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News
Researchers from Princeton, Ant Group and Stanford Introduce AQuA: A Two-Part Agentic Framework for Autonomous Factor Discovery and Model Development in Quantitative Finance
1+ week, 4+ day ago (652+ words) MarkTechPost Quantitative research agents that write their own experiments can corrupt the evidence they later learn from. A leaky feature that scores well gets stored as a successful precedent and propagated through later iterations. Prompt-level instructions and reviewer agents do…...
Auditing Preference Biases and Fine-Tuning Language Models with Direct Preference Optimization on Anthropic HH-RLHF Using TRL and LoRA
3+ week, 3+ day ago (648+ words) This tutorial provides an end-to-end workflow for fine-tuning language models using Direct Preference Optimization (DPO). We demonstrate how to audit the Anthropic HH-RLHF dataset for structural and length-based biases, implement a robust training pipeline using TRL and LoRA, and evaluate…...
IMDb Sentiment Analysis with DistilBERT LoRA, TF-IDF Baselines, Calibration, Interpretability, Robustness Testing, and Semi-Supervised Learning
1+ mon, 4+ day ago (681+ words) This tutorial provides a comprehensive guide to building a robust sentiment analysis workflow. By combining classical TF-IDF baselines with modern parameter-efficient fine-tuning (DistilBERT + LoRA), we explore deep model interpretability, calibration, and semi-supervised techniques to achieve scalable sentiment inference The post…...
Unsloth vs Axolotl vs TRL vs LLaMA-Factory: A Fine-Tuning Framework Comparison on Speed, VRAM, and Multi-GPU
1+ mon, 3+ week ago (1264+ words) Four open source projects dominate LLM fine-tuning today. Unsloth, Axolotl, TRL, and LLaMA-Factory all wrap the same underlying PyTorch and Hugging Face stack. They diverge on where they spend engineering effort. Unsloth rewrites kernels. Axolotl composes parallelism strategies. TRL defines…...
Validating Distributed LLM Serving Benchmarks with NVIDIA srt-slurm, SLURM Recipes, Parameter Sweeps, and Pareto Analysis
1+ mon, 3+ week ago (719+ words) In this tutorial, we explore NVIDIA’s srt-slurm framework and learn how we use srtctl to convert declarative YAML configurations into reproducible SLURM benchmark workflows for distributed LLM serving. We set up the project in Google Colab, inspect its internal architecture,…...
Feyn AI Releases SQRL, a Text-to-SQL Model Family That Inspects the Database Before Writing a Query
1+ mon, 3+ week ago (520+ words) Most text-to-SQL systems treat the task as translation. Feyn AI (YC-backed startup) reframes it around inspection. The Feyn team has released SQRL, a family of models that turn natural language questions into SQL. Instead of generating a query immediately, SQRL…...
Zyphra Releases ZUNA1.1: An Apache 2.0 EEG Foundation Model With Variable-Length Inputs From 0.5 To 30 Seconds
1+ mon, 3+ week ago (510+ words) This week, Zyphra released ZUNA1.1 under the Apache 2.0 license. The EEG foundation model reconstructs, denoises, and upsamples data across arbitrary channel layouts. It builds on ZUNA1, the Zyphra’s earlier open EEG foundation model. The main change is flexibility, not a jump in…...
OpenAI Details GPT-Red: An Internal Automated Red-Teaming Model That Beat Human Red-Teamers 84% To 13% On Prompt Injection
1+ mon, 3+ week ago (650+ words) This week, OpenAI published details of GPT-Red, an internal-only automated red-teaming model. Its job is to attack OpenAI’s own models and find prompt injection vulnerabilities. OpenAI gives two reasons. Human red-teaming is time-intensive and does not scale. Commonly used robustness…...
Mira Murati's Thinking Machines Lab Makes The Technical Case For Human-Centered AI Built On Customizable Model Weights
2+ mon, 1+ day ago (194+ words) Thinking Machines Lab published a report to build AI that extends human will and judgment. Most AI in use today is trained in a handful of places, then frozen. The report argues that this design excludes the people a model…...
Meet Nemotron Labs 3 Puzzle 75B A9B: A Compressed Hybrid MoE LLM Delivering 2.03x Server Throughput
2+ mon, 3+ day ago (1039+ words) The deployment target was fixed before the architecture search began. Target one was 2x server throughput at 100 tokens per second per user. Target two was 8 concurrent 1M-token requests on a single H100. Three checkpoints on Hugging Face: BF16, FP8, and NVFP4. Nemotron-3-Super is a…...