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Model: llava-hf/llava-1.5-7b-hf Source: Original Platform
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README.md
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---
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language:
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- en
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datasets:
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- liuhaotian/LLaVA-Instruct-150K
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pipeline_tag: image-text-to-text
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arxiv: 2304.08485
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license: llama2
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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 Model Card
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Below is the model card of Llava model 7b, which is copied from the original Llava model card that you can find [here](https://huggingface.co/liuhaotian/llava-v1.5-13b).
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Check out also the Google Colab demo to run Llava on a free-tier Google Colab instance: [](https://colab.research.google.com/drive/1qsl6cd2c8gGtEW1xV5io7S8NHh-Cp1TV?usp=sharing)
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Or check out our Spaces demo! [](https://huggingface.co/spaces/llava-hf/llava-4bit)
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## Model details
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**Model type:**
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LLaVA is an open-source chatbot trained by fine-tuning LLaMA/Vicuna on GPT-generated multimodal instruction-following data.
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It is an auto-regressive language model, based on the transformer architecture.
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**Model date:**
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LLaVA-v1.5-7B was trained in September 2023.
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**Paper or resources for more information:**
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https://llava-vl.github.io/
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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-1.5-7b-hf"`](https://huggingface.co/llava-hf/llava-1.5-7b-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-hf/llava-1.5-7b-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-1.5-7b-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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-----------
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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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## License
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Llama 2 is licensed under the LLAMA 2 Community License,
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Copyright (c) Meta Platforms, Inc. All Rights Reserved.
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added_tokens.json
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{
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"<image>": 32000,
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"<pad>": 32001
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}
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{% for message in messages %}{% if message['role'] != 'system' %}{{ message['role'].upper() + ': '}}{% endif %}{# Render all images first #}{% for content in message['content'] | selectattr('type', 'equalto', 'image') %}{{ '<image>
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' }}{% endfor %}{# Render all text next #}{% if message['role'] != 'assistant' %}{% for content in message['content'] | selectattr('type', 'equalto', 'text') %}{{ content['text'] + ' '}}{% endfor %}{% else %}{% for content in message['content'] | selectattr('type', 'equalto', 'text') %}{% generation %}{{ content['text'] + ' '}}{% endgeneration %}{% endfor %}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ 'ASSISTANT:' }}{% endif %}
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{
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"chat_template": "{% for message in messages %}{% if message['role'] != 'system' %}{{ message['role'].upper() + ': '}}{% endif %}{# Render all images first #}{% for content in message['content'] | selectattr('type', 'equalto', 'image') %}{{ '<image>\n' }}{% endfor %}{# Render all text next #}{% if message['role'] != 'assistant' %}{% for content in message['content'] | selectattr('type', 'equalto', 'text') %}{{ content['text'] + ' '}}{% endfor %}{% else %}{% for content in message['content'] | selectattr('type', 'equalto', 'text') %}{% generation %}{{ content['text'] + ' '}}{% endgeneration %}{% endfor %}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ 'ASSISTANT:' }}{% endif %}"
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}
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{
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"architectures": [
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"LlavaForConditionalGeneration"
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],
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"ignore_index": -100,
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"image_token_index": 32000,
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"model_type": "llava",
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"pad_token_id": 32001,
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"projector_hidden_act": "gelu",
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"text_config": {
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"_name_or_path": "lmsys/vicuna-7b-v1.5",
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"architectures": [
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"LlamaForCausalLM"
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],
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"max_position_embeddings": 4096,
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"model_type": "llama",
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"rms_norm_eps": 1e-05,
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"torch_dtype": "float16",
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"vocab_size": 32064
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},
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"tie_word_embeddings": false,
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"torch_dtype": "float16",
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"transformers_version": "4.36.0.dev0",
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"vision_config": {
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"hidden_size": 1024,
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"image_size": 336,
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"intermediate_size": 4096,
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"model_type": "clip_vision_model",
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"num_attention_heads": 16,
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"num_hidden_layers": 24,
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"patch_size": 14,
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"projection_dim": 768,
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"vocab_size": 32000
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},
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"vision_feature_layer": -2,
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"vision_feature_select_strategy": "default",
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"vocab_size": 32064
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}
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{"framework": "pytorch", "task": "image-text-to-text", "allow_remote": true}
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"transformers_version": "4.36.0.dev0"
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}
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|
||||
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|
||||
28
preprocessor_config.json
Normal file
28
preprocessor_config.json
Normal file
@@ -0,0 +1,28 @@
|
||||
{
|
||||
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|
||||
"height": 336,
|
||||
"width": 336
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"size": {
|
||||
"shortest_edge": 336
|
||||
}
|
||||
}
|
||||
7
processor_config.json
Normal file
7
processor_config.json
Normal file
@@ -0,0 +1,7 @@
|
||||
{
|
||||
"image_token": "<image>",
|
||||
"num_additional_image_tokens": 1,
|
||||
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|
||||
"processor_class": "LlavaProcessor",
|
||||
"vision_feature_select_strategy": "default"
|
||||
}
|
||||
30
special_tokens_map.json
Normal file
30
special_tokens_map.json
Normal file
@@ -0,0 +1,30 @@
|
||||
{
|
||||
"bos_token": {
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
"eos_token": {
|
||||
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|
||||
"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
|
||||
}
|
||||
}
|
||||
277157
tokenizer.json
Normal file
277157
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.
64
tokenizer_config.json
Normal file
64
tokenizer_config.json
Normal file
@@ -0,0 +1,64 @@
|
||||
{
|
||||
"add_bos_token": true,
|
||||
"add_eos_token": false,
|
||||
"add_prefix_space": null,
|
||||
"added_tokens_decoder": {
|
||||
"0": {
|
||||
"content": "<unk>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"1": {
|
||||
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|
||||
"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": {
|
||||
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|
||||
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|
||||
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|
||||
"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>",
|
||||
"extra_special_tokens": {
|
||||
"image_token": "<image>"
|
||||
},
|
||||
"image_token": "<image>",
|
||||
"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
|
||||
}
|
||||
Reference in New Issue
Block a user