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Fine Tuning

211 items · started 2019-09-19 · ongoing (last activity 2026-06-26)

  2. Building general-purpose role-playing agents that faithfully portray any character from a natural-language profile remains challenging. The dominant paradigm -- supervised fine-tuning -- encourages behavioral mimicry without deep, human-li…

  3. Large pretrained text-to-speech (TTS) models sound almost human for well-resourced languages, but much worse for languages that are rare in their training data. We study this quality gap for Khmer and Korean using VoxCPM2, a 2.4B-parameter…

  4. LLMs fine-tuned for security classification are usually evaluated on held-out examples from the same distribution as their training data. We show that this can miss vulnerabilities introduced by fine-tuning itself: models can learn token-l…

  5. Fine-tuning through RL reshapes the internal representations of language models to enable agentic behaviors such as tool use, yet the mechanistic basis of these changes remains poorly understood. While RL substantially improves structured…

  6. While parameter-efficient fine-tuning (PEFT) typically targets attention projectors, its efficacy for tasks requiring sequential state accumulation remains under-explored. We examine if PEFT for such tasks can benefit from state space mode…

  7. The DP-LoRA silent corruption: how 5 months of broken fine-tuning hid in plain sight How a device-placement ordering quirk between opacus, PEFT, and HuggingFace caused DP fine-tuning to silently break, and what to check in your own setup.…

  8. Vision-Language-Action (VLA) models are often constrained by the imitation ceiling imposed by sub-optimal data. While Reinforcement Learning (RL) fine-tuning can surpass this limit, it is notoriously sample inefficient.

  9. Accelerating Transformers Fine-Tuning with NVIDIA NeMo AutoModel NVIDIA NeMo AutoModel is an open library part of the NVIDIA NeMo framework for building custom generative AI models at scale. NeMo AutoModel builds cleanly on top of v5, addi…

  10. Supervised fine-tuning (SFT) datasets are critical to the downstream performance of large language models, yet they often contain low-quality or harmful question-response pairs. To improve SFT data quality, we develop a unified bilevel fra…

  11. Prompt-based learning has emerged as a dominant paradigm in natural language processing. This study explores the impact of diverse pre-training objectives on the performance of encoder-decoder pre-trained language models across generation…

  18. Inference-time engineering can alter model behavior without fine-tuning. However, its utility for improving diagnostic performance in medical vision-language models (VLMs) remains unclear.

  19. Alignment tuning is meant to make harmful-request refusal robust, yet this safety behavior can be erased by a small set of benign fine-tuning examples. This is a deployment risk for open-weight models because a checkpoint can pass refusal…

  20. Was debugging my TTS pipeline and doing some research on natural voice options, and Claude Opus 4.8 mentioned its training cutoff is January 2026. But the model launched on May 28, 2026.

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  22. Controlling generative models is computationally expensive. This is because optimal alignment with a reward function--whether via inference-time steering or fine-tuning--requires estimating the value function.

  23. Low-rank adaptation (LoRA) makes it cheap to train many domain- and task-specific language model adapters, but whether two adapters can be merged is usually discovered only after both have been fully trained and evaluated. This late feedba…

  24. Post-training alignment of large language models often combines supervised fine-tuning (SFT) on expert demonstrations with reinforcement learning (RL) from preference or verifiable feedback. SFT provides a useful behavioral anchor but can…

  25. Whether LLMs scoring well on vulnerability benchmarks genuinely reason about security or merely pattern-match on contaminated data remains unresolved. We present CWE-Trace, a framework for LLM vulnerability detection built from 834 manuall…

  26. The challenge associated with recognizing dysarthric speech primarily arises from pronounced acoustic variability attributed to impaired articulatory precision. Past research has demonstrated improved recognition through the use of hybrid…

  27. Fine-tuning of Large Language Models (LLMs) using Low-Rank Adaptation (LoRA) on an end-user's data offers personalized experiences while keeping data private, but faces severe memory constraints on consumer hardware. Peak memory during fin…

  28. We study cross-lingual transfer by fine-tuning seven large language models (4B--671B parameters) on Arabic and evaluating zero-shot reading comprehension on Semitic languages and non-Semitic controls. Across dense and Mixture-of-Experts ar…

  29. Realistic coding-agent benchmarks often replay public GitHub issues and pull requests, making them vulnerable to overlap with model pretraining, fine-tuning, synthetic-data generation, or benchmark-driven model selection. Fully synthetic t…

  30. The increasing deployment of parameter-efficient fine-tuning (PEFT) has led to model ecosystems in which a single backbone is paired with many task-specialized adapters. In this setting, inference-time queries often arrive without task lab…

  31. While recent advances in machine learning have equipped Weather Foundation Models (WFMs) with substantial generalization capabilities across diverse downstream tasks, the escalating computational requirements associated with their expandin…

