Text2Diagram is an AI based diagramming tool that uses Natural language text to create diagrams.
https://github.com/bhaskatripathi/Text2Diagram
webrtc
https://github.com/livekit/livekit
https://github.com/AudioLLMs/Awesome-Audio-LLM
Introduction to Text-to-Speech Using LLM’s
https://medium.com/@prajwal_/introduction-to-text-to-speech-using-llms-58ef41a92f9e
https://17aitech.com/?p=13611

https://github.com/Lightning-AI/litgpt
https://huggingface.co/THUDM/chatglm-6b/blob/main/modeling_chatglm.py

<a href="https://colab.research.google.com/gist/wangyang1749/f783b1fbdfd15ae9f3fa133499a035ca/graphrag_openai_neo4j_langchain.ipynb" target="_black"><img alt="Static Badge" src="/badge/colab-badge.svg"></a> <a href="https://gist.github.com/wangyang1749/f783b1fbdfd15ae9f3fa133499a035ca" target="_black"><img alt="Static Badge" src="/badge/github-badge.svg"></a>

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "gpt2"  
tokenizer = AutoTokenizer.from_pretrained(model_name)
input_text = "hello"
inputs = tokenizer(input_text, return_tensors="pt")

https://ant-design-charts.antgroup.com/zh/examples/statistics/line#line-var-size
https://zhuanlan.zhihu.com/p/27903098086
https://sandeep14.medium.com/running-graphrag-locally-with-neo4j-and-ollama-text-format-371bf88b14b7
点击下载

点击下载
http://wangyang-bucket.oss-cn-beijing.aliyuncs.com/cms/image/sh_1741857686607.pdf

https://sandbox.neo4j.com/

https://python.langchain.ac.cn/docs/how_to/graph_constructing/
----小爱发货内容----

1. 使用方法：
   • 关闭魔法（国外网）
   • 输入URL和key
     2.使用说明【教程在第八步，报错请看第七步】：
     https://kcnmu6rhf93r.feishu.cn/wiki/QIdLwkgcAiTBopkHG8oc2WfZnHg?from=from_copylink
     3.小爱导航 官网 chat -学术优化- lobe -查询余额地址 ：
   • https://a.xiaoai.plus/
     注：商品仅供个人测试，请遵守法律法规。

接口：

• 请求地址1：https://xiaoai.plus
• 请求地址2：https://xiaoai.plus/v1
• 路由请求：https://xiaoai.plus/v1/chat/completions

• 依次测试接口
• key在最上面，有报错问题不要发淘宝，联系【使用文档】里有售后专员！

-----每周有老客户专属小额福利活动，可联系聊天室里专员了解----
sk-FgKk2OO5RYzYRJEf7eaMytOLsuIbZecGxaJvRnWDg1GCIkNh
https://js.langchain.com/docs/integrations/chat/
https://onlinelibrary.wiley.com/doi/full/10.1002/qub2.69
https://link.springer.com/article/10.1007/s13721-024-00458-1
https://arxiv.org/abs/2502.06890
Foundation models in bioinformatics

https://www.mdpi.com/2813-2998/4/1/9#
https://link.springer.com/article/10.1186/s13073-024-01315-6
Learning the language of protein-protein interactions https://github.com/VarunUllanat/mint
seqLens: optimizing language models for genomic predictions https://www.biorxiv.org/content/10.1101/2025.03.12.642848v1

https://jyj.xianyang.gov.cn/zwgk/fdzdgknr/zcwj/wjzq/202403/t20240319_1744296.html
https://readloudly.com/viewer?type=reader&id=67e143b2947f68c8ab10a163&page=1
https://mermaid.live/
https://ant-design-charts.antgroup.com/examples
https://tidyplots.org/
https://www.ncbi.nlm.nih.gov/books/NBK179288/
https://www.ncbi.nlm.nih.gov/home/tools/
Improved metagenome binning and assembly using deep variational autoencoders