Fix: correct typos and formatting issues in README

- Removed duplicate closing </a> tag in the introduction section.
- Fixed extra space in the link reference to VLMEvalKit.
- Cleaned up redundant hash symbols in code comments.
- Added missing periods at the end of sentences for consistency.
This commit is contained in:
戴福生 2025-01-28 00:49:28 +07:00 committed by GitHub
parent a42ad6dab3
commit 45680ae127
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194

View File

@ -17,7 +17,6 @@
<a href="https://www.deepseek.com/" target="_blank">
<img alt="Homepage" src="images/badge.svg" />
</a>
</a>
<a href="https://huggingface.co/deepseek-ai" target="_blank">
<img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-DeepSeek%20AI-ffc107?color=ffc107&logoColor=white" />
</a>
@ -66,7 +65,7 @@
**2024.11.13**: JanusFlow is released, a new unified model with rectified flow for image generation. See [paper](https://arxiv.org/abs/2411.07975), [demo](https://huggingface.co/spaces/deepseek-ai/JanusFlow-1.3B) and [usage](https://github.com/deepseek-ai/Janus?tab=readme-ov-file#janusflow).
**2024.10.23**: Evaluation code for reproducing the multimodal understanding results from the paper has been added to VLMEvalKit. Please refer to [this link]( https://github.com/open-compass/VLMEvalKit/pull/541).
**2024.10.23**: Evaluation code for reproducing the multimodal understanding results from the paper has been added to VLMEvalKit. Please refer to [this link](https://github.com/open-compass/VLMEvalKit/pull/541).
**2024.10.20**: (1) Fix a bug in [tokenizer_config.json](https://huggingface.co/deepseek-ai/Janus-1.3B/blob/main/tokenizer_config.json). The previous version caused classifier-free guidance to not function properly, resulting in relatively poor visual generation quality. (2) Release Gradio demo ([online demo](https://huggingface.co/spaces/deepseek-ai/Janus-1.3B) and [local](#gradio-demo)).
@ -165,10 +164,10 @@ prepare_inputs = vl_chat_processor(
conversations=conversation, images=pil_images, force_batchify=True
).to(vl_gpt.device)
# # run image encoder to get the image embeddings
# run image encoder to get the image embeddings
inputs_embeds = vl_gpt.prepare_inputs_embeds(**prepare_inputs)
# # run the model to get the response
# run the model to get the response
outputs = vl_gpt.language_model.generate(
inputs_embeds=inputs_embeds,
attention_mask=prepare_inputs.attention_mask,