additional constraints beyond those stipulated in the original licenses. Furthermore, users are reminded to ensure that their use of the dataset and checkpoints is in compliance with all applicable laws and regulations.
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### Bibtext citation
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||||||
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```bibtext
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@misc{li2024llavanextinterleavetacklingmultiimagevideo,
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title={LLaVA-NeXT-Interleave: Tackling Multi-image, Video, and 3D in Large Multimodal Models},
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author={Feng Li and Renrui Zhang and Hao Zhang and Yuanhan Zhang and Bo Li and Wei Li and Zejun Ma and Chunyuan Li},
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year={2024},
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eprint={2407.07895},
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archivePrefix={arXiv},
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primaryClass={cs.CV},
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url={https://arxiv.org/abs/2407.07895},
|
||||||
|
}
|
||||||
|
```
|
||||||
6
added_tokens.json
Normal file
6
added_tokens.json
Normal file
@@ -0,0 +1,6 @@
|
|||||||
|
{
|
||||||
|
"<image>": 151646,
|
||||||
|
"<|endoftext|>": 151643,
|
||||||
|
"<|im_end|>": 151645,
|
||||||
|
"<|im_start|>": 151644
|
||||||
|
}
|
||||||
3
chat_template.json
Normal file
3
chat_template.json
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
{
|
||||||
|
"chat_template": "{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n'}}{# Render all images first #}{% for content in message['content'] | selectattr('type', 'equalto', 'image') %}{{ '<image>' }}{% endfor %}{# Render all text next #}{% if message['role'] != 'assistant' %}{% for content in message['content'] | selectattr('type', 'equalto', 'text') %}{{ '\n' + content['text'] }}{% endfor %}{% else %}{% for content in message['content'] | selectattr('type', 'equalto', 'text') %}{% generation %}{{ '\n' + content['text'] }}{% endgeneration %}{% endfor %}{% endif %}{{'<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}"
|
||||||
|
}
|
||||||
42
config.json
Normal file
42
config.json
Normal file
@@ -0,0 +1,42 @@
|
|||||||
|
{
|
||||||
|
"architectures": [
|
||||||
|
"LlavaForConditionalGeneration"
|
||||||
|
],
|
||||||
|
"ignore_index": -100,
|
||||||
|
"image_token_index": 151646,
|
||||||
|
"model_type": "llava",
|
||||||
|
"projector_hidden_act": "gelu",
|
||||||
|
"text_config": {
|
||||||
|
"_name_or_path": "Qwen/Qwen1.5-7B-Chat",
|
||||||
|
"architectures": [
|
||||||
|
"Qwen2ForCausalLM"
|
||||||
|
],
|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
"model_type": "qwen2",
|
||||||
|
"rope_theta": 1000000.0,
|
||||||
|
"sliding_window": null,
|
||||||
|
"torch_dtype": "bfloat16",
|
||||||
|
"use_sliding_window": false,
|
||||||
|
"vocab_size": 152000
|
||||||
|
},
|
||||||
|
"torch_dtype": "bfloat16",
|
||||||
|
"transformers_version": "4.44.0.dev0",
|
||||||
|
"vision_config": {
|
||||||
|
"hidden_act": "gelu_pytorch_tanh",
|
||||||
|
"hidden_size": 1152,
|
||||||
|
"image_size": 384,
|
||||||
|
"intermediate_size": 4304,
|
||||||
|
"layer_norm_eps": 1e-06,
|
||||||
|
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|
||||||
|
"num_attention_heads": 16,
|
||||||
|
"num_hidden_layers": 26,
|
||||||
|
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|
||||||
|
"vision_use_head": false
|
||||||
|
},
|
||||||
|
"vision_feature_layer": -1,
|
||||||
|
"vision_feature_select_strategy": "full"
|
||||||
|
}
|
||||||
1
configuration.json
Normal file
1
