Heron Chat Git ELYZA Fast 7b V0

🚀 Heron GIT 日語 ELYZA Llama 2 Fast 7B

Heron GIT 日語 ELYZA Llama 2 Fast 7B 是一款視覺語言模型，能夠針對輸入的圖像進行對話交流，為圖像理解和交互提供了強大的支持。

🚀 快速開始

按照 安裝指南 進行操作。

💻 使用示例

基礎用法

import requests
from PIL import Image

import torch
from transformers import AutoProcessor
from heron.models.git_llm.git_gpt_neox import GitGPTNeoXForCausalLM

device_id = 0

# prepare a pretrained model
model = GitGPTNeoXForCausalLM.from_pretrained(
    'turing-motors/heron-chat-git-ELYZA-fast-7b-v0', torch_dtype=torch.float16
)
model.eval()
model.to(f"cuda:{device_id}")

# prepare a processor
processor = AutoProcessor.from_pretrained('turing-motors/heron-chat-git-ELYZA-fast-7b-v0')

# prepare inputs
url = "https://www.barnorama.com/wp-content/uploads/2016/12/03-Confusing-Pictures.jpg"
image = Image.open(requests.get(url, stream=True).raw)

text = f"##human: これは何の寫真ですか？\n##gpt: "

# do preprocessing
inputs = processor(
    text,
    image,
    return_tensors="pt",
    truncation=True,
)
inputs = {k: v.to(f"cuda:{device_id}") for k, v in inputs.items()}

# set eos token
eos_token_id_list = [
    processor.tokenizer.pad_token_id,
    processor.tokenizer.eos_token_id,
]

# do inference
with torch.no_grad():
    out = model.generate(**inputs, max_length=256, do_sample=False, temperature=0., eos_token_id=eos_token_id_list)

# print result
print(processor.tokenizer.batch_decode(out)[0])

📚 詳細文檔

模型詳情

Heron GIT 日語 ELYZA Llama 2 Fast 7B 是一個能夠對輸入圖像進行對話的視覺語言模型。
該模型使用 heron 庫 進行訓練。詳情請參考代碼。

訓練過程

該模型首先使用 STAIR 圖像描述數據集對適配器進行訓練。在第二階段，使用 LLaVA-Instruct-150K-JA 和日語視覺基因組數據集，通過 LoRA 方法進行微調。

訓練數據集

• LLaVA-Instruct-150K-JA
• 日語 STAIR 圖像描述數據集
• 日語視覺基因組 VQA 數據集

使用與限制

預期用途

此模型旨在用於類似聊天的應用程序以及研究目的。

侷限性

該模型可能會產生不準確或錯誤的信息，其準確性無法保證。目前仍處於研究和開發階段。

如何引用

@misc{GitElyzaFast, 
    url    = {[https://huggingface.co/turing-motors/heron-chat-git-ELYZA-fast-7b-v0](https://huggingface.co/turing-motors/heron-chat-git-ELYZA-fast-7b-v0)}, 
    title  = {Heron GIT Japanese ELYZA Llama 2 Fast 7B}, 
    author = {Yuichi Inoue, Kotaro Tanahashi, and Yu Yamaguchi}
}

參考文獻

@misc{touvron2023llama,
      title={Llama 2: Open Foundation and Fine-Tuned Chat Models}, 
      author={Hugo Touvron and Louis Martin and Kevin Stone and Peter Albert and Amjad Almahairi and Yasmine Babaei and Nikolay Bashlykov and Soumya Batra and Prajjwal Bhargava and Shruti Bhosale and Dan Bikel and Lukas Blecher and Cristian Canton Ferrer and Moya Chen and Guillem Cucurull and David Esiobu and Jude Fernandes and Jeremy Fu and Wenyin Fu and Brian Fuller and Cynthia Gao and Vedanuj Goswami and Naman Goyal and Anthony Hartshorn and Saghar Hosseini and Rui Hou and Hakan Inan and Marcin Kardas and Viktor Kerkez and Madian Khabsa and Isabel Kloumann and Artem Korenev and Punit Singh Koura and Marie-Anne Lachaux and Thibaut Lavril and Jenya Lee and Diana Liskovich and Yinghai Lu and Yuning Mao and Xavier Martinet and Todor Mihaylov and Pushkar Mishra and Igor Molybog and Yixin Nie and Andrew Poulton and Jeremy Reizenstein and Rashi Rungta and Kalyan Saladi and Alan Schelten and Ruan Silva and Eric Michael Smith and Ranjan Subramanian and Xiaoqing Ellen Tan and Binh Tang and Ross Taylor and Adina Williams and Jian Xiang Kuan and Puxin Xu and Zheng Yan and Iliyan Zarov and Yuchen Zhang and Angela Fan and Melanie Kambadur and Sharan Narang and Aurelien Rodriguez and Robert Stojnic and Sergey Edunov and Thomas Scialom},
      year={2023},
      eprint={2307.09288},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

📄 許可證

本項目採用 CC BY-NC 4.0 許可證。