4+ week, 2+ day ago   (554+ words) MarkTechPost In this tutorial, we build an end-to-end workflow for working with the SupraLabs reasoning corpus. We stream a representative subset directly from the Hugging Face Hub, inspect its source distribution, token-length patterns, task composition, and reasoning-to-answer ratios, and then…...

1+ mon, 12+ hour ago   (691+ words) Build a custom LLM post-training pipeline using AllenAI’s Open Instruct framework. This comprehensive guide walks through Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and Reinforcement Learning with Verifiable Rewards (GRPO), optimized to run efficiently on 16GB hardware without needing heavy distributed…...

5+ mon, 1+ week ago   (223+ words) In the early stages of the LLM boom, post-training was often treated as an experimental ‘dark art.’ TRL v1.0 aims to change that by providing a consistent developer experience built on three core pillars: a dedicated Command Line Interface (CLI), a…...

7+ mon, 2+ week ago   (317+ words) K2 V2 is a dense decoder only transformer with 80 layers, hidden size 8192, and 64 attention heads with grouped query attention and rotary position embeddings. It is trained on around 12 trillion tokens drawn from the TxT360 corpus and related curated datasets that cover web text,…...

8+ mon, 5+ day ago   (497+ words) Technology Innovation Institute (TII), Abu Dhabi, has released Falcon-H1R-7B, a 7B parameter reasoning specialized model that matches or exceeds many 14B to 47B reasoning models in math, code and general benchmarks, while staying compact and efficient. It builds on Falcon H1 7B Base and is…...

9+ mon, 55+ min ago   (308+ words) Can a 3B model deliver 30B class reasoning by fixing the training recipe instead of scaling parameters? Nanbeige LLM Lab at Boss Zhipin has released Nanbeige4-3B, a 3B parameter small language model family trained with an unusually heavy emphasis on data quality, curriculum scheduling,…...

9+ mon, 1+ week ago   (357+ words) Both DeepSeek-V3.2 and DeepSeek-V3.2-Speciale use the DeepSeek-V3 Mixture of Experts transformer with about 671B total parameters and 37B active parameters per token, inherited from V3.1 Terminus. The only structural change is DeepSeek Sparse Attention, introduced through continued pre-training. DeepSeek Sparse Attention splits…...

9+ mon, 2+ week ago   (489+ words) In evaluations, DeepSeekMath-V2 reaches gold level scores on IMO 2025 and CMO 2024, and achieves 118 of 120 points on Putnam 2024 when used with scaled test time compute. Most recent math reasoning models use reinforcement learning that rewards only the final answer on benchmarks…...

9+ mon, 2+ week ago   (262+ words) We set up Tinygrad in our Colab environment and immediately begin experimenting with tensors and automatic differentiation. We create a small computation graph and observe how gradients flow through matrix operations. As we print the outputs, we gain an intuitive…...

10+ mon, 1+ week ago   (240+ words) BCT, token level consistency: Generate a response on the clean prompt with the current checkpoint, then fine-tune so the wrapped prompt yields the same tokens. This is standard cross entropy supervised fine-tuning, with the constraint that targets are always generated…...