Instructions to use REILX/llava-Qwen2-7B-Instruct-Chinese-CLIP-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use REILX/llava-Qwen2-7B-Instruct-Chinese-CLIP-v2 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="REILX/llava-Qwen2-7B-Instruct-Chinese-CLIP-v2")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("REILX/llava-Qwen2-7B-Instruct-Chinese-CLIP-v2")
model = AutoModelForImageTextToText.from_pretrained("REILX/llava-Qwen2-7B-Instruct-Chinese-CLIP-v2")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use REILX/llava-Qwen2-7B-Instruct-Chinese-CLIP-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "REILX/llava-Qwen2-7B-Instruct-Chinese-CLIP-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "REILX/llava-Qwen2-7B-Instruct-Chinese-CLIP-v2",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/REILX/llava-Qwen2-7B-Instruct-Chinese-CLIP-v2

SGLang

How to use REILX/llava-Qwen2-7B-Instruct-Chinese-CLIP-v2 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "REILX/llava-Qwen2-7B-Instruct-Chinese-CLIP-v2" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "REILX/llava-Qwen2-7B-Instruct-Chinese-CLIP-v2",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "REILX/llava-Qwen2-7B-Instruct-Chinese-CLIP-v2" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "REILX/llava-Qwen2-7B-Instruct-Chinese-CLIP-v2",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use REILX/llava-Qwen2-7B-Instruct-Chinese-CLIP-v2 with Docker Model Runner:
```
docker model run hf.co/REILX/llava-Qwen2-7B-Instruct-Chinese-CLIP-v2
```

模型 llava-Qwen2-7B-Instruct-Chinese-CLIP-v2 扩大中文图文训练数据集，增强中文文字识别能力和表情包内涵识别能力

模型结构：
llava-Qwen2-7B-Instruct-Chinese-CLIP = Qwen/Qwen2-7B-Instruct + multi_modal_projector + OFA-Sys/chinese-clip-vit-large-patch14-336px
微调模块

vision_tower和language_model的q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj模块进行lora训练
mmp层全量训练

微调参数

lora_r=32,lora_alpha=64,num_train_epochs=3,per_device_train_batch_size=1,gradient_accumulation_steps=8,high_lr=5e-4,low_lr=1e-5,model_max_length=2048.
训练时长：68小时06分钟

数据集
训练数据集对比上一代模型REILX/llava-Qwen2-7B-Instruct-Chinese-CLIP，扩大至四种中文数据集，图片总数扩大47倍，文字总数扩大5倍。
包括以下：

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。
文本描述信息通过REILX/chinese-meme-description-dataset 下载
图像可通过emo-visual-data, ChineseBQB下载
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。
priyank-m/chinese_text_recognition
图片数据总量2.0Gb，约500000张图片。文字总量207Mb，约500000个图像文本对描述信息。
SWHL/ChineseOCRBench
图片数据总量134Mb，约3410张图片。文字总量1.3Mb，约3410个图像文本对描述信息。
fly0331/ChineseTest
图片数据总量530Mb，约6247张图片。文字总量5.4Mb，约6247个图像文本对描述信息。

为了提升模型对中文文字的识别能力，我们引入了 priyank-m/chinese_text_recognition、SWHL/ChineseOCRBench 和 fly0331/ChineseTest 三个基于中文文字领域图片的数据集。这些数据集的融入将有效弥补 REILX/llava-Qwen2-7B-Instruct-Chinese-CLIP 模型在中文文字识别方面的不足，使其能够更好地理解和处理中文文本信息。

效果展示
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
代码
推理代码

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 你是一位有深度的网络图片解读者，擅长解读和描述网络图片。你能洞察图片中的细微之处，对图中的人物面部表情、文字信息、情绪流露和背景寓意具有超强的理解力，描述信息需要详细。"}
    ]
    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)

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Safetensors

Model size

8B params

Tensor type

F16

Datasets used to train REILX/llava-Qwen2-7B-Instruct-Chinese-CLIP-v2

Collection including REILX/llava-Qwen2-7B-Instruct-Chinese-CLIP-v2

LLava

Collection

lava • 4 items • Updated Aug 2, 2024