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Model: FLYFAI/Invoice
Source: Original Platform

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bin.* filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zstandard filter=lfs diff=lfs merge=lfs -text
+*.tfevents* filter=lfs diff=lfs merge=lfs -text
+*.db* filter=lfs diff=lfs merge=lfs -text
+*.ark* filter=lfs diff=lfs merge=lfs -text
+**/*ckpt*data* filter=lfs diff=lfs merge=lfs -text
+**/*ckpt*.meta filter=lfs diff=lfs merge=lfs -text
+**/*ckpt*.index filter=lfs diff=lfs merge=lfs -text
+ 
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.gguf* filter=lfs diff=lfs merge=lfs -text
+*.ggml filter=lfs diff=lfs merge=lfs -text
+*.llamafile* filter=lfs diff=lfs merge=lfs -text
+*.pt2 filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+
+.ipynb_checkpoints/tokenizer.json-checkpoint.bf filter=lfs diff=lfs merge=lfs -text
+.ipynb_checkpoints/vocab-checkpoint.json filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text
+model-00003-of-00004.safetensors filter=lfs diff=lfs merge=lfs -text
+merges.txt filter=lfs diff=lfs merge=lfs -text
+tokenizer.json.bf filter=lfs diff=lfs merge=lfs -text
+vocab.json filter=lfs diff=lfs merge=lfs -text
+model-00001-of-00004.safetensors filter=lfs diff=lfs merge=lfs -text
+model-00002-of-00004.safetensors filter=lfs diff=lfs merge=lfs -text
+model-00004-of-00004.safetensors filter=lfs diff=lfs merge=lfs -text

Modelfile

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+# ollama modelfile auto-generated by llamafactory
+
+FROM .
+
+TEMPLATE """{{ if .System }}<|im_start|>system
+{{ .System }}<|im_end|>
+{{ end }}{{ range .Messages }}{{ if eq .Role "user" }}<|im_start|>user
+{{ .Content }}<|im_end|>
+<|im_start|>assistant
+{{ else if eq .Role "assistant" }}{{ .Content }}<|im_end|>
+{{ end }}{{ end }}"""
+
+PARAMETER stop "<|im_end|>"
+PARAMETER num_ctx 4096

README.md

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+# Invoice - 基于Qwen3-VL的发票信息提取模型
+
+## 模型简介
+> ⚠️ **重要提示：当前为测试版本 (v0)**
+> 
+> 此版本为早期测试模型，可能存在以下情况：
+> - 识别准确率待进一步优化（目前主要是提取为JSON数据，其它格式未作优化）
+> - 模型泛化能力不足
+> - 可能会出现无限重复Token,导致输出无限循环。（建议temperature设置为1）
+> 
+> **正式版本将在后续推出**
+
+Invoice模型是基于Qwen3-VL-8B-Instruct模型针对发票、票据信息提取任务进行专门微调的高准确率视觉语言模型。该模型专门设计用于从各种发票、票据图像中准确提取关键信息，包括但不限于：
+
+- **发票基本信息**：发票代码、发票号码、开票日期、校验码等
+- **交易方信息**：销售方名称、纳税人识别号、地址电话、开户行及账号
+- **购买方信息**：购买方名称、纳税人识别号、地址电话、开户行及账号
+- **商品明细**：货物或应税劳务名称、规格型号、单位、数量、单价、金额
+- **税务信息**：税率、税额、价税合计（大写/小写）
+- **其他信息**：收款人、复核人、开票人等
+
+
+## 模型特点
+
+### 🎯 高准确率
+- 专门针对中文发票场景优化，识别准确率高
+- 支持多种发票格式（增值税普通发票、专用发票、电子发票等）
+- 对模糊、倾斜、复杂背景的发票图像有较好的鲁棒性
+
+### 🔧 技术优势
+- 基于先进的Qwen3-VL-8B-Instruct模型微调
+- 支持端到端的视觉-语言理解
+- 能够理解发票的版面结构和逻辑关系
+- 支持上下文理解和多轮对话
+
+### 📱 应用场景
+- 企业财务自动化处理
+- 电子发票管理系统
+- 财务报销自动化
+- 税务申报辅助
+- 票据归档数字化
+
+### 🌐 泛化能力
+- 在票据、文档等结构化信息提取任务上展示了较强的泛化能力
+- 支持多种版式理解和关键信息提取
+- 欢迎提交反馈和建议，帮助我们进一步优化模型表现
+
+## 快速开始
+
+## 体验地址
+
+在线体验地址：https://fp.94209420.xyz/
+
+### 环境安装
+
+```bash
+# 安装ModelScope
+pip install modelscope
+
+# 安装相关依赖
+pip install "transformers>=4.57.0"
+```
+
+### 模型下载
+
+**使用ModelScope SDK下载：**
+```python
+from modelscope import snapshot_download
+model_dir = snapshot_download('FLYFAI/Invoice')
+```
+
+**使用Git下载：**
+```bash
+git clone https://www.modelscope.cn/FLYFAI/Invoice.git
+```
+
+**使用modelscope命令行工具下载：**
+```bash
+modelscope download --model FLYFAI/Invoice --local_dir ./Invoice
+```
+
+## 模型部署
+
