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kun kerdthaisong

augustus2011

AI & ML interests

Computer Vision, Natural Language Processing

Recent Activity

upvoted a collection 3 days ago

Datasets for Pretrained Thai LLM

liked a Space 5 days ago

ThaiLLM-Leaderboard/leaderboard

reacted to NJX-njx's post with 🔥 12 days ago

Recently, I have open-sourced an AI emotional companion product based on openclaw, called opensoul. On this platform, you can create a "soulmate" that matches your personality, and configure it with the skills, tools you want it to have, as well as the platforms it can integrate with (such as Telegram, Discord, etc.). You can even create group chats, invite multiple agents and your friends to chat about recent events, discuss projects together, and so on. On the one hand, I hope it can better accompany you in daily life by virtue of its unique memory mechanism, self-feedback and iteration mechanism, and the modeling of users' emotions. On the other hand, I also hope it can help you better handle your work with its unique skills, tools and ability to deal with complex task scenarios. Although the entire product has taken shape, I think there are still many areas that need adjustment and optimization. I also hope to rely on the strength of the community to do a good job in AI emotional companionship. This is the project introduction URL: https://opensoul-web.vercel.app This is the GitHub project URL: https://github.com/NJX-njx/opensoul @AdinaY@lilianweng@burtenshaw@clem let's just do it

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upvoted a collection 3 days ago

Datasets for Pretrained Thai LLM

Collection

List Datasets for pretrained Thai LLM by PyThaiNLP • 25 items • Updated Aug 5, 2025 • 15

liked a Space 5 days ago

Leaderboard

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Browse and submit evaluation results for AI benchmarks

reacted to NJX-njx's post with 🔥 12 days ago

Post

7426

Recently, I have open-sourced an AI emotional companion product based on openclaw, called opensoul.

On this platform, you can create a "soulmate" that matches your personality, and configure it with the skills, tools you want it to have, as well as the platforms it can integrate with (such as Telegram, Discord, etc.).
You can even create group chats, invite multiple agents and your friends to chat about recent events, discuss projects together, and so on.

On the one hand, I hope it can better accompany you in daily life by virtue of its unique memory mechanism, self-feedback and iteration mechanism, and the modeling of users' emotions. On the other hand, I also hope it can help you better handle your work with its unique skills, tools and ability to deal with complex task scenarios.

Although the entire product has taken shape, I think there are still many areas that need adjustment and optimization. I also hope to rely on the strength of the community to do a good job in AI emotional companionship.

This is the project introduction URL: https://opensoul-web.vercel.app
This is the GitHub project URL: https://github.com/NJX-njx/opensoul
@AdinaY @lilianweng@burtenshaw@clem
let's just do it

24 replies

upvoted a paper 26 days ago

GigaBrain-0.5M*: a VLA That Learns From World Model-Based Reinforcement Learning

Paper • 2602.12099 • Published 29 days ago • 58

upvoted a paper 3 months ago

Qwen-Image-Layered: Towards Inherent Editability via Layer Decomposition

Paper • 2512.15603 • Published Dec 17, 2025 • 66

reacted to MonsterMMORPG's post with 👀 3 months ago

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3665

Wan 2.2 Complete Training Tutorial - Text to Image, Text to Video, Image to Video, Windows & Cloud : https://youtu.be/ocEkhAsPOs4

Wan 2.2 training is now so easy. I have done over 64 different unique Wan 2.2 trainings to prepare the very best working training configurations for you. The configurations are fully working locally with as low as 6 GB GPUs. So you will be able to train your awesome Wan 2.2 image or video generation LoRAs on your Windows computer with easiness. Moreover, I have shown how to train on cloud platforms RunPod and Massed Compute so even if you have no GPU or you want faster training, you can train on cloud for very cheap prices fully privately.

Full step by step tutorial : https://youtu.be/ocEkhAsPOs4

⏱️ Video Chapters:

0:00 Introduction to Wan 2.2 Training & Capabilities
0:56 Installing & Updating Musubi Tuner Locally
2:20 Explanation of Optimized Presets & Research Logic
4:00 Differences Between T2I, T2V, and I2V Configs
5:36 Extracting Files & Running Update Batch File
6:14 Downloading Wan 2.2 Training Models via Script
7:30 Loading Configs: Selecting GPU & VRAM Options
9:33 Using nvitop to Monitor RAM & VRAM Usage
10:28 Preparing Image Dataset & Trigger Words
11:17 Generating Dataset Config & Resolution Logic
12:55 Calculating Epochs & Checkpoint Save Frequency
13:40 Troubleshooting: Fixing Missing VAE Path Error
15:12 VRAM Cache Behavior & Training Speed Analysis
15:51 Trade-offs: Learning Rate vs Resolution vs Epochs
16:29 Installing SwarmUI & Updating ComfyUI Backend
18:13 Importing Latest Presets into SwarmUI
19:25 Downloading Inference Models via Script
20:33 Generating Images with Trained Low Noise LoRA
22:22 Upscaling Workflow for High-Fidelity Results
24:15 Increasing Base Resolution to 1280x1280
27:26 Text-to-Video Generation with Lightning LoRA
30:12 Image-to-Video Generation Workflow & Settings
31:35 Restarting Backend to Clear VRAM for Model Switching
33:45 Fixing RAM Crashes with Cache-None Argument
....