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A local web UI for finetuning AI models on personal text, featuring live gradient activity monitoring, A/B prompt comparison, and AMD ROCm compatibility.
The paper introduces Diff Mining, a framework for identifying finetuning objectives in language models by analyzing logit differences between finetuned and base models, enabling interpretable auditing of learned behaviors.
Apodex is an open-sourced deep research harness and AI model, which is a finetune of Qwen 3.5 35B A3B, achieving performance comparable to frontier models with only 3B active parameters.
This blog post introduces how to train and finetune multi-vector embedding models using the Sentence Transformers library, showcasing its v6.0 update with a new MultiVectorEncoder type and demonstrating superior performance on medical retrieval tasks.
This paper investigates whether the Knobe effect (a moral bias in intentionality judgments) emerges in finetuned LLMs, and uses layer-patching analysis to localize the bias to specific layers, showing that targeted interventions can eliminate the effect without retraining.
This paper introduces Enlightenment, a training-free post-tuning method for large-scale models that modifies shortcuts in key modules without weight updates, achieving sudden capability boosts. It demonstrates effectiveness across LLMs and VLMs via attention head-mixing and scalar-modulated residual connections.
A proof-of-concept In-Context LoRA adapter for LTX-Video 2.3 that re-renders video scenes from new camera angles using a fixed vocabulary prompt, trained on synthetic multi-view data.
BLOOMZ and mT0 are multilingual models finetuned on the crosslingual task mixture xP3, enabling zero-shot instruction following in dozens of languages.
This paper introduces Contrastive Decoding Diffing (CDD), a method that recovers verbatim finetuning data from LLMs using only logit access, without requiring model weights or activations. It demonstrates high verbatim recovery across multiple model families and reveals unintended data leakage, such as the recurring fictional persona 'Dr. Elena Rodriguez' from synthetic training data.
This paper proposes Self-Recognition Finetuning as an intervention to prevent and reverse emergent misalignment in LLMs, showing it stabilizes the model's aligned character rather than adopting a misaligned persona.
Proposes a finetuning-based hardware-aware training algorithm to mitigate I-V non-linearity and retention errors in ReRAM crossbar arrays, enabling robust DNN deployment with minimal overhead. Evaluated on image classification and QA tasks, achieving near-baseline accuracy.
Cleo is a finetuned version of Qwen3.5-2B-Base designed for text-to-SQL tasks, using a unified harness for training and inference that supports live execution evidence and safety checks. All code, model, and datasets are open-source.
This paper introduces Muon^p, a novel optimizer that uses fractional spectral-power updates to interpolate between Muon and gradient descent, providing theoretical justification and empirical gains on billion-scale fine-tuning tasks.
This paper proposes a modular approach for adapting pretrained language models to low-resource languages by freezing embeddings and tuning the rest, showing improvements on NLU tasks for Scottish Gaelic, Irish, and Quechua.
TRL now supports fine-tuning models on agent traces from various sources like Claude Code, Codex, OpenClaw, and Pi, moving towards a standardized stack for training agentic models.
This paper introduces GGT-100K, a dataset of 103,707 image pairs for real-world image restoration, generated by using multimodal foundation models like Nano-Banana-2 to produce high-quality targets from low-quality inputs. Experiments show the dataset improves the generalization of various image restoration models.
This article discusses making LLM finetuning accessible to non-coders, highlighting a video demonstration of how anyone can customize LLMs without coding.