SAIL 7B

🚀 SAIL

SAIL是一個用於視覺和語言的單Transformer模型，是統一的多模態大語言模型，能在多種視覺語言任務中展現出強大性能。

鏈接

• 📂 GitHub
• 📜 論文
• 🚀 快速開始

✨ 主要特性

SAIL是一個用於視覺和語言的單Transformer模型，是統一的多模態大語言模型（MLLM），它在單一架構中無縫集成了原始像素編碼和語言解碼。無需依賴預訓練的視覺編碼器，SAIL在廣泛的視覺語言任務中取得了有競爭力的性能，並展現出強大的視覺表示能力，在語義分割等任務中可與最先進的視覺模型相媲美。

📦 模型

🚀 快速開始

我們提供了一個運行SAIL的示例代碼。

from example import *

NON_VISION_TOKEN_ID = -1
PATH_TO_MODEL = "path to model"
PATH_TO_TOKENIZER = "path to tokenizer"
IMAGE_PATH = "path to image"
PROMPT = "content of prompt"

model, tokenizer = get_transformer_and_tokenizer(
    PATH_TO_MODEL,
    PATH_TO_TOKENIZER
)
model = model.cuda()

image_processor = lambda x: convert_image_base64_to_patches(load_image_to_base64(x), model.config.vision_patch_size, fix_res_size=None)
prompt_inp = tokenizer.bos_token + '[INST] {} [/INST]'.format(PROMPT)
image_path = IMAGE_PATH   
image_patches = image_processor(image_path)
nh, nw = image_patches.shape[:2]
image_tokens, image_tokens_len = prepare_image_textual_seq_norowsep(nh, nw, tokenizer, add_cls=False)

input_tokens = image_tokens + prompt_inp
input_ids = tokenizer(input_tokens, add_special_tokens=False, return_tensors="pt").input_ids
vision_patch_indices = torch.full_like(input_ids, fill_value=NON_VISION_TOKEN_ID)
vision_patches = image_patches.view(nh * nw, -1)
assert (input_ids == tokenizer.vis_patch_tok_id).sum() == vision_patches.size(0)
assert (input_ids >= tokenizer.vis_beg_tok_id).sum() == image_tokens_len

vision_patch_indices[input_ids==tokenizer.vis_patch_tok_id] = torch.arange(vision_patches.size(0))
attention_mask = create_single_prefix_mask(image_tokens_len, input_ids.size(-1)).unsqueeze(0).unsqueeze(0)
position_ids = generate_mm_pos_ids_singleit(input_ids.squeeze(0).numpy().tolist(), tokenizer.vis_patch_tok_id, nh, nw).unsqueeze(1)

input_ids = input_ids.long().cuda()
vision_patch_indices = vision_patch_indices.long().cuda()
vision_patches = vision_patches.to(torch.bfloat16).cuda()
position_ids = position_ids.long().cuda()
attention_mask = attention_mask.cuda()

padding_attention_mask = torch.ones_like(input_ids).cuda()

inputs = dict(
    input_ids = input_ids,
    position_ids = position_ids,
    attention_mask = padding_attention_mask,
    vision_patches = vision_patches,
    vision_patch_indices = vision_patch_indices,
    use_cache=True
)

cached_inputs = dict(
    input_ids = input_ids[:, :image_tokens_len],
    position_ids = position_ids[:, :, :image_tokens_len],
    attention_mask = attention_mask[:,:, :image_tokens_len, :image_tokens_len],
    vision_patches = vision_patches,
    vision_patch_indices = vision_patch_indices[:, :image_tokens_len],
    use_cache=True
)

prefix_cache = DynamicCache()
with torch.no_grad():
    prefix_cache = model.forward(**cached_inputs, past_key_values=prefix_cache).past_key_values

past_key_values = copy.deepcopy(prefix_cache)
generate_config = GenerationConfig(
    max_new_tokens=1024,
    return_dict_in_generate=True,
    output_attentions=False
)
generated = model.generate(
    **inputs,
    past_key_values=past_key_values,
    generation_config=generate_config
)
generated_ids = generated['sequences'][:, input_ids.size(1):]
response = tokenizer.batch_decode(
    generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False
)[0]

print(f"\nModel Response: ===\n{response}\n===")

📄 許可證

本項目採用Apache - 2.0許可證。

📚 引用

如果您在研究中發現本項目有用，請考慮引用：

@article{lei2025sail,
  title={The Scalability of Simplicity: Empirical Analysis of Vision-Language Learning with a Single Transformer},
  author={Lei, Weixian and Wang, Jiacong and Wang, Haochen and Li, Xiangtai and Liew, Jun Hao and Feng, Jiashi and Huang, Zilong},
  journal={arXiv preprint arXiv:2504.10462},
  year={2025}
}