语言:
- 韩语
- 英语
许可证: cc-by-nc-sa-4.0
库名称: transformers
Llama3-Chat_Vector-kor_llava
我参考了Beomi(韩语Chat Vector LLAVA模型创建者)和Toshi456(日语Chat Vector LLAVA模型创建者)的模型,实现了一个韩语LLAVA模型。
参考模型:
- beomi/Llama-3-KoEn-8B-xtuner-llava-preview(https://huggingface.co/beomi/Llama-3-KoEn-8B-xtuner-llava-preview)
- toshi456/chat-vector-llava-v1.5-7b-ja(https://huggingface.co/toshi456/chat-vector-llava-v1.5-7b-ja)
- xtuner/llava-llama-3-8b-transformers
引用
@misc {Llama3-Chat_Vector-kor_llava,
author = { {nebchi} },
title = { Llama3-Chat_Vector-kor_llava },
year = 2024,
url = { https://huggingface.co/nebchi/Llama3-Chat_Vector-kor_llava },
publisher = { Hugging Face }
}

在GPU上运行模型
import requests
from PIL import Image
import torch
from transformers import AutoProcessor, LlavaForConditionalGeneration, TextStreamer
model_id = "nebchi/Llama3-Chat_Vector-kor_llava"
model = LlavaForConditionalGeneration.from_pretrained(
model_id,
torch_dtype='auto',
device_map='auto',
revision='a38aac3',
)
processor = AutoProcessor.from_pretrained(model_id)
tokenizer = processor.tokenizer
terminators = [
tokenizer.eos_token_id,
tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
streamer = TextStreamer(tokenizer)
prompt = ("<|start_header_id|>user<|end_header_id|>\n\n<image>\n请描述这张图片。<|eot_id|>"
"<|start_header_id|>assistant<|end_header_id|>\n\n这张图片展示了")
image_file = "https://search.pstatic.net/common/?src=http%3A%2F%2Fimgnews.naver.net%2Fimage%2F5582%2F2018%2F04%2F20%2F0000001323_001_20180420094641826.jpg&type=sc960_832"
raw_image = Image.open(requests.get(image_file, stream=True).raw)
inputs = processor(prompt, raw_image, return_tensors='pt').to(0, torch.float16)
output = model.generate(
**inputs,
max_new_tokens=512,
do_sample=True,
eos_token_id=terminators,
no_repeat_ngram_size=3,
temperature=0.7,
top_p=0.9,
streamer=streamer
)
print(processor.decode(output[0][2:], skip_special_tokens=False))
运行结果
这张图片清晰地展示了城市景观。城市内部有众多建筑物,连接城市的道路和交通系统十分发达。这座城市的特点是拥有高大且分布广泛的建筑群和完善的交通网络。