@DeRonin_: Chinese open-source LLMs totally replace all new released models it allows you to train own LLM locally yeah, threshold…
Summary
A tweet claiming that Chinese open-source LLMs have completely replaced newly released models, allowing local training, though raising the bar for casual developers.
View Cached Full Text
Cached at: 06/28/26, 09:59 AM
@EXM7777 Chinese open-source LLMs totally replace all new released models
it allows you to train own LLM locally
yeah, threshold has increased and student 15 y.o couldn’t build something revolution now
but it makes this world harder for lazymaxxing
i guess nothing wrong happened
Similar Articles
Developing open source LLM from ground up from pretrain - rlhf(PPO/GRPO)
A developer shares progress on training a 7B parameter open source LLM from scratch using a DeepSeek architecture optimized for low VRAM, with the goal of democratizing AI development and eventually surpassing large proprietary models.
@seclink: It seems Ollama has been thoroughly bested by vLLM. Given the rapid pace of large model development (with new models released almost weekly), using vLLM is often more practical and convenient than using tools like DeepSpeed or TensorRT.
The article argues that vLLM has overtaken Ollama in usability due to the rapid pace of new model releases, finding it more practical than alternatives like DeepSpeed or TensorRT.
@neural_avb: If you think about it, LLM training in 2026 is really a 3-step loop : - train it on some data - dogfood it/run categori…
The tweet outlines a 3-step loop for LLM training in 2026: train on data, run evals, and add synthetic data for underperforming tasks. It emphasizes the accessibility of legal distillation via open source models and cheap APIs, noting that training on reasoning traces alone can achieve high scores.
@oliviscusAI: OpenAI's co-founder just released his personal guide to train LLMs from scratch. It's called llm.c. No heavy setup. Jus…
OpenAI co-founder Andrej Karpathy released llm.c, an open-source guide to training LLMs from scratch with simple code that runs on any hardware, including CPUs and MacBooks, and is 7% faster than standard approaches.
@0xshimei: https://x.com/0xshimei/status/2053088751862288846
This article provides a comprehensive 2026 guide to free and low-cost large language models, comparing domestic (China) and international options.