configuration.json
Normal file
@@ -0,0 +1 @@
|
|||||||
|
{"framework": "pytorch", "task": "chat", "allow_remote": true}
|
||||||
6
generation_config.json
Normal file
6
generation_config.json
Normal file
@@ -0,0 +1,6 @@
|
|||||||
|
{
|
||||||
|
"_from_model_config": true,
|
||||||
|
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|
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|
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|
||||||
|
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|
||||||
|
}
|
||||||
151388
merges.txt
Normal file
151388
merges.txt
Normal file
File diff suppressed because it is too large
Load Diff
3
model-00001-of-00004.safetensors
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
"vision_tower.vision_model.encoder.layers.6.layer_norm1.weight": "model-00001-of-00004.safetensors",
|
||||||
|
"vision_tower.vision_model.encoder.layers.6.layer_norm2.bias": "model-00001-of-00004.safetensors",
|
||||||
|
"vision_tower.vision_model.encoder.layers.6.layer_norm2.weight": "model-00001-of-00004.safetensors",
|
||||||
|
"vision_tower.vision_model.encoder.layers.6.mlp.fc1.bias": "model-00001-of-00004.safetensors",
|
||||||
|
"vision_tower.vision_model.encoder.layers.6.mlp.fc1.weight": "model-00001-of-00004.safetensors",
|
||||||
|
"vision_tower.vision_model.encoder.layers.6.mlp.fc2.bias": "model-00001-of-00004.safetensors",
|
||||||
|
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|
||||||
|
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|
||||||
|
"vision_tower.vision_model.encoder.layers.6.self_attn.k_proj.weight": "model-00001-of-00004.safetensors",
|
||||||
|
"vision_tower.vision_model.encoder.layers.6.self_attn.out_proj.bias": "model-00001-of-00004.safetensors",
|
||||||
|
"vision_tower.vision_model.encoder.layers.6.self_attn.out_proj.weight": "model-00001-of-00004.safetensors",
|
||||||
|
"vision_tower.vision_model.encoder.layers.6.self_attn.q_proj.bias": "model-00001-of-00004.safetensors",
|
||||||
|
"vision_tower.vision_model.encoder.layers.6.self_attn.q_proj.weight": "model-00001-of-00004.safetensors",
|
||||||
|
"vision_tower.vision_model.encoder.layers.6.self_attn.v_proj.bias": "model-00001-of-00004.safetensors",
|
||||||
|
"vision_tower.vision_model.encoder.layers.6.self_attn.v_proj.weight": "model-00001-of-00004.safetensors",
|
||||||
|
"vision_tower.vision_model.encoder.layers.7.layer_norm1.bias": "model-00001-of-00004.safetensors",
|
||||||
|
"vision_tower.vision_model.encoder.layers.7.layer_norm1.weight": "model-00001-of-00004.safetensors",
|
||||||
|
"vision_tower.vision_model.encoder.layers.7.layer_norm2.bias": "model-00001-of-00004.safetensors",
|
||||||
|
"vision_tower.vision_model.encoder.layers.7.layer_norm2.weight": "model-00001-of-00004.safetensors",
|
||||||
|
"vision_tower.vision_model.encoder.layers.7.mlp.fc1.bias": "model-00001-of-00004.safetensors",
|
||||||
|
"vision_tower.vision_model.encoder.layers.7.mlp.fc1.weight": "model-00001-of-00004.safetensors",
|
||||||
|
"vision_tower.vision_model.encoder.layers.7.mlp.fc2.bias": "model-00001-of-00004.safetensors",
|
||||||
|
"vision_tower.vision_model.encoder.layers.7.mlp.fc2.weight": "model-00001-of-00004.safetensors",
|
||||||
|
"vision_tower.vision_model.encoder.layers.7.self_attn.k_proj.bias": "model-00001-of-00004.safetensors",
|
||||||
|
"vision_tower.vision_model.encoder.layers.7.self_attn.k_proj.weight": "model-00001-of-00004.safetensors",
|
||||||
|
"vision_tower.vision_model.encoder.layers.7.self_attn.out_proj.bias": "model-00001-of-00004.safetensors",
|
||||||
|
"vision_tower.vision_model.encoder.layers.7.self_attn.out_proj.weight": "model-00001-of-00004.safetensors",
|
||||||
|
"vision_tower.vision_model.encoder.layers.7.self_attn.q_proj.bias": "model-00001-of-00004.safetensors",