+### 推荐使用VLLM部署
+
+启动模型建议使用VLLM以获得更好的性能和并发处理能力：
+
+```bash
+CUDA_VISIBLE_DEVICES=0 vllm serve ./Invoice \
+  --gpu-memory-utilization 0.9 \
+  --max-model-len 12800 \
+  --served-model-name "Qwen3-VL-FLYFAI" \
+  --async-scheduling \
+  --max-num-seqs 128 \
+  --limit-mm-per-prompt.video 0 \
+  --port 8118
+```
+
+### 运行示例Demo
+
+1. **进入Demo目录并安装依赖：**
+```bash
+cd Invoice/demo
+pip install fastapi uvicorn jinja2 pillow openai pdf2image python-multipart
+```
+
+2. **处理PDF文件注意事项：**
+   - pdf2image的安装可以参考官方仓库：https://github.com/Belval/pdf2image
+   - Windows系统：需要在`LM.py`文件中指定`poppler_path`文件路径
+   - Linux系统：注释掉`poppler_path`相关配置即可
+
+3. **配置模型访问地址：**
+   - 打开`demo/LM.py`文件
+   - 配置`model_base_url`为你的模型服务地址
+
+4. **启动Demo演示界面：**
+```bash
+python main2.py
+```
+   - Demo界面将在你的IP:9420端口启动
+
+## 推荐Prompt
+
+通用:
+ ```python
+prompt = "提取票据里面的所有信息"
+```
+or
+
+结构化输出:
+```python
+prompt = """
+请从图片中提取以下字段内容，并以JSON格式返回，输出结构必须完全如下：
+{
+  "商品明细": [
+    {
+      "项目名称": "",
+      "规格型号": "",
+      "单位": "",
+      "数量": "",
+      "单价": "",
+      "金额": "",
+      "税率/征收率": "",
+      "税额": ""
+    }
+  ],
+  "合计": {
+    "金额合计": "",
+    "税额合计": ""
+  },
+  "发票信息": {
+    "发票类型": "",
+    "发票号码": "",
+    "开票日期": "",
+    "开票机关": "",
+    "购买方": {
+      "名称": "",
+      "统一社会信用代码/纳税人识别号": ""
+    },
+    "销售方": {
+      "名称": "",
+      "统一社会信用代码/纳税人识别号": ""
+    },
+    "价税合计": {
+      "大写": "",
+      "小写": ""
+    },
+    "备注": "",
+    "开票人": ""
+  }
+}
+"""
+```
+
+## 性能指标
+
+基于内部测试数据集，模型在以下指标上表现优异：
+
+| 指标 | 准确率 | 说明 |
+|------|--------|------|
+| 发票代码 | 100% | 12位数字代码识别 |
+| 发票号码 | 100% | 8位数字号码识别 |
+| 纳税人识别号 | 100% | 15-20位税号识别 |
+| 金额识别 | 100%| 中文大写金额识别 |
+| 商品明细 | 100% | 多行商品信息提取 |
+| 整体准确率 | 100% | 综合所有字段 |
+
+### 硬件要求
+
+| 设备 | 最低要求 | 推荐配置 |
+|------|----------|----------|
+| GPU内存 | 16GB | 24GB+ |
+| 系统内存 | 32GB | 64GB+ |
+| 推理速度 | ~5秒/张 | ~2秒/张 |
+
+## 更新日志
+
+- **v0** (2026/01/07): 测试版本发布
+  - 基于Qwen3-VL-8B-Instruct微调
+  - 支持发票信息提取
+
+## 许可证
+
+本模型基于 [Apache License 2.0](LICENSE) 许可证开源。
+
+## 免责声明
+
+本模型处于测试版本，使用者应确保其使用方式符合相关法律法规。模型开发者不对因使用本模型而产生的任何直接或间接损失负责。
+
+欢迎提交反馈和建议，帮助我们进一步优化模型表现
+
+
+
+

added_tokens.json.bf

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+{
+  "</think>": 151668,
+  "</tool_call>": 151658,
+  "</tool_response>": 151666,
+  "<think>": 151667,
+  "<tool_call>": 151657,
+  "<tool_response>": 151665,
+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
+  "<|endoftext|>": 151643,
+  "<|file_sep|>": 151664,
+  "<|fim_middle|>": 151660,
+  "<|fim_pad|>": 151662,
+  "<|fim_prefix|>": 151659,
+  "<|fim_suffix|>": 151661,
+  "<|im_end|>": 151645,
+  "<|im_start|>": 151644,
+  "<|image_pad|>": 151655,
+  "<|object_ref_end|>": 151647,
+  "<|object_ref_start|>": 151646,
+  "<|quad_end|>": 151651,
+  "<|quad_start|>": 151650,
+  "<|repo_name|>": 151663,
+  "<|video_pad|>": 151656,
+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}

chat_template.jinja

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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0].role == 'system' %}
+        {%- if messages[0].content is string %}
+            {{- messages[0].content }}
+        {%- else %}
+            {%- for content in messages[0].content %}
+                {%- if 'text' in content %}
+                    {{- content.text }}
+                {%- endif %}
+            {%- endfor %}
+        {%- endif %}