  32. Current unlearning methods for LLMs optimize on the private information they seek to remove by incorporating it into their fine-tuning data. We argue this not only risks reinforcing exposure to sensitive data, but also fundamentally contra…

  33. Pre-trained materials foundation models, or machine learning interatomic potentials, leverage general physicochemical knowledge to effectively approximate potential energy surfaces. However, they often require domain-specific calibration d…

  34. Beyond LoRA: Can you beat the most popular fine-tuning technique? When you plan to fine-tune a model in a parameter-efficient way, think beyond LoRA If you want to fine-tune an open model on your own data, you are probably interested in so…

  35. AI-native architectures are vital for 6G wireless communications. The black-box nature and high complexity of deep learning models employed in critical applications, such as channel estimation, limit their practical deployment.

  36. Fine-tuning flow matching models is a central challenge in settings with limited data, evolving distributions, or computational constraints. While recent work has produced significant advances, particularly in the area of reward-based fine…

  37. Depression is the leading cause of disability worldwide, and early detection of symptom change is essential for timely intervention. Validated instruments such as the Patient Health Questionnaire-9 (PHQ-9) support symptom monitoring at sca…

  38. This study explores how bilingual fine-tuning affects automatic speech recognition (ASR) in low-resource languages. We evaluate this method across nine linguistically and geographically diverse language pairs, covering a range of language…

  39. Making large language models (LLMs) deeply forget specific knowledge and values without sacrificing general capabilities remains a central challenge in unlearning. However, current methods are easily reversed by fine-tuning or few-shot pro…

  40. Fine-tuning large language models (LLMs) is compute-intensive and error-prone: model performance depends sensitively on data quality and hyperparameter choices, and naïve runs can even degrade model performance. This raises a practical que…

  41. The high cost of fine-tuning LLMs poses a significant economic barrier; pre-hoc performance prediction offers a critical solution to substantially reduce this expense. However, the theoretical limits of pre-hoc performance prediction remai…

  42. Open-weight video diffusion models can generate photorealistic unsafe content, from violence to misinformation, yet existing defenses either require expensive safety fine-tuning that degrades general capability, or apply external filters t…

  43. This report examines the fine-tuning of Large Language Models (LLMs), integrating theoretical insights with practical applications. It outlines the historical evolution of LLMs from traditional Natural Language Processing (NLP) models to t…

  44. Reading Anthropic's work on emotion-like representations got me thinking. ​ If we can identify latent representations for concepts such as fear, despair, etc., could similar methods be used to identify representations associated with malic…

  45. When pretrained VLA policies are fine-tuned through online RL, each rollout episode produces only a single binary outcome (success or failure), yet the actor update requires per-transition supervision. Existing approaches commonly reduce t…

  46. We study the task of CVE-conditioned exploit generation, where a model drafts proof-of-concept (PoC) exploits given software vulnerability context. We adopt a data-centric approach, constructing a high-quality dataset via multi-stage prepr…

  47. The continuous scaling of large language models (LLMs) incurs prohibitive computational costs, making Mixture-of-Experts (MoE) a scalable alternative for efficient fine-tuning via sparse activation. While federated learning (FL) emerges as…

  48. Fine-tuning large language models (LLMs) has become a central application of modern optimization, enabling pretrained models to adapt to diverse downstream tasks and domain-specific data. A major obstacle in large-scale fine-tuning is the…

  49. Sequential fine-tuning of Large Language Models (LLMs) adaptation to target tasks often triggers catastrophic forgetting, where the acquisition of novel target skills degrades ancestral capabilities. This paper presents a systematic compar…

  50. Graphical user interface (GUI) agents powered by multimodal large language models (MLLMs) have shown greater promise for human-interaction. However, due to the high fine-tuning cost, users often rely on open-source GUI agents or APIs offer…

  51. Traditional loss functions, including cross-entropy, contrastive, triplet, and su pervised contrastive losses, used for fine-tuning pre-trained language models such as BERT, operate only within local neighborhoods and fail to account for t…

  52. Federated learning (FL) for large language models (LLMs) has attracted increasing attention as a privacy-preserving approach for adapting models over distributed data, where parameter-efficient methods such as Low-Rank Adaptation (LoRA) ar…

  53. Supervised fine-tuning (SFT) is a commonly used technique to adapt large language models (LLMs) to downstream tasks. In practice, SFT on a full dataset is computationally expensive and sometimes suffers from overfitting or bias amplificati…

  54. Fine-tuning a 7B language model for specialized advising is attractive in resource-constrained settings, but multi-epoch runs routinely exceed the wall-clock limits of the free-tier GPUs (Kaggle, Colab) such users rely on. We report two th…

  55. Large language models routinely generate code with exploitable security flaws. Prior literature attributes this limitation to a lack of security expertise, steering current defense mechanisms toward heavy fine-tuning or external knowledge…

