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Upload ZipRerank (Qwen3-VL-8B-Instruct, listwise visual-document reranker)
Browse files- .gitattributes +36 -0
- README.md +242 -0
- added_tokens.json +28 -0
- chat_template.jinja +120 -0
- config.json +64 -0
- generation_config.json +13 -0
- merges.txt +0 -0
- model-00001-of-00004.safetensors +3 -0
- model-00002-of-00004.safetensors +3 -0
- model-00003-of-00004.safetensors +3 -0
- model-00004-of-00004.safetensors +3 -0
- model.safetensors.index.json +758 -0
- preprocessor_config.json +39 -0
- special_tokens_map.json +31 -0
- tokenizer.json +3 -0
- tokenizer_config.json +240 -0
- video_preprocessor_config.json +41 -0
- vocab.json +0 -0
.gitattributes
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README.md
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| 1 |
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---
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library_name: transformers
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pipeline_tag: visual-document-retrieval
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license: apache-2.0
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base_model: Qwen/Qwen3-VL-8B-Instruct
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tags:
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- reranker
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- rerank
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- listwise-reranker
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- visual-document-retrieval
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| 11 |
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- multimodal
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| 12 |
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- document-understanding
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- qwen3-vl
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| 14 |
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- rankgpt
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| 15 |
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- mmdocir
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language:
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- en
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| 18 |
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---
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| 19 |
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| 20 |
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# ZipRerank
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| 21 |
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**ZipRerank** is a **listwise reranker for visual documents**, built on top of
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| 23 |
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[`Qwen/Qwen3-VL-8B-Instruct`](https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct).
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| 24 |
+
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| 25 |
+
Given a text query and a set of document page images (typically rendered from a PDF),
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| 26 |
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ZipRerank scores every page and returns them ordered from most to least relevant.
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| 27 |
+
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| 28 |
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ZipRerank can be used either as:
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| 29 |
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| 30 |
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- **the second stage** of a two-stage visual-document retrieval pipeline — a cheap first-stage
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| 31 |
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retriever (dense page embeddings, ColPali / ColQwen family, keyword search, …) proposes the
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| 32 |
+
top-K pages, and ZipRerank reranks them; or
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| 33 |
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- **a dedicated sliding-window reranker with overlap**, ranking up to 20 pages per forward pass
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| 34 |
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and stitching longer documents together with an overlapping window loop.
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| 35 |
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| 36 |
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## Model details
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| 37 |
+
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| 38 |
+
| | |
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| 39 |
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|---|---|
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| 40 |
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| Architecture | `Qwen3VLForConditionalGeneration` |
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| 41 |
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| Base model | `Qwen/Qwen3-VL-8B-Instruct` |
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| 42 |
+
| Parameters | ~8B |
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| 43 |
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| Precision | `bfloat16` |
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| 44 |
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| Max window size | 20 pages per forward pass |
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| 45 |
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| Training data | MMDocIR training set + RankZephyr data |
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| 46 |
+
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| 47 |
+
## Installation
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| 48 |
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| 49 |
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```bash
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| 50 |
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pip install "transformers>=4.57" accelerate torch torchvision pillow pymupdf
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| 51 |
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# Optional but strongly recommended for fast inference:
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| 52 |
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pip install flash-attn --no-build-isolation
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| 53 |
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```
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| 54 |
+
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| 55 |
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## Quick start
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| 56 |
+
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| 57 |
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The snippet below is self-contained. It:
|
| 58 |
+
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| 59 |
+
1. Renders a PDF to page images with PyMuPDF (you can also pass your own `PIL.Image` list).
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| 60 |
+
2. Builds a RankGPT-style prompt that asks the model to rank pages `A`–`T`.
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| 61 |
+
3. Terminates the prompt with a `[` token so the first predicted token is a letter.
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| 62 |
+
4. Reads the logits at that position for each letter and sorts pages by score.
|
| 63 |
+
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| 64 |
+
```python
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| 65 |
+
import fitz # PyMuPDF
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| 66 |
+
import torch
|
| 67 |
+
from PIL import Image
|
| 68 |
+
from transformers import AutoProcessor
|
| 69 |
+
from transformers.models.qwen3_vl import Qwen3VLForConditionalGeneration
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| 70 |
+
|
| 71 |
+
MODEL_ID = "mtri-admin/ZipRerank"
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| 72 |
+
|
| 73 |
+
processor = AutoProcessor.from_pretrained(MODEL_ID, trust_remote_code=True)
|
| 74 |
+
model = Qwen3VLForConditionalGeneration.from_pretrained(
|
| 75 |
+
MODEL_ID,
|
| 76 |
+
torch_dtype=torch.bfloat16,
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| 77 |
+
device_map="auto",
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| 78 |
+
attn_implementation="flash_attention_2", # or "sdpa" if flash-attn is unavailable
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| 79 |
+
trust_remote_code=True,
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| 80 |
+
).eval()
|
| 81 |
+
tokenizer = processor.tokenizer
|
| 82 |
+
|
| 83 |
+
|
| 84 |
+
def pdf_to_images(pdf_path: str, max_size: int = 1024):
|
| 85 |
+
"""Render every page so the longest edge is at most ``max_size`` pixels."""
|
| 86 |
+
doc = fitz.open(pdf_path)
|
| 87 |
+
images = []
|
| 88 |
+
for page in doc:
|
| 89 |
+
scale = max_size / max(page.rect.width, page.rect.height)
|
| 90 |
+
pix = page.get_pixmap(matrix=fitz.Matrix(scale, scale))
|
| 91 |
+
images.append(Image.frombytes("RGB", (pix.width, pix.height), pix.samples))
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| 92 |
+
doc.close()
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| 93 |
+
return images
|
| 94 |
+
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| 95 |
+
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| 96 |
+
def create_ranking_prompt(query: str, num_passages: int) -> str:
|
| 97 |
+
lines = [
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| 98 |
+
"You are RankGPT, an intelligent assistant that can rank passages "
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| 99 |
+
"based on their relevancy to the query.",
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| 100 |
+
"",
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| 101 |
+
f"I will provide you with {num_passages} passages as images.",
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| 102 |
+
"Rank the passages based on their relevance to the search query.",
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| 103 |
+
"",
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| 104 |
+
"The images are provided in order: "
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| 105 |
+
+ ", ".join(
|
| 106 |
+
f"Picture {i + 1} is passage [{chr(ord('A') + i)}]"
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| 107 |
+
for i in range(num_passages)
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| 108 |
+
)
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| 109 |
+
+ ".",
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| 110 |
+
"",
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| 111 |
+
f"Search Query: {query}",
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| 112 |
+
"",
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| 113 |
+
"Rank the passages above based on their relevance to the search query.",
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| 114 |
+
"The passages should be listed in descending order using identifiers.",
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| 115 |
+
"The most relevant passages should be listed first.",
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| 116 |
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"The output format should be [A] > [B], etc.",
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| 117 |
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"Only output the ranking results, do not say anything else.",
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| 118 |
+
]
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| 119 |
+
return "\n".join(lines)
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| 120 |
+
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| 121 |
+
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| 122 |
+
@torch.no_grad()
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| 123 |
+
def rerank_window(query: str, images):
|
| 124 |
+
"""Rank up to 20 page images in a single forward pass.
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| 125 |
+
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| 126 |
+
Returns a list of 0-based indices into ``images``, ordered best-first.
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| 127 |
+
"""
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| 128 |
+
assert 1 <= len(images) <= 20, "Window size must be between 1 and 20."
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| 129 |
+
messages = [{
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| 130 |
+
"role": "user",
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| 131 |
+
"content": [{"type": "text", "text": create_ranking_prompt(query, len(images))}]
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| 132 |
+
+ [{"type": "image", "image": img} for img in images],
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| 133 |
+
}]
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| 134 |
+
inputs = processor.apply_chat_template(
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| 135 |
+
[messages],
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| 136 |
+
tokenize=True,
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| 137 |
+
add_generation_prompt=True,
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| 138 |
+
return_dict=True,
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| 139 |
+
return_tensors="pt",
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| 140 |
+
)
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| 141 |
+
# Force the first predicted token to be a letter by appending "["
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| 142 |
+
prompt_ids = inputs["input_ids"][0].tolist()
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| 143 |
+
prompt_ids.append(tokenizer.encode("[", add_special_tokens=False)[0])
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| 144 |
+
input_ids = torch.tensor([prompt_ids], dtype=torch.long, device=model.device)
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| 145 |
+
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| 146 |
+
logits = model(
|
| 147 |
+
input_ids=input_ids,
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| 148 |
+
attention_mask=torch.ones_like(input_ids),
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| 149 |
+
pixel_values=inputs["pixel_values"].to(model.device),
|
| 150 |
+
image_grid_thw=inputs["image_grid_thw"].to(model.device),
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| 151 |
+
).logits[0, -1, :]
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| 152 |
+
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| 153 |
+
letter_ids = [
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| 154 |
+
tokenizer.encode(chr(ord("A") + i), add_special_tokens=False)[0]
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| 155 |
+
for i in range(len(images))
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| 156 |
+
]
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| 157 |
+
scores = [logits[tid].item() for tid in letter_ids]
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| 158 |
+
return sorted(range(len(images)), key=lambda i: scores[i], reverse=True)
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| 159 |
+
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| 160 |
+
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| 161 |
+
pages = pdf_to_images("report.pdf", max_size=1024)
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| 162 |
+
ranking = rerank_window("What is the company revenue?", pages[:20])
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| 163 |
+
print("Best-first page indices:", ranking)
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| 164 |
+
```
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| 165 |
+
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| 166 |
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### Sliding window for long documents
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| 167 |
+
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| 168 |
+
For documents with more than 20 pages, slide a window from the end of the list toward the
|
| 169 |
+
beginning, progressively bubbling the most relevant pages to the front. Typical defaults are
|
| 170 |
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`window_size=20`, `stride=10` (50% overlap).
|
| 171 |
+
|
| 172 |
+
```python
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| 173 |
+
def rerank(query, images, window_size=20, stride=10):
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| 174 |
+
n = len(images)
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| 175 |
+
ws = min(window_size, n)
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| 176 |
+
st = min(stride, n)
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| 177 |
+
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| 178 |
+
if n <= ws:
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+
return rerank_window(query, images)
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| 180 |
+
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| 181 |
+
indices = list(range(n))
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| 182 |
+
cur = list(images)
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| 183 |
+
end, start = n, n - ws
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| 184 |
+
while end > 0 and start + st != 0:
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| 185 |
+
start = max(start, 0)
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| 186 |
+
ranked = rerank_window(query, cur[start:end])
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| 187 |
+
new_indices = [indices[start + p] for p in ranked]
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| 188 |
+
new_images = [cur[start + p] for p in ranked]
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| 189 |
+
for i, (idx, img) in enumerate(zip(new_indices, new_images)):
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| 190 |
+
indices[start + i] = idx
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| 191 |
+
cur[start + i] = img
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| 192 |
+
end -= st
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| 193 |
+
start -= st
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| 194 |
+
return indices
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| 195 |
+
|
| 196 |
+
|
| 197 |
+
ranking = rerank("What is the company revenue?", pages)
|
| 198 |
+
```
|
| 199 |
+
|
| 200 |
+
> **Tip.** For maximum throughput on long documents, add a content-addressed LRU cache
|
| 201 |
+
> around `model.model.get_image_features(...)` so that overlapping windows reuse ViT
|
| 202 |
+
> embeddings across calls — each page image then needs to be encoded by the vision tower at
|
| 203 |
+
> most once per query.
|
| 204 |
+
|
| 205 |
+
### Using your own images
|
| 206 |
+
|
| 207 |
+
`rerank_window` / `rerank` accept any list of `PIL.Image.Image`. If you already have page
|
| 208 |
+
images (e.g. from `pdf2image`, a screenshot pipeline, or a document layout tool), you can skip
|
| 209 |
+
`pdf_to_images` entirely:
|
| 210 |
+
|
| 211 |
+
```python
|
| 212 |
+
from PIL import Image
|
| 213 |
+
|
| 214 |
+
images = [Image.open(p).convert("RGB") for p in ["page1.png", "page2.png", "page3.png"]]
|
| 215 |
+
ranking = rerank("architecture diagram", images)
|
| 216 |
+
```
|
| 217 |
+
|
| 218 |
+
## How it works
|
| 219 |
+
|
| 220 |
+
1. **Prompt construction** — a RankGPT-style prompt asks the model to rank the pages
|
| 221 |
+
(labeled `A`–`T`) by relevance to the query.
|
| 222 |
+
2. **Logits scoring** — one forward pass; the logit for each letter token at the last
|
| 223 |
+
position is the relevance score for that page.
|
| 224 |
+
3. **Sliding window** — for `n > window_size`, a window slides from the end of the list
|
| 225 |
+
toward the start, progressively reranking overlapping slices.
|
| 226 |
+
|
| 227 |
+
## Intended use and limitations
|
| 228 |
+
|
| 229 |
+
**Intended use.** Reranking of candidate document pages for tasks such as visual document
|
| 230 |
+
question answering, enterprise document search, and RAG over PDFs. Works either as the
|
| 231 |
+
second stage of a retrieve-then-rerank pipeline, or as a standalone sliding-window reranker
|
| 232 |
+
over an arbitrary list of page images.
|
| 233 |
+
|
| 234 |
+
**Out-of-scope.** ZipRerank is not a first-stage retriever: running it over every page of a
|
| 235 |
+
large corpus is expensive and unnecessary — use a cheap retriever first, then rerank the
|
| 236 |
+
top-K pages with ZipRerank.
|
| 237 |
+
|
| 238 |
+
**Limitations.**
|
| 239 |
+
- Training focused on English documents; multilingual performance has not been evaluated,
|
| 240 |
+
so results on non-English content may vary.
|
| 241 |
+
- The window size is capped at 20 pages per forward pass (letters `A`–`T`); longer documents
|
| 242 |
+
rely on the sliding-window procedure described above.
|
added_tokens.json
ADDED
|
@@ -0,0 +1,28 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"</think>": 151668,
|
| 3 |
+
"</tool_call>": 151658,
|
| 4 |
+
"</tool_response>": 151666,
|
| 5 |
+
"<think>": 151667,
|
| 6 |
+
"<tool_call>": 151657,
|
| 7 |
+
"<tool_response>": 151665,
|
| 8 |
+
"<|box_end|>": 151649,
|
| 9 |
+
"<|box_start|>": 151648,
|
| 10 |
+
"<|endoftext|>": 151643,
|
| 11 |
+
"<|file_sep|>": 151664,
|
| 12 |
+
"<|fim_middle|>": 151660,
|
| 13 |
+
"<|fim_pad|>": 151662,
|
| 14 |
+
"<|fim_prefix|>": 151659,
|
| 15 |
+
"<|fim_suffix|>": 151661,
|
| 16 |
+
"<|im_end|>": 151645,
|
| 17 |
+
"<|im_start|>": 151644,
|
| 18 |
+
"<|image_pad|>": 151655,
|
| 19 |
+
"<|object_ref_end|>": 151647,
|
| 20 |
+
"<|object_ref_start|>": 151646,
|
| 21 |
+
"<|quad_end|>": 151651,
|
| 22 |
+
"<|quad_start|>": 151650,
|
| 23 |
+
"<|repo_name|>": 151663,
|
| 24 |
+
"<|video_pad|>": 151656,
|
| 25 |
+
"<|vision_end|>": 151653,
|
| 26 |
+
"<|vision_pad|>": 151654,
|
| 27 |
+
"<|vision_start|>": 151652
|
| 28 |
+
}
|
chat_template.jinja
ADDED
|
@@ -0,0 +1,120 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{%- if tools %}
|
| 2 |
+
{{- '<|im_start|>system\n' }}
|
| 3 |
+
{%- if messages[0].role == 'system' %}
|
| 4 |
+
{%- if messages[0].content is string %}
|
| 5 |
+
{{- messages[0].content }}
|
| 6 |
+
{%- else %}
|
| 7 |
+
{%- for content in messages[0].content %}
|
| 8 |
+
{%- if 'text' in content %}
|
| 9 |
+
{{- content.text }}
|
| 10 |
+
{%- endif %}
|
| 11 |
+
{%- endfor %}
|
| 12 |
+
{%- endif %}
|
| 13 |
+
{{- '\n\n' }}
|
| 14 |
+
{%- endif %}
|
| 15 |
+
{{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
|
| 16 |
+
{%- for tool in tools %}
|
| 17 |
+
{{- "\n" }}
|
| 18 |
+
{{- tool | tojson }}
|
| 19 |
+
{%- endfor %}
|
| 20 |
+
{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
|
| 21 |
+
{%- else %}
|
| 22 |
+
{%- if messages[0].role == 'system' %}
|
| 23 |
+
{{- '<|im_start|>system\n' }}
|
| 24 |
+
{%- if messages[0].content is string %}
|
| 25 |
+
{{- messages[0].content }}
|
| 26 |
+
{%- else %}
|
| 27 |
+
{%- for content in messages[0].content %}
|
| 28 |
+
{%- if 'text' in content %}
|
| 29 |
+
{{- content.text }}
|
| 30 |
+
{%- endif %}
|
| 31 |
+
{%- endfor %}
|
| 32 |
+
{%- endif %}
|
| 33 |
+
{{- '<|im_end|>\n' }}
|
| 34 |
+
{%- endif %}
|
| 35 |
+
{%- endif %}
|
| 36 |
+
{%- set image_count = namespace(value=0) %}
|
| 37 |
+
{%- set video_count = namespace(value=0) %}
|
| 38 |
+
{%- for message in messages %}
|
| 39 |
+
{%- if message.role == "user" %}
|
| 40 |
+
{{- '<|im_start|>' + message.role + '\n' }}
|
| 41 |
+
{%- if message.content is string %}
|
| 42 |
+
{{- message.content }}
|
| 43 |
+
{%- else %}
|
| 44 |
+
{%- for content in message.content %}
|
| 45 |
+
{%- if content.type == 'image' or 'image' in content or 'image_url' in content %}
|
| 46 |
+
{%- set image_count.value = image_count.value + 1 %}
|
| 47 |
+
{%- if add_vision_id %}Picture {{ image_count.value }}: {% endif -%}
|
| 48 |
+
<|vision_start|><|image_pad|><|vision_end|>
|
| 49 |
+
{%- elif content.type == 'video' or 'video' in content %}
|
| 50 |
+
{%- set video_count.value = video_count.value + 1 %}
|
| 51 |
+
{%- if add_vision_id %}Video {{ video_count.value }}: {% endif -%}
|
| 52 |
+
<|vision_start|><|video_pad|><|vision_end|>
|
| 53 |
+
{%- elif 'text' in content %}
|
| 54 |
+
{{- content.text }}
|
| 55 |
+
{%- endif %}
|
| 56 |
+
{%- endfor %}
|
| 57 |
+
{%- endif %}
|
| 58 |
+
{{- '<|im_end|>\n' }}
|
| 59 |
+
{%- elif message.role == "assistant" %}
|
| 60 |
+
{{- '<|im_start|>' + message.role + '\n' }}
|
| 61 |
+
{%- if message.content is string %}
|
| 62 |
+
{{- message.content }}
|
| 63 |
+
{%- else %}
|
| 64 |
+
{%- for content_item in message.content %}
|
| 65 |
+
{%- if 'text' in content_item %}
|
| 66 |
+
{{- content_item.text }}
|
| 67 |
+
{%- endif %}
|
| 68 |
+
{%- endfor %}
|
| 69 |
+
{%- endif %}
|
| 70 |
+
{%- if message.tool_calls %}
|
| 71 |
+
{%- for tool_call in message.tool_calls %}
|
| 72 |
+
{%- if (loop.first and message.content) or (not loop.first) %}
|
| 73 |
+
{{- '\n' }}
|
| 74 |
+
{%- endif %}
|
| 75 |
+
{%- if tool_call.function %}
|
| 76 |
+
{%- set tool_call = tool_call.function %}
|
| 77 |
+
{%- endif %}
|
| 78 |
+
{{- '<tool_call>\n{"name": "' }}
|
| 79 |
+
{{- tool_call.name }}
|
| 80 |
+
{{- '", "arguments": ' }}
|
| 81 |
+
{%- if tool_call.arguments is string %}
|
| 82 |
+
{{- tool_call.arguments }}
|
| 83 |
+
{%- else %}
|
| 84 |
+
{{- tool_call.arguments | tojson }}
|
| 85 |
+
{%- endif %}
|
| 86 |
+
{{- '}\n</tool_call>' }}
|
| 87 |
+
{%- endfor %}
|
| 88 |
+
{%- endif %}
|
| 89 |
+
{{- '<|im_end|>\n' }}
|
| 90 |
+
{%- elif message.role == "tool" %}
|
| 91 |
+
{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
|
| 92 |
+
{{- '<|im_start|>user' }}
|
| 93 |
+
{%- endif %}
|
| 94 |
+
{{- '\n<tool_response>\n' }}
|
| 95 |
+
{%- if message.content is string %}
|
| 96 |
+
{{- message.content }}
|
| 97 |
+
{%- else %}
|
| 98 |
+
{%- for content in message.content %}
|
| 99 |
+
{%- if content.type == 'image' or 'image' in content or 'image_url' in content %}
|
| 100 |
+
{%- set image_count.value = image_count.value + 1 %}
|
| 101 |
+
{%- if add_vision_id %}Picture {{ image_count.value }}: {% endif -%}
|
| 102 |
+
<|vision_start|><|image_pad|><|vision_end|>
|
| 103 |
+
{%- elif content.type == 'video' or 'video' in content %}
|
| 104 |
+
{%- set video_count.value = video_count.value + 1 %}
|
| 105 |
+
{%- if add_vision_id %}Video {{ video_count.value }}: {% endif -%}
|
| 106 |
+
<|vision_start|><|video_pad|><|vision_end|>
|
| 107 |
+
{%- elif 'text' in content %}
|
| 108 |
+
{{- content.text }}
|
| 109 |
+
{%- endif %}
|
| 110 |
+
{%- endfor %}
|
| 111 |
+
{%- endif %}
|
| 112 |
+
{{- '\n</tool_response>' }}
|
| 113 |
+
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
|
| 114 |
+
{{- '<|im_end|>\n' }}
|
| 115 |
+
{%- endif %}
|
| 116 |
+
{%- endif %}
|
| 117 |
+
{%- endfor %}
|
| 118 |
+
{%- if add_generation_prompt %}
|
| 119 |
+
{{- '<|im_start|>assistant\n' }}
|
| 120 |
+
{%- endif %}
|
config.json
ADDED
|
@@ -0,0 +1,64 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"architectures": [
|
| 3 |
+
"Qwen3VLForConditionalGeneration"
|
| 4 |
+
],
|
| 5 |
+
"dtype": "bfloat16",
|
| 6 |
+
"image_token_id": 151655,
|
| 7 |
+
"model_type": "qwen3_vl",
|
| 8 |
+
"text_config": {
|
| 9 |
+
"attention_bias": false,
|
| 10 |
+
"attention_dropout": 0.0,
|
| 11 |
+
"bos_token_id": 151643,
|
| 12 |
+
"dtype": "bfloat16",
|
| 13 |
+
"eos_token_id": 151645,
|
| 14 |
+
"head_dim": 128,
|
| 15 |
+
"hidden_act": "silu",
|
| 16 |
+
"hidden_size": 4096,
|
| 17 |
+
"initializer_range": 0.02,
|
| 18 |
+
"intermediate_size": 12288,
|
| 19 |
+
"max_position_embeddings": 262144,
|
| 20 |
+
"model_type": "qwen3_vl_text",
|
| 21 |
+
"num_attention_heads": 32,
|
| 22 |
+
"num_hidden_layers": 36,
|
| 23 |
+
"num_key_value_heads": 8,
|
| 24 |
+
"rms_norm_eps": 1e-06,
|
| 25 |
+
"rope_scaling": {
|
| 26 |
+
"mrope_interleaved": true,
|
| 27 |
+
"mrope_section": [
|
| 28 |
+
24,
|
| 29 |
+
20,
|
| 30 |
+
20
|
| 31 |
+
],
|
| 32 |
+
"rope_type": "default"
|
| 33 |
+
},
|
| 34 |
+
"rope_theta": 5000000,
|
| 35 |
+
"use_cache": true,
|
| 36 |
+
"vocab_size": 151936
|
| 37 |
+
},
|
| 38 |
+
"tie_word_embeddings": false,
|
| 39 |
+
"transformers_version": "4.57.1",
|
| 40 |
+
"video_token_id": 151656,
|
| 41 |
+
"vision_config": {
|
| 42 |
+
"deepstack_visual_indexes": [
|
| 43 |
+
8,
|
| 44 |
+
16,
|
| 45 |
+
24
|
| 46 |
+
],
|
| 47 |
+
"depth": 27,
|
| 48 |
+
"dtype": "bfloat16",
|
| 49 |
+
"hidden_act": "gelu_pytorch_tanh",
|
| 50 |
+
"hidden_size": 1152,
|
| 51 |
+
"in_channels": 3,
|
| 52 |
+
"initializer_range": 0.02,
|
| 53 |
+
"intermediate_size": 4304,
|
| 54 |
+
"model_type": "qwen3_vl",
|
| 55 |
+
"num_heads": 16,
|
| 56 |
+
"num_position_embeddings": 2304,
|
| 57 |
+
"out_hidden_size": 4096,
|
| 58 |
+
"patch_size": 16,
|
| 59 |
+
"spatial_merge_size": 2,
|
| 60 |
+
"temporal_patch_size": 2
|
| 61 |
+
},
|
| 62 |
+
"vision_end_token_id": 151653,
|
| 63 |
+
"vision_start_token_id": 151652
|
| 64 |
+
}
|
generation_config.json
ADDED
|
@@ -0,0 +1,13 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"bos_token_id": 151643,
|
| 3 |
+
"do_sample": true,
|
| 4 |
+
"eos_token_id": [
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|
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|
| 11 |
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|
| 12 |
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"transformers_version": "4.57.1"
|
| 13 |
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}
|
merges.txt
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|
| 1 |
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{
|
| 2 |
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"metadata": {
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| 3 |
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"total_parameters": 8767123696,
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| 4 |
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| 757 |
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|
| 758 |
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preprocessor_config.json
ADDED
|
@@ -0,0 +1,39 @@
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|
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|
|
|
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|
| 1 |
+
{
|
| 2 |
+
"crop_size": null,
|
| 3 |
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"data_format": "channels_first",
|
| 4 |
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|
| 5 |
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"device": null,
|
| 6 |
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"disable_grouping": null,
|
| 7 |
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|
| 8 |
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|
| 9 |
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"do_normalize": true,
|
| 10 |
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|
| 11 |
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"do_rescale": true,
|
| 12 |
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"do_resize": true,
|
| 13 |
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"image_mean": [
|
| 14 |
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0.5,
|
| 15 |
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0.5,
|
| 16 |
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0.5
|
| 17 |
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],
|
| 18 |
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"image_processor_type": "Qwen2VLImageProcessorFast",
|
| 19 |
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"image_std": [
|
| 20 |
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0.5,
|
| 21 |
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0.5,
|
| 22 |
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0.5
|
| 23 |
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],
|
| 24 |
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|
| 25 |
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"max_pixels": null,
|
| 26 |
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"merge_size": 2,
|
| 27 |
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"min_pixels": null,
|
| 28 |
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"pad_size": null,
|
| 29 |
+
"patch_size": 16,
|
| 30 |
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"processor_class": "Qwen3VLProcessor",
|
| 31 |
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"resample": 3,
|
| 32 |
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"rescale_factor": 0.00392156862745098,
|
| 33 |
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"return_tensors": null,
|
| 34 |
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"size": {
|
| 35 |
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"longest_edge": 16777216,
|
| 36 |
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"shortest_edge": 65536
|
| 37 |
+
},
|
| 38 |
+
"temporal_patch_size": 2
|
| 39 |
+
}
|
special_tokens_map.json
ADDED
|
@@ -0,0 +1,31 @@
|
|
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|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"additional_special_tokens": [
|
| 3 |
+
"<|im_start|>",
|
| 4 |
+
"<|im_end|>",
|
| 5 |
+
"<|object_ref_start|>",
|
| 6 |
+
"<|object_ref_end|>",
|
| 7 |
+
"<|box_start|>",
|
| 8 |
+
"<|box_end|>",
|
| 9 |
+
"<|quad_start|>",
|
| 10 |
+
"<|quad_end|>",
|
| 11 |
+
"<|vision_start|>",
|
| 12 |
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"<|vision_end|>",
|
| 13 |
+
"<|vision_pad|>",
|
| 14 |
+
"<|image_pad|>",
|
| 15 |
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"<|video_pad|>"
|
| 16 |
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],
|
| 17 |
+
"eos_token": {
|
| 18 |
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"content": "<|im_end|>",
|
| 19 |
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"lstrip": false,
|
| 20 |
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"normalized": false,
|
| 21 |
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|
| 22 |
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|
| 23 |
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},
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| 24 |
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| 25 |
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| 26 |
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|
| 27 |
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|
| 28 |
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"rstrip": false,
|
| 29 |
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"single_word": false
|
| 30 |
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}
|
| 31 |
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|
tokenizer.json
ADDED
|
@@ -0,0 +1,3 @@
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|
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|
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|
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|
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|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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| 2 |
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oid sha256:aeb13307a71acd8fe81861d94ad54ab689df773318809eed3cbe794b4492dae4
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| 3 |
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size 11422654
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tokenizer_config.json
ADDED
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@@ -0,0 +1,240 @@
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|
| 1 |
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|
| 3 |
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|
| 4 |
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|
| 5 |
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|
| 6 |
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| 7 |
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|
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|
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|
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|
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|
| 12 |
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|
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|
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|
| 16 |
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|
| 17 |
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|
| 18 |
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|
| 19 |
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|
| 20 |
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| 22 |
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|
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| 27 |
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|
| 28 |
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|
| 30 |
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| 31 |
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|
| 32 |
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|
| 33 |
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|
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|
| 35 |
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|
| 36 |
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| 37 |
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|
| 38 |
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|
| 39 |
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| 41 |
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|
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| 43 |
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|
| 44 |
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"single_word": false,
|
| 51 |
+
"special": true
|
| 52 |
+
},
|
| 53 |
+
"151649": {
|
| 54 |
+
"content": "<|box_end|>",
|
| 55 |
+
"lstrip": false,
|
| 56 |
+
"normalized": false,
|
| 57 |
+
"rstrip": false,
|
| 58 |
+
"single_word": false,
|
| 59 |
+
"special": true
|
| 60 |
+
},
|
| 61 |
+
"151650": {
|
| 62 |
+
"content": "<|quad_start|>",
|
| 63 |
+
"lstrip": false,
|
| 64 |
+
"normalized": false,
|
| 65 |
+
"rstrip": false,
|
| 66 |
+
"single_word": false,
|
| 67 |
+
"special": true
|
| 68 |
+
},
|
| 69 |
+
"151651": {
|
| 70 |
+
"content": "<|quad_end|>",
|
| 71 |
+
"lstrip": false,
|
| 72 |
+
"normalized": false,
|
| 73 |
+
"rstrip": false,
|
| 74 |
+
"single_word": false,
|
| 75 |
+
"special": true
|
| 76 |
+
},
|
| 77 |
+
"151652": {
|
| 78 |
+
"content": "<|vision_start|>",
|
| 79 |
+
"lstrip": false,
|
| 80 |
+
"normalized": false,
|
| 81 |
+
"rstrip": false,
|
| 82 |
+
"single_word": false,
|
| 83 |
+
"special": true
|
| 84 |
+
},
|
| 85 |
+
"151653": {
|
| 86 |
+
"content": "<|vision_end|>",
|
| 87 |
+
"lstrip": false,
|
| 88 |
+
"normalized": false,
|
| 89 |
+
"rstrip": false,
|
| 90 |
+
"single_word": false,
|
| 91 |
+
"special": true
|
| 92 |
+
},
|
| 93 |
+
"151654": {
|
| 94 |
+
"content": "<|vision_pad|>",
|
| 95 |
+
"lstrip": false,
|
| 96 |
+
"normalized": false,
|
| 97 |
+
"rstrip": false,
|
| 98 |
+
"single_word": false,
|
| 99 |
+
"special": true
|
| 100 |
+
},
|
| 101 |
+
"151655": {
|
| 102 |
+
"content": "<|image_pad|>",
|
| 103 |
+
"lstrip": false,
|
| 104 |
+
"normalized": false,
|
| 105 |
+
"rstrip": false,
|
| 106 |
+
"single_word": false,
|
| 107 |
+
"special": true
|
| 108 |
+
},
|
| 109 |
+
"151656": {
|
| 110 |
+
"content": "<|video_pad|>",
|
| 111 |
+
"lstrip": false,
|
| 112 |
+
"normalized": false,
|
| 113 |
+
"rstrip": false,
|
| 114 |
+
"single_word": false,
|
| 115 |
+
"special": true
|
| 116 |
+
},
|
| 117 |
+
"151657": {
|
| 118 |
+
"content": "<tool_call>",
|
| 119 |
+
"lstrip": false,
|
| 120 |
+
"normalized": false,
|
| 121 |
+
"rstrip": false,
|
| 122 |
+
"single_word": false,
|
| 123 |
+
"special": false
|
| 124 |
+
},
|
| 125 |
+
"151658": {
|
| 126 |
+