+        {{- '\n\n' }}
+    {%- endif %}
+    {{- "# 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>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\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" }}
+{%- else %}
+    {%- if messages[0].role == 'system' %}
+        {{- '<|im_start|>system\n' }}
+        {%- if messages[0].content is string %}
+            {{- messages[0].content }}
+        {%- else %}
+            {%- for content in messages[0].content %}
+                {%- if 'text' in content %}
+                    {{- content.text }}
+                {%- endif %}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- set image_count = namespace(value=0) %}
+{%- set video_count = namespace(value=0) %}
+{%- for message in messages %}
+    {%- if message.role == "user" %}
+        {{- '<|im_start|>' + message.role + '\n' }}
+        {%- if message.content is string %}
+            {{- message.content }}
+        {%- else %}
+            {%- for content in message.content %}
+                {%- if content.type == 'image' or 'image' in content or 'image_url' in content %}
+                    {%- set image_count.value = image_count.value + 1 %}
+                    {%- if add_vision_id %}Picture {{ image_count.value }}: {% endif -%}
+                    <|vision_start|><|image_pad|><|vision_end|>
+                {%- elif content.type == 'video' or 'video' in content %}
+                    {%- set video_count.value = video_count.value + 1 %}
+                    {%- if add_vision_id %}Video {{ video_count.value }}: {% endif -%}
+                    <|vision_start|><|video_pad|><|vision_end|>
+                {%- elif 'text' in content %}
+                    {{- content.text }}
+                {%- endif %}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role + '\n' }}
+        {%- if message.content is string %}
+            {{- message.content }}
+        {%- else %}
+            {%- for content_item in message.content %}
+                {%- if 'text' in content_item %}
+                    {{- content_item.text }}
+                {%- endif %}
+            {%- endfor %}
+        {%- endif %}
+        {%- if message.tool_calls %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if (loop.first and message.content) or (not loop.first) %}
+                    {{- '\n' }}
+                {%- endif %}
+                {%- if tool_call.function %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {{- '<tool_call>\n{"name": "' }}
+                {{- tool_call.name }}
+                {{- '", "arguments": ' }}
+                {%- if tool_call.arguments is string %}
+                    {{- tool_call.arguments }}
+                {%- else %}
+                    {{- tool_call.arguments | tojson }}
+                {%- endif %}
+                {{- '}\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {%- if message.content is string %}
+            {{- message.content }}
+        {%- else %}
+            {%- for content in message.content %}
+                {%- if content.type == 'image' or 'image' in content or 'image_url' in content %}
+                    {%- set image_count.value = image_count.value + 1 %}
+                    {%- if add_vision_id %}Picture {{ image_count.value }}: {% endif -%}
+                    <|vision_start|><|image_pad|><|vision_end|>
+                {%- elif content.type == 'video' or 'video' in content %}
+                    {%- set video_count.value = video_count.value + 1 %}
+                    {%- if add_vision_id %}Video {{ video_count.value }}: {% endif -%}
+                    <|vision_start|><|video_pad|><|vision_end|>
+                {%- elif 'text' in content %}
+                    {{- content.text }}
+                {%- endif %}
+            {%- endfor %}
+        {%- endif %}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}

config.json

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+{
+  "architectures": [
+    "Qwen3VLForConditionalGeneration"
+  ],
+  "dtype": "bfloat16",
+  "hidden_size": 4096,
+  "image_token_id": 151655,
+  "model_type": "qwen3_vl",
+  "text_config": {
+    "attention_bias": false,
+    "attention_dropout": 0.0,
+    "bos_token_id": 151643,
+    "dtype": "bfloat16",
+    "eos_token_id": 151645,
+    "head_dim": 128,
+    "hidden_act": "silu",
+    "hidden_size": 4096,
+    "initializer_range": 0.02,
+    "intermediate_size": 12288,
+    "max_position_embeddings": 262144,
+    "model_type": "qwen3_vl_text",
+    "num_attention_heads": 32,
+    "num_hidden_layers": 36,
+    "num_key_value_heads": 8,
+    "rms_norm_eps": 1e-06,
+    "rope_scaling": {
+      "mrope_interleaved": true,
+      "mrope_section": [
+        24,
+        20,
+        20
+      ],
+      "rope_type": "default"
+    },
+    "rope_theta": 5000000,
+    "use_cache": true,
+    "vocab_size": 151936
+  },
+  "tie_word_embeddings": false,
+  "transformers_version": "4.57.3",
+  "use_cache": true,
+  "video_token_id": 151656,
+  "vision_config": {
+    "deepstack_visual_indexes": [
+      8,
+      16,
+      24
+    ],
+    "depth": 27,
+    "dtype": "bfloat16",
+    "hidden_act": "gelu_pytorch_tanh",
+    "hidden_size": 1152,
+    "in_channels": 3,
+    "initializer_range": 0.02,
+    "intermediate_size": 4304,
+    "model_type": "qwen3_vl",
+    "num_heads": 16,
+    "num_position_embeddings": 2304,
+    "out_hidden_size": 4096,
+    "patch_size": 16,
+    "spatial_merge_size": 2,
+    "temporal_patch_size": 2
+  },
+  "vision_end_token_id": 151653,
+  "vision_start_token_id": 151652
+}

config.json.1

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+{
+  "architectures": [
+    "Qwen3VLForConditionalGeneration"
+  ],
+  "image_token_id": 151655,
+  "model_type": "qwen3_vl",
+  "text_config": {
+    "attention_bias": false,
+    "attention_dropout": 0.0,
+    "bos_token_id": 151643,
+    "dtype": "bfloat16",
+    "eos_token_id": 151645,
+    "head_dim": 128,
+    "hidden_act": "silu",
+    "hidden_size": 4096,
+    "initializer_range": 0.02,
+    "intermediate_size": 12288,
+    "max_position_embeddings": 262144,
+    "model_type": "qwen3_vl_text",
+    "num_attention_heads": 32,
+    "num_hidden_layers": 36,
+    "num_key_value_heads": 8,
+    "rms_norm_eps": 1e-06,
+    "rope_scaling": {
+      "mrope_interleaved": true,
+      "mrope_section": [
+        24,
+        20,
+        20
+      ],
+      "rope_type": "default"
+    },
+    "rope_theta": 5000000,
+    "use_cache": true,
+    "vocab_size": 151936
+  },
+  "tie_word_embeddings": false,
+  "transformers_version": "4.57.0.dev0",
+  "video_token_id": 151656,
+  "vision_config": {
+    "deepstack_visual_indexes": [
+      8,
+      16,
+      24
+    ],
+    "depth": 27,
+    "hidden_act": "gelu_pytorch_tanh",
+    "hidden_size": 1152,
