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Model: llava-hf/llava-v1.6-mistral-7b-hf Source: Original Platform
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---
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license: apache-2.0
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tags:
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- vision
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- image-text-to-text
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language:
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- en
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pipeline_tag: image-text-to-text
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inference: true
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---
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# LLaVa-Next, leveraging [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) as LLM
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The LLaVA-NeXT model was proposed in [LLaVA-NeXT: Improved reasoning, OCR, and world knowledge](https://llava-vl.github.io/blog/2024-01-30-llava-next/) by Haotian Liu, Chunyuan Li, Yuheng Li, Bo Li, Yuanhan Zhang, Sheng Shen, Yong Jae Lee. LLaVa-NeXT (also called LLaVa-1.6) improves upon [LLaVa-1.5](https://huggingface.co/transformers/main/model_doc/llava.html) by increasing the input image resolution and training on an improved visual instruction tuning dataset to improve OCR and common sense reasoning.
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Disclaimer: The team releasing LLaVa-NeXT did not write a model card for this model so this model card has been written by the Hugging Face team.
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## Model description
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LLaVa combines a pre-trained large language model with a pre-trained vision encoder for multimodal chatbot use cases. LLaVA 1.6 improves on LLaVA 1.5 BY:
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- Using [Mistral-7B](https://mistral.ai/news/announcing-mistral-7b/) (for this checkpoint) and [Nous-Hermes-2-Yi-34B](https://huggingface.co/NousResearch/Nous-Hermes-2-Yi-34B) which has better commercial licenses,
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and bilingual support
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- More diverse and high quality data mixture
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- Dynamic high resolution
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## Intended uses & limitations
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You can use the raw model for tasks like image captioning, visual question answering, multimodal chatbot use cases. See the [model hub](https://huggingface.co/models?search=llava-hf) to look for
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other versions on a task that interests you.
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### How to use
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Here's the prompt template for this model but we recomment to use the chat templates to format the prompt with `processor.apply_chat_template()`.
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That will apply the correct template for a given checkpoint for you.
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```
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"[INST] <image>\nWhat is shown in this image? [/INST]"
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```
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To run the model with the `pipeline`, see the below example:
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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-v1.6-mistral-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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You can also load and use the model like following:
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```python
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from transformers import LlavaNextProcessor, LlavaNextForConditionalGeneration
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import torch
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from PIL import Image
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import requests
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processor = LlavaNextProcessor.from_pretrained("llava-hf/llava-v1.6-mistral-7b-hf")
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model = LlavaNextForConditionalGeneration.from_pretrained("llava-hf/llava-v1.6-mistral-7b-hf", torch_dtype=torch.float16, low_cpu_mem_usage=True)
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model.to("cuda:0")
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# prepare image and text prompt, using the appropriate prompt template
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url = "https://github.com/haotian-liu/LLaVA/blob/1a91fc274d7c35a9b50b3cb29c4247ae5837ce39/images/llava_v1_5_radar.jpg?raw=true"
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image = Image.open(requests.get(url, stream=True).raw)
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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 is shown in this 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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inputs = processor(images=image, text=prompt, return_tensors="pt").to("cuda:0")
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# autoregressively complete prompt
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output = model.generate(**inputs, max_new_tokens=100)
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print(processor.decode(output[0], 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 = LlavaNextForConditionalGeneration.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 = LlavaNextForConditionalGeneration.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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### BibTeX entry and citation info
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```bibtex
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@misc{liu2023improved,
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title={Improved Baselines with Visual Instruction Tuning},
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author={Haotian Liu and Chunyuan Li and Yuheng Li and Yong Jae Lee},
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year={2023},
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eprint={2310.03744},
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archivePrefix={arXiv},
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primaryClass={cs.CV}
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}
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```
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{
