Granite-4.1-8B is an 8B parameter long-context instruct model from IBM, enhanced through finetuning and alignment for advanced tool calling, instruction following, and chat capabilities. It supports multiple languages and was released in April 2026 under the Apache 2.0 license.
Consistent use of Claude allows it to build up a working memory, which manifests as a pile of plain markdown files. This effectively creates a personal "LLM Wiki," where the model remembers user decisions and preferences without requiring re-explanation.
A new book and open-source code are released, detailing how to build modern LLM architectures like GPT-2, Llama 3, and DeepSeek from scratch in PyTorch. It explains the architectural changes required to transform GPT-2 into Llama 3 and implements DeepSeek's advanced features.
This content explores the Gemma Playground, using a 'Robot Duck' as an application example. The focus is on demonstrating the capabilities of the Gemma model in a practical scenario.
The paper introduces "bicache", the first KV caching technique for shared prefixes in diffusion language models (DLMs), addressing challenges where existing LLM caching methods fail due to DLMs' bidirectional attention. This new approach aims to unlock high-throughput DLM serving by leveraging observations about shared prefix KVs stability in shallow layers.
This research paper explores the automatic extraction of data from brain MRI reports using the open-weight large language model LLaMA 3.1. It evaluates the LLM's performance in analyzing Dutch neuroradiology reports, demonstrating high zero-shot performance.
GraphLoRA proposes a novel framework for Large Language Model Recommendation (LLMRec) that integrates structural information with textual semantics. It achieves this by embedding a trainable graph message-passing network within the low-rank adaptation pathway, allowing collaborative topology to explicitly guide parameter updates.
This content presents a video demonstrating how an LLM's decoder blocks changed during training, building upon a popular previous post. The author shares visual data to illustrate the model's evolution process.
This content introduces a new side project and its GitHub repository, focusing on mitigating LLM hallucination through a novel contrastive sampling and selective training method. The core idea treats hallucination as a preference problem, using self-generated negative samples and divergence-based, gated learning to push correct answers and suppress wrong ones.
![New project about llm hallucination [P]](/cdn-cgi/image/width=3840,quality=75,format=webp/https://preview.redd.it/b9qn0mpye4xg1.png?width=140&height=74&auto=webp&s=955d8bf1d2ce779e50e0b5ff03462f4c8a8e3d5d)
This content announces the execution of the 1-bit Bonsai 1.7B model (290MB) directly in the browser, leveraging WebGPU technology. A link to the demonstration is provided via Hugging Face Spaces, highlighting an innovation in client-side ML.
Este estudo aborda erros de Tipo II na classificação de tarefas por LLMs, onde prompts aparentemente simples exigem compreensão profunda. A pesquisa demonstrou que prompts de exploração aberta ("What's really going on here?") reduzem significativamente esses erros em comparação com prompts de extração direta.
The author successfully trained a Qwen2.5-0.5B-Instruct model for Reddit post summarization using GRPO, achieving an average rollout length of 64 tokens with combined quality and length rewards. The experiment, run on a Mac Mini cluster, uses an LLM-as-a-Judge (GPT-5) for evaluation and plans future iterations with adjusted reward functions.
![Trained a Qwen2.5-0.5B-Instruct bf16 model on Reddit post summarization task with GRPO written from scratch in PyTorch - updates! [P]](/cdn-cgi/image/width=3840,quality=75,format=webp/https://preview.redd.it/7nrsulwdkbvg1.png?width=140&height=69&auto=webp&s=7c61d2f68d6b094614b5dff0cb9347873885e226)
Cloudflare released Unweight, a lossless compression system that reduces LLM size by 15-22% without sacrificing output accuracy. The tool, which saves roughly 3 GB of VRAM on Nvidia H100 GPUs for Llama-3.1-8B, has been open-sourced on GitHub with plans to extend compression.
OpenAI has announced its new GPT-5.5 model, touting it as its "smartest and most intuitive" offering to date and a significant step forward in computer interaction. This new version excels at complex tasks like coding, research, and document creation across various tools, handling multi-part requests by planning and self-checking.
A senior engineer spent the past year implementing five LLM architectures from scratch in PyTorch, including GPT-2, Llama 3, and DeepSeek. The project resulted in open-source code and a detailed book documenting the process, explaining advanced concepts like KV cache, MoE, and FP8 quantization.
The user praises Qwen3.6 OpenCode as an "incredible" local model for complex coding tasks, highlighting its effectiveness in implementing RLS across a multi-language codebase. While not perfect, its ability to iterate on compiler errors makes it a viable alternative to models like Claude Code for daily use.
Um método inovador usa os RT Cores de GPUs para roteamento de especialistas em modelos MoE, resultando em aceleração de 218x e 731x menos VRAM para essa tarefa. A pesquisa também revela que os especialistas MoE se especializam por tipo sintático, e não por tópico como se acreditava.
A user recounts their experience with the Qwen3.6 model, which successfully built and tested a tower defense game, demonstrating the ability to identify and fix its own bugs. The AI confirmed builds using screenshots, astonishing the user with its advanced capabilities.
O autor iniciou uma série de vídeos sobre a construção de uma camada de orquestração para o pós-treinamento de LLMs. Ele descreve seus esforços para melhorar o framework `verl` para treinamento RL em escala, focando na modernização de pacotes e remoção de dependências irrelevantes.
GLM 5.1 appears highly competitive in social reasoning against frontier models, based on a custom benchmark involving autonomous Blood on the Clocktower games. It offers significant cost efficiency at $0.92 per game compared to Claude Opus 4.6's $3.69, with a 0% tool error rate.