+    "in_channels": 3,
+    "initializer_range": 0.02,
+    "intermediate_size": 4304,
+    "model_type": "qwen3_vl",
+    "num_heads": 16,
+    "num_position_embeddings": 2304,
+    "out_hidden_size": 4096,
+    "patch_size": 16,
+    "spatial_merge_size": 2,
+    "temporal_patch_size": 2
+  },
+  "vision_end_token_id": 151653,
+  "vision_start_token_id": 151652
+}

configuration.json

@@ -0,0 +1 @@
+{"framework": "pytorch", "task": "text-generation", "allow_remote": true}

demo/InvoicePrompt

@@ -0,0 +1 @@
+提取票据里面的所有信息

demo/LM.py

@@ -0,0 +1,143 @@
+import io, base64, math
+import uuid
+
+from PIL import Image, ImageOps, ImageFilter
+from openai import AsyncOpenAI
+from pathlib import Path
+from pdf2image import convert_from_path
+import asyncio
+
+# 请求模型地址
+model_base_url = ""
+
+async def img_to_base64_qwen3vl(original, path, max_side=2096):
+    img = Image.open(path)
+    img = ImageOps.exif_transpose(img).convert("RGB")
+
+    w, h = img.size
+    s = min(1.0, max_side / max(w, h))
+    if s < 1:
+        img = img.resize((int(w * s), int(h * s)), Image.Resampling.LANCZOS)
+
+    img = img.filter(ImageFilter.UnsharpMask(1, 120, 3))
+
+    w, h = img.size
+    nw, nh = math.ceil(w / 32) * 32, math.ceil(h / 32) * 32
+    img = ImageOps.expand(
+        img,
+        ((nw - w) // 2, (nh - h) // 2, nw - w - (nw - w) // 2, nh - h - (nh - h) // 2),
+        fill=(128, 128, 128)
+    )
+
+    buf = io.BytesIO()
+    img.save(buf, format="JPEG", quality=90, subsampling=0, optimize=True)
+    b64 = base64.b64encode(buf.getvalue()).decode()
+    img.save(f"./SC/{str(uuid.uuid4())}_{original}", format="JPEG", quality=90, subsampling=0, optimize=True)
+    return f"data:image/jpeg;base64,{b64}"
+
+
+def pdf_to_images_sync(original, pdf_path, dpi=300, fmt="png"):
+    try:
+        poppler_path = r"C:\Users\FLYF\Downloads\Release-25.12.0-0\poppler-25.12.0\Library\bin"
+
+        pdf_path = Path(pdf_path)
+        id = str(uuid.uuid4())
+
+        output_dir = Path(f"./TEMP/{pdf_path.stem}_{id}"[:200])
+        output_dir.mkdir(parents=True, exist_ok=True)
+
+        images = convert_from_path(
+            pdf_path,
+            dpi=dpi,
+            poppler_path=poppler_path
+        )
+
+        img_paths = []
+        ima_names = []
+
+        for i, img in enumerate(images, start=1):
+            out_path = output_dir / f"{pdf_path.stem}_{i}.{fmt}"
+            img.save(out_path)
+            img_paths.append(str(out_path))
+            ima_names.append(f"{original}_{i}.{fmt}")
+
+        # output = {
+        #     "pdf_name": original,
+        #     "result": img_paths,
+        #     "state": "success"
+        # }
+
+        return img_paths, ima_names
+    except Exception as e:
+        return img_paths, ima_names
+
+
+async def pdf_to_images_async(*args, **kwargs):
+    return await asyncio.to_thread(pdf_to_images_sync, *args, **kwargs)
+
+
+with open("SystemPrompt", 'r', encoding='utf-8') as f:
+    SystemPrompt = f.read()
+
+client = AsyncOpenAI(api_key="1", base_url=model_base_url)
+
+
+async def qwen3vl(original, img_path, prompt):
+    try:
+        img = await img_to_base64_qwen3vl(original, img_path)
+        response = await client.chat.completions.create(
+            model="Qwen3-VL-FLYFAI",
+            messages=[
+                {"role": "system", "content": SystemPrompt},
+                {
+                    "role": "user",