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"<image>": 32000,
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"<pad>": 32001
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}
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{
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"chat_template": "{% for message in messages %}{% if message['role'] == 'system' %}{{ '<<SYS>>\n' + message['content'][0]['text'] + '\n<</SYS>>\n\n' }}{% elif message['role'] == 'user' %}{{ '[INST] ' }}{# Render all images first #}{% for content in message['content'] | selectattr('type', 'equalto', 'image') %}{{ '<image>\n' }}{% endfor %}{# Render all text next #}{% for content in message['content'] | selectattr('type', 'equalto', 'text') %}{{ content['text'] }}{% endfor %}{{' [/INST]' }}{% elif message['role'] == 'assistant' %}{{ ' ' + message['content'][0]['text'] + '</s> '}}{% else %}{{ raise_exception('Only user and assistant roles are supported!') }}{% endif %}{% endfor %}"
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}
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{
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"architectures": [
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"LlavaNextForConditionalGeneration"
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],
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"ignore_index": -100,
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"image_grid_pinpoints": [
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[
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336,
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],
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],
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"image_token_index": 32000,
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"model_type": "llava_next",
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"projector_hidden_act": "gelu",
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"text_config": {
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"_name_or_path": "mistralai/Mistral-7B-Instruct-v0.2",
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"architectures": [
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"MistralForCausalLM"
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],
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"intermediate_size": 14336,
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"max_position_embeddings": 32768,
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"model_type": "mistral",
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"num_key_value_heads": 8,
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"rms_norm_eps": 1e-05,
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"rope_theta": 1000000.0,
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"sliding_window": null,
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"vocab_size": 32064
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"torch_dtype": "float16",
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"transformers_version": "4.39.0.dev0",
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"use_image_newline_parameter": true,
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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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"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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|
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|
||||
52
preprocessor_config.json
Normal file
52
preprocessor_config.json
Normal file
@@ -0,0 +1,52 @@
|
||||
{
|
||||
"aspect_ratio_setting": "anyres",
|
||||
"crop_size": {
|
||||
"height": 336,
|
||||
"width": 336
|
||||
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|
||||
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|
||||
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|
||||
"do_normalize": true,
|
||||
"do_pad": true,
|
||||
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|
||||
"do_resize": true,
|
||||
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|
||||
[
|
||||
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|
||||
672
|
||||
],
|
||||
[
|
||||
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|
||||
336
|
||||
],
|
||||
[
|
||||
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|
||||
672
|
||||
],
|
||||
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|
||||
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|
||||
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|
||||
],
|
||||
[
|
||||
336,
|
||||
1008
|
||||
]
|
||||
],
|
||||
"image_mean": [
|
||||
0.48145466,
|
||||
0.4578275,
|
||||
0.40821073
|
||||
],
|
||||
"image_processor_type": "LlavaNextImageProcessor",
|
||||
"image_std": [
|
||||
0.26862954,
|
||||
0.26130258,
|
||||
0.27577711
|
||||
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|
||||
"processor_class": "LlavaNextProcessor",
|
||||
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|
||||
"rescale_factor": 0.00392156862745098,
|
||||
"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,
|
||||
"patch_size": 14,
|
||||
"processor_class": "LlavaNextProcessor",
|
||||
"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": {
|
||||
"content": "<s>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"eos_token": {
|
||||
"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
|
||||
}
|
||||
}
|
||||
91148
tokenizer.json
Normal file
91148
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.
69
tokenizer_config.json
Normal file
69
tokenizer_config.json
Normal file
@@ -0,0 +1,69 @@
|
||||
{
|
||||
"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": {
|
||||
"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": {
|
||||
"content": "<pad>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [],
|
||||
"bos_token": "<s>",
|
||||
"chat_template": "{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if message['role'] == 'user' %}{{ '[INST] ' + message['content'] + ' [/INST]' }}{% elif message['role'] == 'assistant' %}{{ message['content'] + eos_token}}{% else %}{{ raise_exception('Only user and assistant roles are supported!') }}{% endif %}{% endfor %}",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "</s>",
|
||||
"extra_special_tokens": {
|
||||
"image_token": "<image>"
|
||||
},
|
||||
"image_token": "<image>",
|
||||
"legacy": true,
|
||||
"max_length": null,
|
||||
"model_max_length": 1000000000000000019884624838656,
|
||||
"pad_to_multiple_of": null,
|
||||
"pad_token": "<pad>",
|
||||
"pad_token_type_id": 0,
|
||||
"padding_side": "left",
|
||||
"processor_class": "LlavaNextProcessor",
|
||||
"sp_model_kwargs": {},
|
||||
"spaces_between_special_tokens": false,
|
||||
"tokenizer_class": "LlamaTokenizer",
|
||||
"unk_token": "<unk>",
|
||||
"use_default_system_prompt": false
|
||||
}
|
||||
Reference in New Issue
Block a user