|
||||||
|
"vision_tower.vision_model.encoder.layers.7.self_attn.q_proj.weight": "model-00001-of-00004.safetensors",
|
||||||
|
"vision_tower.vision_model.encoder.layers.7.self_attn.v_proj.bias": "model-00001-of-00004.safetensors",
|
||||||
|
"vision_tower.vision_model.encoder.layers.7.self_attn.v_proj.weight": "model-00001-of-00004.safetensors",
|
||||||
|
"vision_tower.vision_model.encoder.layers.8.layer_norm1.bias": "model-00001-of-00004.safetensors",
|
||||||
|
"vision_tower.vision_model.encoder.layers.8.layer_norm1.weight": "model-00001-of-00004.safetensors",
|
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|
"vision_tower.vision_model.encoder.layers.8.layer_norm2.bias": "model-00001-of-00004.safetensors",
|
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|
"vision_tower.vision_model.encoder.layers.8.layer_norm2.weight": "model-00001-of-00004.safetensors",
|
||||||
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"vision_tower.vision_model.encoder.layers.8.mlp.fc1.bias": "model-00001-of-00004.safetensors",
|
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"vision_tower.vision_model.encoder.layers.8.mlp.fc1.weight": "model-00001-of-00004.safetensors",
|
||||||
|
"vision_tower.vision_model.encoder.layers.8.mlp.fc2.bias": "model-00001-of-00004.safetensors",
|
||||||
|
"vision_tower.vision_model.encoder.layers.8.mlp.fc2.weight": "model-00001-of-00004.safetensors",
|
||||||
|
"vision_tower.vision_model.encoder.layers.8.self_attn.k_proj.bias": "model-00001-of-00004.safetensors",
|
||||||
|
"vision_tower.vision_model.encoder.layers.8.self_attn.k_proj.weight": "model-00001-of-00004.safetensors",
|
||||||
|
"vision_tower.vision_model.encoder.layers.8.self_attn.out_proj.bias": "model-00001-of-00004.safetensors",
|
||||||
|
"vision_tower.vision_model.encoder.layers.8.self_attn.out_proj.weight": "model-00001-of-00004.safetensors",
|
||||||
|
"vision_tower.vision_model.encoder.layers.8.self_attn.q_proj.bias": "model-00001-of-00004.safetensors",
|
||||||
|
"vision_tower.vision_model.encoder.layers.8.self_attn.q_proj.weight": "model-00001-of-00004.safetensors",
|
||||||
|
"vision_tower.vision_model.encoder.layers.8.self_attn.v_proj.bias": "model-00001-of-00004.safetensors",
|
||||||
|
"vision_tower.vision_model.encoder.layers.8.self_attn.v_proj.weight": "model-00001-of-00004.safetensors",
|
||||||
|
"vision_tower.vision_model.encoder.layers.9.layer_norm1.bias": "model-00001-of-00004.safetensors",
|
||||||
|
"vision_tower.vision_model.encoder.layers.9.layer_norm1.weight": "model-00001-of-00004.safetensors",
|
||||||
|
"vision_tower.vision_model.encoder.layers.9.layer_norm2.bias": "model-00001-of-00004.safetensors",
|
||||||
|
"vision_tower.vision_model.encoder.layers.9.layer_norm2.weight": "model-00001-of-00004.safetensors",
|
||||||
|
"vision_tower.vision_model.encoder.layers.9.mlp.fc1.bias": "model-00001-of-00004.safetensors",
|
||||||
|
"vision_tower.vision_model.encoder.layers.9.mlp.fc1.weight": "model-00001-of-00004.safetensors",
|
||||||
|
"vision_tower.vision_model.encoder.layers.9.mlp.fc2.bias": "model-00001-of-00004.safetensors",
|
||||||
|
"vision_tower.vision_model.encoder.layers.9.mlp.fc2.weight": "model-00001-of-00004.safetensors",
|
||||||
|
"vision_tower.vision_model.encoder.layers.9.self_attn.k_proj.bias": "model-00001-of-00004.safetensors",
|
||||||
|
"vision_tower.vision_model.encoder.layers.9.self_attn.k_proj.weight": "model-00001-of-00004.safetensors",
|
||||||
|
"vision_tower.vision_model.encoder.layers.9.self_attn.out_proj.bias": "model-00001-of-00004.safetensors",
|
||||||
|
"vision_tower.vision_model.encoder.layers.9.self_attn.out_proj.weight": "model-00001-of-00004.safetensors",
|
||||||
|