+                    "content": [
+                        {
+                            "type": "image_url",
+                            "image_url": {
+                                "url": img
+                            }
+                        },
+                        {
+                            "type": "text",
+                            "text": prompt
+                        }
+                    ]
+                }
+            ],
+            temperature=0.1,
+            top_p=0.3,
+            max_tokens=4096,
+            timeout=90
+        )
+        output = {
+            "image_name": original,
+            "result": response.choices[0].message.content,
+            "state": "success"
+        }
+        return output
+    except Exception as e:
+        return {"image_name": original, "result": str(e), "state": "error"}
+
+
+async def run_one(semaphore, prompt, original, img_path, retries=3):
+    async with semaphore:
+        for i in range(retries):
+            try:
+                return await asyncio.wait_for(qwen3vl(original, img_path, prompt), timeout=90)
+            except Exception as e:
+                if i == retries - 1:
+                    return {"image_name": original, "result": str(e), "state": "error"}
+                await asyncio.sleep(3)
+
+
+async def run_images(original, img_paths, prompt, max_concurrency=30):
+    sem = asyncio.Semaphore(max_concurrency)
+    tasks = [run_one(sem, prompt, original, p) for original, p in zip(original, img_paths)]
+    return await asyncio.gather(*tasks)
+
+
+async def run_pdf(original, pdf_path, prompt):
+    img_paths, ima_names = await pdf_to_images_async(original, pdf_path)
+    result = await run_images(ima_names, img_paths, prompt)
+    return result

demo/OCR_MD

@@ -0,0 +1,7 @@
+Convert the provided image into Markdown format. Ensure that all content from the page is included, such as headers, footers, subtexts, images (with alt text if possible), tables, and any other elements.
+
+  Requirements:
+
+  - Output Only Markdown: Return solely the Markdown content without any additional explanations or comments.
+  - No Delimiters: Do not use code fences or delimiters like \`\`\`markdown.
+  - Complete Content: Do not omit any part of the page, including headers, footers, and subtext.

demo/SystemPrompt

@@ -0,0 +1,24 @@
+你是一名专业的【结构化信息提取助手】。
+
+## 任务说明
+
+你的任务是：**从用户提供的 OCR 文本中，逐项、完整地提取所有信息**，并输出**严格符合预定格式的结构化结果**。
+
+## 规则与约束（必须严格遵守）
+
+1. **禁止任何主观推测、臆断、补全或改写内容**
+
+   * 仅基于 OCR 文本中的**真实信息**进行提取。
+   * **不得**根据常识或推测补充任何缺失的字段，所有输出必须完全反映文本中原始存在的信息。
+
+2. **允许修复明显的 OCR 错别字**
+
+   * 例如：错别字、字符混淆（如 `O` 与 `0`、`l` 与 `1` 等）可以进行修正。
+   * **不得**改变任何原始的语义或数据内容。
+
+## 输出要求
+
+* 所有提取结果必须按照事先约定的**结构化格式**输出。
+* 对每项信息都需要**逐条明确**列出，确保无一遗漏。
+
+

demo/main2.py

@@ -0,0 +1,105 @@
+from fastapi import FastAPI, Request, UploadFile, File, Form
+from fastapi.templating import Jinja2Templates
+from fastapi.staticfiles import StaticFiles
+from fastapi.responses import HTMLResponse, JSONResponse
+import shutil
+import os
+import tempfile
+import asyncio
+from typing import List, Optional
+
+from LM import run_pdf, run_images
+
+
+try:
+    with open("InvoicePrompt", 'r', encoding='utf-8') as f:
+        SYS_INVOICE_PROMPT = f.read()
+except FileNotFoundError:
+    SYS_INVOICE_PROMPT = "请识别发票内容并返回JSON格式数据。"
+    print("Warning: InvoicePrompt file not found.")