"content": "</tool_call>",
|
| 127 |
+
"lstrip": false,
|
| 128 |
+
"normalized": false,
|
| 129 |
+
"rstrip": false,
|
| 130 |
+
"single_word": false,
|
| 131 |
+
"special": false
|
| 132 |
+
},
|
| 133 |
+
"151659": {
|
| 134 |
+
"content": "<|fim_prefix|>",
|
| 135 |
+
"lstrip": false,
|
| 136 |
+
"normalized": false,
|
| 137 |
+
"rstrip": false,
|
| 138 |
+
"single_word": false,
|
| 139 |
+
"special": false
|
| 140 |
+
},
|
| 141 |
+
"151660": {
|
| 142 |
+
"content": "<|fim_middle|>",
|
| 143 |
+
"lstrip": false,
|
| 144 |
+
"normalized": false,
|
| 145 |
+
"rstrip": false,
|
| 146 |
+
"single_word": false,
|
| 147 |
+
"special": false
|
| 148 |
+
},
|
| 149 |
+
"151661": {
|
| 150 |
+
"content": "<|fim_suffix|>",
|
| 151 |
+
"lstrip": false,
|
| 152 |
+
"normalized": false,
|
| 153 |
+
"rstrip": false,
|
| 154 |
+
"single_word": false,
|
| 155 |
+
"special": false
|
| 156 |
+
},
|
| 157 |
+
"151662": {
|
| 158 |
+
"content": "<|fim_pad|>",
|
| 159 |
+
"lstrip": false,
|
| 160 |
+
"normalized": false,
|
| 161 |
+
"rstrip": false,
|
| 162 |
+
"single_word": false,
|
| 163 |
+
"special": false
|
| 164 |
+
},
|
| 165 |
+
"151663": {
|
| 166 |
+
"content": "<|repo_name|>",
|
| 167 |
+
"lstrip": false,
|
| 168 |
+
"normalized": false,
|
| 169 |
+
"rstrip": false,
|
| 170 |
+
"single_word": false,
|
| 171 |
+
"special": false
|
| 172 |
+
},
|
| 173 |
+
"151664": {
|
| 174 |
+
"content": "<|file_sep|>",
|
| 175 |
+
"lstrip": false,
|
| 176 |
+
"normalized": false,
|
| 177 |
+
"rstrip": false,
|
| 178 |
+
"single_word": false,
|
| 179 |
+
"special": false
|
| 180 |
+
},
|
| 181 |
+
"151665": {
|
| 182 |
+
"content": "<tool_response>",
|
| 183 |
+
"lstrip": false,
|
| 184 |
+
"normalized": false,
|
| 185 |
+
"rstrip": false,
|
| 186 |
+
"single_word": false,
|
| 187 |
+
"special": false
|
| 188 |
+
},
|
| 189 |
+
"151666": {
|
| 190 |
+
"content": "</tool_response>",
|
| 191 |
+
"lstrip": false,
|
| 192 |
+
"normalized": false,
|
| 193 |
+
"rstrip": false,
|
| 194 |
+
"single_word": false,
|
| 195 |
+
"special": false
|
| 196 |
+
},
|
| 197 |
+
"151667": {
|
| 198 |
+
"content": "<think>",
|
| 199 |
+
"lstrip": false,
|
| 200 |
+
"normalized": false,
|
| 201 |
+
"rstrip": false,
|
| 202 |
+
"single_word": false,
|
| 203 |
+
"special": false
|
| 204 |
+
},
|
| 205 |
+
"151668": {
|
| 206 |
+
"content": "</think>",
|
| 207 |
+
"lstrip": false,
|
| 208 |
+
"normalized": false,
|
| 209 |
+
"rstrip": false,
|
| 210 |
+
"single_word": false,
|
| 211 |
+
"special": false
|
| 212 |
+
}
|
| 213 |
+
},
|
| 214 |
+
"additional_special_tokens": [
|
| 215 |
+
"<|im_start|>",
|
| 216 |
+
"<|im_end|>",
|
| 217 |
+
"<|object_ref_start|>",
|
| 218 |
+
"<|object_ref_end|>",
|
| 219 |
+
"<|box_start|>",
|
| 220 |
+
"<|box_end|>",
|
| 221 |
+
"<|quad_start|>",
|
| 222 |
+
"<|quad_end|>",
|
| 223 |
+
"<|vision_start|>",
|
| 224 |
+
"<|vision_end|>",
|
| 225 |
+
"<|vision_pad|>",
|
| 226 |
+
"<|image_pad|>",
|
| 227 |
+
"<|video_pad|>"
|
| 228 |
+
],
|
| 229 |
+
"bos_token": null,
|
| 230 |
+
"clean_up_tokenization_spaces": false,
|
| 231 |
+
"eos_token": "<|im_end|>",
|
| 232 |
+
"errors": "replace",
|
| 233 |
+
"extra_special_tokens": {},
|
| 234 |
+
"model_max_length": 262144,
|
| 235 |
+
"pad_token": "<|endoftext|>",
|
| 236 |
+
"processor_class": "Qwen3VLProcessor",
|
| 237 |
+
"split_special_tokens": false,
|
| 238 |
+
"tokenizer_class": "Qwen2Tokenizer",
|
| 239 |
+
"unk_token": null
|
| 240 |
+
}
|
video_preprocessor_config.json
ADDED
|
@@ -0,0 +1,41 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"crop_size": null,
|
| 3 |
+
"data_format": "channels_first",
|
| 4 |
+
"default_to_square": true,
|
| 5 |
+
"device": null,
|
| 6 |
+
"do_center_crop": null,
|
| 7 |
+
"do_convert_rgb": true,
|
| 8 |
+
"do_normalize": true,
|
| 9 |
+
"do_rescale": true,
|
| 10 |
+
"do_resize": true,
|
| 11 |
+
"do_sample_frames": true,
|
| 12 |
+
"fps": 2,
|
| 13 |
+
"image_mean": [
|
| 14 |
+
0.5,
|
| 15 |
+
0.5,
|
| 16 |
+
0.5
|
| 17 |
+
],
|
| 18 |
+
"image_std": [
|
| 19 |
+
0.5,
|
| 20 |
+
0.5,
|
| 21 |
+
0.5
|
| 22 |
+
],
|
| 23 |
+
"input_data_format": null,
|
| 24 |
+
"max_frames": 768,
|
| 25 |
+
"merge_size": 2,
|
| 26 |
+
"min_frames": 4,
|
| 27 |
+
"num_frames": null,
|
| 28 |
+
"pad_size": null,
|
| 29 |
+
"patch_size": 16,
|
| 30 |
+
"processor_class": "Qwen3VLProcessor",
|
| 31 |
+
"resample": 3,
|
| 32 |
+
"rescale_factor": 0.00392156862745098,
|
| 33 |
+
"return_metadata": false,
|
| 34 |
+
"size": {
|
| 35 |
+
"longest_edge": 25165824,
|
| 36 |
+
"shortest_edge": 4096
|
| 37 |
+
},
|
| 38 |
+
"temporal_patch_size": 2,
|
| 39 |
+
"video_metadata": null,
|
| 40 |
+
"video_processor_type": "Qwen3VLVideoProcessor"
|
| 41 |
+
}
|
vocab.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|