"vision_tower.vision_model.encoder.layers.9.self_attn.q_proj.bias": "model-00001-of-00004.safetensors",
|
||||||
|
"vision_tower.vision_model.encoder.layers.9.self_attn.q_proj.weight": "model-00001-of-00004.safetensors",
|
||||||
|
"vision_tower.vision_model.encoder.layers.9.self_attn.v_proj.bias": "model-00001-of-00004.safetensors",
|
||||||
|
"vision_tower.vision_model.encoder.layers.9.self_attn.v_proj.weight": "model-00001-of-00004.safetensors",
|
||||||
|
"vision_tower.vision_model.post_layernorm.bias": "model-00001-of-00004.safetensors",
|
||||||
|
"vision_tower.vision_model.post_layernorm.weight": "model-00001-of-00004.safetensors"
|
||||||
|
}
|
||||||
|
}
|
||||||
24
preprocessor_config.json
Normal file
24
preprocessor_config.json
Normal file
@@ -0,0 +1,24 @@
|
|||||||
|
{
|
||||||
|
"do_convert_rgb": null,
|
||||||
|
"do_normalize": true,
|
||||||
|
"do_rescale": true,
|
||||||
|
"do_resize": true,
|
||||||
|
"image_mean": [
|
||||||
|
0.5,
|
||||||
|
0.5,
|
||||||
|
0.5
|
||||||
|
],
|
||||||
|
"image_processor_type": "SiglipImageProcessor",
|
||||||
|
"image_std": [
|
||||||
|
0.5,
|
||||||
|
0.5,
|
||||||
|
0.5
|
||||||
|
],
|
||||||
|
"processor_class": "LlavaProcessor",
|
||||||
|
"resample": 3,
|
||||||
|
"rescale_factor": 0.00392156862745098,
|
||||||
|
"size": {
|
||||||
|
"height": 384,
|
||||||
|
"width": 384
|
||||||
|
}
|
||||||
|
}
|
||||||
7
processor_config.json
Normal file
7
processor_config.json
Normal file
@@ -0,0 +1,7 @@
|
|||||||
|
{
|
||||||
|
"image_token": "<image>",
|
||||||
|
"num_additional_image_tokens": 0,
|
||||||
|
"patch_size": 14,
|
||||||
|
"processor_class": "LlavaProcessor",
|
||||||
|
"vision_feature_select_strategy": "full"
|
||||||
|
}
|
||||||
20
special_tokens_map.json
Normal file
20
special_tokens_map.json
Normal file
@@ -0,0 +1,20 @@
|
|||||||
|
{
|
||||||
|
"additional_special_tokens": [
|
||||||
|
"<|im_start|>",
|
||||||
|
"<|im_end|>"
|
||||||
|
],
|
||||||
|
"eos_token": {
|
||||||
|
"content": "<|im_end|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false
|
||||||
|
},
|
||||||
|
"pad_token": {
|
||||||
|
"content": "<|endoftext|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false
|
||||||
|
}
|
||||||
|
}
|
||||||
303121
tokenizer.json
Normal file
303121
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
56
tokenizer_config.json
Normal file
56
tokenizer_config.json
Normal file
@@ -0,0 +1,56 @@
|
|||||||
|
{
|
||||||
|
"add_prefix_space": false,
|
||||||
|
"added_tokens_decoder": {
|
||||||
|
"151643": {
|
||||||
|
"content": "<|endoftext|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151644": {
|
||||||
|
"content": "<|im_start|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151645": {
|
||||||
|
"content": "<|im_end|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151646": {
|
||||||
|
"content": "<image>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"additional_special_tokens": [
|
||||||
|
"<|im_start|>",
|
||||||
|
"<|im_end|>"
|
||||||
|
],
|
||||||
|
"bos_token": null,
|
||||||
|
"chat_template": "{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n' }}{% endif %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}",
|
||||||
|
"clean_up_tokenization_spaces": false,
|
||||||
|
"eos_token": "<|im_end|>",
|
||||||
|
"errors": "replace",
|
||||||
|
"extra_special_tokens": {
|
||||||
|
"image_token": "<image>"
|
||||||
|
},
|
||||||
|
"image_token": "<image>",
|
||||||
|
"model_max_length": 32768,
|
||||||
|
"pad_token": "<|endoftext|>",
|
||||||
|
"processor_class": "LlavaProcessor",
|
||||||
|
"split_special_tokens": false,
|
||||||
|
"tokenizer_class": "Qwen2Tokenizer",
|
||||||
|
"unk_token": null
|
||||||
|
}
|
||||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user