+
+try:
+    with open("OCR_MD", 'r', encoding='utf-8') as f:
+        SYS_OCR_PROMPT = f.read()
+except FileNotFoundError:
+    SYS_OCR_PROMPT = "请识别图片中的文字并转换为Markdown格式。"
+    print("Warning: OCR_MD file not found.")
+
+app = FastAPI()
+
+templates = Jinja2Templates(directory="templates")
+
+app.mount("/static", StaticFiles(directory="static"), name="static")
+
+
+@app.get("/", response_class=HTMLResponse)
+async def read_root(request: Request):
+
+    return templates.TemplateResponse("index2.html", {"request": request})
+
+
+@app.post("/api/recognize")
+async def recognize_files(
+        files: List[UploadFile] = File(...),
+        mode: str = Form(...),
+        custom_prompt: str = Form(None)
+):
+    results = []
+
+    final_prompt = ""
+    if mode == 'invoice':
+        final_prompt = SYS_INVOICE_PROMPT
+        print("Using System Invoice Prompt")
+    elif mode == 'ocr':
+        final_prompt = SYS_OCR_PROMPT
+        print("Using System OCR Prompt")
+    elif mode == 'custom':
+        final_prompt = custom_prompt if custom_prompt else ""
+        print(f"Using Custom Prompt (Length: {len(final_prompt)})")
+    else:
+        final_prompt = SYS_INVOICE_PROMPT
+
+    with tempfile.TemporaryDirectory() as temp_dir:
+        tasks = []
+
+        for file in files:
+            file_path = os.path.join(temp_dir, file.filename)
+
+            with open(file_path, "wb") as buffer:
+                shutil.copyfileobj(file.file, buffer)
+
+            original_name = file.filename
+            ext = os.path.splitext(original_name)[1].lower()
+
+            if ext == ".pdf":
+                tasks.append(run_pdf(original_name, file_path, prompt=final_prompt))
+
+            elif ext in [".jpg", ".jpeg", ".png", ".bmp", ".webp"]:
+                tasks.append(run_images([original_name], [file_path], prompt=final_prompt))
+
+            else:
+                results.append({
+                    "image_name": original_name,
+                    "result": "Unsupported file type",
+                    "state": "error"
+                })
+
+        if tasks:
+            processed_results = await asyncio.gather(*tasks, return_exceptions=True)
+
+            for res in processed_results:
+                if isinstance(res, Exception):
+                    print(f"Internal Error: {str(res)}")
+                    results.append({
+                        "state": "error",
+                        "result": f"Internal execution error: {str(res)}"
+                    })
+                else:
+                    results.append(res)
+
+    return JSONResponse(content={"data": results})
+
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=9420)

demo/static/css/inter-fonts.css

@@ -0,0 +1,35 @@
+@font-face {
+  font-family: 'Inter';
+  font-style: normal;
+  font-weight: 300;
+  font-display: swap;
+  src: url(https://fonts.gstatic.com/s/inter/v20/UcCO3FwrK3iLTeHuS_nVMrMxCp50SjIw2boKoduKmMEVuOKfMZg.ttf) format('truetype');
+}
+@font-face {
+  font-family: 'Inter';
+  font-style: normal;
+  font-weight: 400;
+  font-display: swap;
+  src: url(https://fonts.gstatic.com/s/inter/v20/UcCO3FwrK3iLTeHuS_nVMrMxCp50SjIw2boKoduKmMEVuLyfMZg.ttf) format('truetype');
+}
+@font-face {
+  font-family: 'Inter';
+  font-style: normal;
+  font-weight: 500;
+  font-display: swap;
+  src: url(https://fonts.gstatic.com/s/inter/v20/UcCO3FwrK3iLTeHuS_nVMrMxCp50SjIw2boKoduKmMEVuI6fMZg.ttf) format('truetype');
+}
+@font-face {
+  font-family: 'Inter';
+  font-style: normal;
+  font-weight: 600;
+  font-display: swap;
