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Finally, the video shows prompting the LLM before and after fine-tuning.
— Akshay 🚀 (@akshay_pachaar) 7 juin 2026
After fine-tuning, the model is able to find the exact missing chess move instead of randomly generating some moves.
Check this 👇 pic.twitter.com/WPAmLLBg4u
Finally, the video shows prompting the LLM before and after fine-tuning. After fine-tuning, the model is able to find the exact missing chess move instead of randomly generating some moves. Check this
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Load dataset We'll fine-tune Gemma 4 12B to master chess. Given a set of previous move (one move missing) & the final result it has to predict the missing move. In order to do this we're using the ChessInstruct dataset from HuggingFace. Check this
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Google released Gemma 4 12B, a multimodal model that runs text, images, and audio on 8GB VRAM!
— Akshay 🚀 (@akshay_pachaar) 7 juin 2026
We'll fine-tune it to master chess and predict the exact next move.
Tech stack:
– @UnslothAI for efficient fine-tuning.
– @huggingface transformers to run it locally.
Let's go! 🚀 pic.twitter.com/qPXjlYrShs
Google released Gemma 4 12B, a multimodal model that runs text, images, and audio on 8GB VRAM! We'll fine-tune it to master chess and predict the exact next move. Tech stack:
– @UnslothAI for efficient fine-tuning.
– @huggingface transformers to run it locally. Let's go!
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This is a solution for how world models should be trained so they become proper causal agents. This solution was developed by @AdaptiveAgents and myself over the years. We are familiar with the literature. Thanks for the link.
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Precisely not. This is not about model architectures, what people often stress when talking about world models. This works with Jepa or GPT. This is about causal interactive training. It’s all about environments, not agents.
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What if a self-driving car could learn its position just by watching millions of hours of driving video? Researchers at Wayve and Simon Fraser University introduce LA-Pose: they train a model to learn "latent actions"—hidden motion patterns—from unlabeled driving footage, then
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The field of AI is in a local minimum. Not a local minimum in architectures and models, but a local minimum on how we train: a multi-step Frankenstein-like approach. In this new blog post, I propose a path.
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#MachineLearning improves #Healthcare access in Sierra Leone
by Ziad Obermeyer @Nature Learn more: https://
bit.ly/4wc6M97 #MedTech #HealthTech #Tech #TechForGood #ML
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Fun LLM Question from Mike today > Let’s say Thanos snaps away every model from existence except Qwen3.5-2B > Ahmad, what would you do? Assumptions & Clarifications 1. All papers, tech reports, synthetic datasets, HF repos, checkpoints, eval harnesses, and research artifacts
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Can your phone agent do more than just tap and swipe? Tencent Hunyuan researchers (with CUHK & Tsinghua) introduce PhoneHarness. It’s a new harness that lets phone agents seamlessly mix CLI, GUI, and host tools—verifying real side effects, not just screen predictions. Result: