---
license: apache-2.0
datasets:
- REILX/text-description-of-the-meme
language:
- zh
tags:
- llava
- Qwen2
- txtimage-to-txt
- lora
---

### 模型 llava-Qwen2-7B-Instruct-CLIP-ZH 增强中文文字识别能力和表情包内涵识别能力，达到gpt4o、claude-3.5-sonnet的能力！！！！！
1. 模型结构：</br>
llava-Qwen2-7B-Instruct-CLIP-ZH = Qwen/Qwen2-7B-Instruct + multi_modal_projector + openai/clip-vit-large-patch14-336</br>

2. 微调模块
- vision_tower和language_model的q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj部分进行lora训练</br>
- mmp层全量训练</br>

3. 微调</br>
参数：lora_r=32,lora_alpha=64,num_train_epochs=5,per_device_train_batch_size=1,gradient_accumulation_steps=8,high_lr=1e-3,low_lr=2e-5,model_max_length=2048.</br>
设备：8*A800</br>
训练时长：5小时12分钟


5. 数据集</br>
使用gemini-1.5-pro, gemini-1.5-flash, yi-vision, gpt4o,claude-3.5-sonnet模型描述emo-visual-data和ChineseBQB数据集。</br>
文本描述信息通过[text-description-of-the-meme](https://huggingface.co/datasets/REILX/text-description-of-the-meme) 下载</br>
图像可通过[emo-visual-data](https://github.com/LLM-Red-Team/emo-visual-data), [ChineseBQB](https://github.com/zhaoolee/ChineseBQB)下载</br>
图片数据总量1.8G，约10835张中文表情包图片。文字总量42Mb，约24332个图像文本对描述信息。

6. 效果展示</br>
以下测试结果显示模型能识别图像中的文字信息，且能正确识别表情包想要表达的内涵。对比REILX/llava-1.5-7b-hf-meme-lora模型中也测试了原始llava-1.5-7b-hf模型的输出，模型无法正确识别图像中的文本信息。
![](./images/llava-qwen2-lora-01.JPG)
![](./images/llava-qwen2-lora-02.JPG)
![](./images/llava-qwen2-lora-03.JPG)

以下三张图为gpt4o的识别效果</br>
![](./images/gpt4o-01.JPG)
![](./images/gpt4o-02.JPG)
![](./images/gpt4o-03.JPG)

5. 代码</br>
合并模型代码，合并模型之后将add_tokens.json,merge.txt,preprocessor_config.json,specital_token_map.json,tokenizer.json,vocab.json文件复制到"/保存的完整模型路径"。
```python
import torch
from peft import PeftModel, LoraConfig
from transformers import LlavaForConditionalGeneration
model_name = "/替换为你的基础模型路径"
LORA_R = 32
LORA_ALPHA = 64
LORA_DROPOUT = 0.05
TARGET_MODULES = ["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"]
lora_config = LoraConfig(
    r=LORA_R,
    lora_alpha=LORA_ALPHA,
    target_modules=TARGET_MODULES,
    lora_dropout=LORA_DROPOUT,
    bias="none",
    task_type="CAUSAL_LM",
    modules_to_save=["multi_modal_projector"],
) 
model = LlavaForConditionalGeneration.from_pretrained(model_name)
model = PeftModel.from_pretrained(model, "/替换为你的lora模型路径", config=lora_config, adapter_name='lora')

model.cpu()
model.eval()
base_model = model.get_base_model()
base_model.eval()
model.merge_and_unload()
base_model.save_pretrained("/保存的完整模型路径")
```

推理代码
```python
from transformers import LlavaForConditionalGeneration, AutoProcessor
import torch
from PIL import Image

raw_model_name_or_path = "/保存的完整模型路径"
model = LlavaForConditionalGeneration.from_pretrained(raw_model_name_or_path, device_map="cuda:0", torch_dtype=torch.bfloat16)
processor = AutoProcessor.from_pretrained(raw_model_name_or_path)
model.eval()

def build_model_input(model, processor):
    messages = [
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "<image>\n 使用中文描述图片中的信息"}
    ]
    prompt = processor.tokenizer.apply_chat_template(
        messages, tokenize=False, add_generation_prompt=True
    )
    image = Image.open("01.PNG")
    inputs = processor(text=prompt, images=image, return_tensors="pt", return_token_type_ids=False)
    
    for tk in inputs.keys():
        inputs[tk] = inputs[tk].to(model.device)
    generate_ids = model.generate(**inputs, max_new_tokens=200)
    
    generate_ids = [
        oid[len(iids):] for oid, iids in zip(generate_ids, inputs.input_ids)
    ]
    gen_text = processor.batch_decode(generate_ids, skip_special_tokens=False, clean_up_tokenization_spaces=False)[0]
    return gen_text
build_model_input(model, processor)
```