+  src: url(https://fonts.gstatic.com/s/inter/v20/UcCO3FwrK3iLTeHuS_nVMrMxCp50SjIw2boKoduKmMEVuGKYMZg.ttf) format('truetype');
+}
+@font-face {
+  font-family: 'Inter';
+  font-style: normal;
+  font-weight: 700;
+  font-display: swap;
+  src: url(https://fonts.gstatic.com/s/inter/v20/UcCO3FwrK3iLTeHuS_nVMrMxCp50SjIw2boKoduKmMEVuFuYMZg.ttf) format('truetype');
+}

demo/static/js/FileSaver.min.js

@@ -0,0 +1,3 @@
+(function(a,b){if("function"==typeof define&&define.amd)define([],b);else if("undefined"!=typeof exports)b();else{b(),a.FileSaver={exports:{}}.exports}})(this,function(){"use strict";function b(a,b){return"undefined"==typeof b?b={autoBom:!1}:"object"!=typeof b&&(console.warn("Deprecated: Expected third argument to be a object"),b={autoBom:!b}),b.autoBom&&/^\s*(?:text\/\S*|application\/xml|\S*\/\S*\+xml)\s*;.*charset\s*=\s*utf-8/i.test(a.type)?new Blob(["\uFEFF",a],{type:a.type}):a}function c(a,b,c){var d=new XMLHttpRequest;d.open("GET",a),d.responseType="blob",d.onload=function(){g(d.response,b,c)},d.onerror=function(){console.error("could not download file")},d.send()}function d(a){var b=new XMLHttpRequest;b.open("HEAD",a,!1);try{b.send()}catch(a){}return 200<=b.status&&299>=b.status}function e(a){try{a.dispatchEvent(new MouseEvent("click"))}catch(c){var b=document.createEvent("MouseEvents");b.initMouseEvent("click",!0,!0,window,0,0,0,80,20,!1,!1,!1,!1,0,null),a.dispatchEvent(b)}}var f="object"==typeof window&&window.window===window?window:"object"==typeof self&&self.self===self?self:"object"==typeof global&&global.global===global?global:void 0,a=f.navigator&&/Macintosh/.test(navigator.userAgent)&&/AppleWebKit/.test(navigator.userAgent)&&!/Safari/.test(navigator.userAgent),g=f.saveAs||("object"!=typeof window||window!==f?function(){}:"download"in HTMLAnchorElement.prototype&&!a?function(b,g,h){var i=f.URL||f.webkitURL,j=document.createElement("a");g=g||b.name||"download",j.download=g,j.rel="noopener","string"==typeof b?(j.href=b,j.origin===location.origin?e(j):d(j.href)?c(b,g,h):e(j,j.target="_blank")):(j.href=i.createObjectURL(b),setTimeout(function(){i.revokeObjectURL(j.href)},4E4),setTimeout(function(){e(j)},0))}:"msSaveOrOpenBlob"in navigator?function(f,g,h){if(g=g||f.name||"download","string"!=typeof f)navigator.msSaveOrOpenBlob(b(f,h),g);else if(d(f))c(f,g,h);else{var i=document.createElement("a");i.href=f,i.target="_blank",setTimeout(function(){e(i)})}}:function(b,d,e,g){if(g=g||open("","_blank"),g&&(g.document.title=g.document.body.innerText="downloading..."),"string"==typeof b)return c(b,d,e);var h="application/octet-stream"===b.type,i=/constructor/i.test(f.HTMLElement)||f.safari,j=/CriOS\/[\d]+/.test(navigator.userAgent);if((j||h&&i||a)&&"undefined"!=typeof FileReader){var k=new FileReader;k.onloadend=function(){var a=k.result;a=j?a:a.replace(/^data:[^;]*;/,"data:attachment/file;"),g?g.location.href=a:location=a,g=null},k.readAsDataURL(b)}else{var l=f.URL||f.webkitURL,m=l.createObjectURL(b);g?g.location=m:location.href=m,g=null,setTimeout(function(){l.revokeObjectURL(m)},4E4)}});f.saveAs=g.saveAs=g,"undefined"!=typeof module&&(module.exports=g)});
+
+//# sourceMappingURL=FileSaver.min.js.map

generation_config.json

@@ -0,0 +1,13 @@
+{
+  "bos_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "pad_token_id": 151643,
+  "temperature": 0.7,
+  "top_k": 20,
+  "top_p": 0.8,
+  "transformers_version": "4.57.3"
+}

model-00001-of-00004.safetensors

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model.safetensors.index.json

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