  56. Federated fine-tuning of large language models using parameter-efficient methods such as LoRA enables privacy-preserving adaptation of foundation models. Heterogeneous hardware resources introduce challenges, as clients with different adap…

  57. Most medical AI systems improve by scaling additional machinery: more fine-tuning data, more agents, and/or larger retrieval databases. In rare-disease diagnosis, however, such scaling can produce systems that are difficult to deploy, audi…

  58. Text-to-SQL aims to translate natural language questions into executable SQL queries over structured databases, enabling non-expert users to access data intuitively. While recent advances in large language models (LLMs) have shown promise…

  59. Latent Bias Transfer (LBT) A note on how this was made. The hypothesis and the questions are mine — but several of the techniques here (LoRA fine-tuning, activation steering, the statistics) were new to me.

  60. Safety alignment is essential for the responsible deployment of Large Language Models (LLMs). Yet, existing approaches often rely on heavyweight fine-tuning that is costly to update, audit, and maintain across model families.

  61. We study multi-domain LLM training in which two models, each stronger in a different domain, co-evolve by tutoring each other through on-policy feedback. Unlike one-way distillation or single-model fine-tuning, our goal is mutual Pareto im…

  62. Reinforcement Learning with Verifiable Rewards ( RLVR ) has emerged as a transformative paradigm for enhancing the reasoning capabilities of Large Language Models ( LLMs), yet its potential in 3D scene understanding remains under-explored.…

  63. Reinforcement learning (RL) has become a cornerstone for fine-tuning Large Language Models (LLMs), with Proximal Policy Optimization (PPO) serving as the de facto standard algorithm. Despite its ubiquity, we argue that the core ratio clipp…

  64. Post-Training Quantization (PTQ) is essential for deploying Large Language Models (LLMs) on memory-constrained devices, yet it renders models static and difficult to fine-tune. Standard fine-tuning paradigms, including Reinforcement Learni…

  65. Fine-tuning vision-language models to emit dense coordinate lists improves visual grounding but also changes how models serialize, repeat, and terminate structured outputs. We study this behavior as a generation and control surface.

  66. Post-training methods such as supervised fine-tuning (SFT) and preference optimization typically align language models toward a single global assistant behavior. While effective for improving average helpfulness, this can suppress the natu…

  67. We present an approach to fine-tuning large language models using Direct Preference Optimization (DPO), a reinforcement learning technique. Our experimental results demonstrate that DPO simplifies the training pipeline, improves computatio…

  68. Large Language Models such as GPT-4o and GPT-5 achieve strong zero-shot performance on biomedical claim verification, but cost and opacity limit scalable use. We fine-tune three small LLMs: Phi-3-mini (3.8B), Qwen2.5-3B, and Mistral-7B, vi…

  69. Detecting mental health disorders from Arabic social media text remains challenging due to dialectal variation, informal language, limited high-quality annotated resources, and severe class imbalance. While English mental health natural la…

  70. Retrieval-augmented generation (RAG) has become a standard mechanism for grounding language models in external knowledge, yet conventional retrieval based on lexical or semantic similarity is poorly suited for complex reasoning tasks: a se…

  71. As scientific workflows shift from deterministic executables to LLM-based agents, the development practices on offer, such as fine-tuning, reinforcement learning, and prompt-and-go, bury the scientist's judgment. We propose treating agent…

  72. Open-source local CLI that validates OpenAI/Qwen chat JSONL and ShareGPT-style fine-tuning datasets — broken roles, empty messages, duplicates, encoding artifacts, context-window overflows — before they poison a training run.

  73. Making a vintage LLM from scratch In this blog post, I will share the adventures I had creating my own LLM, from (almost) scratch, trained only on old texts. I made my own base-training and fine-tuning scripts, data processing pipelines an…

  74. Large language models (LLMs) are moving from cloud-centric services toward on-device embedded AI, where models interact with private, longitudinal signals sensed from users and their physical environments. Mobile phones are a natural platf…

  75. Fine-tuning large language models (LLMs) improves performance but introduces critical safety vulnerabilities: even minimal harmful data can severely compromise safety measures. We observe that perturbations orthogonal to the alignment dire…

  76. Fine-tuning large language models (LLMs) achieves strong performance but is often limited by the memory overhead of backpropagation. Zeroth-order (ZO) optimization avoids this overhead by estimating gradients through forward passes alone,…

  77. Domain fine-tuning degrades the safety of large language models: fine-tuned specialists readily comply with harmful prompts framed in domain language. Existing inference-time defenses that mix logits from a safe anchor model require both m…

  78. Neural operators approximate mappings between function spaces, but often generalize poorly to other operators and usually require fine-tuning or retraining. In-Context Operator Networks (ICON) addresses this issue by prompting the model wi…

  79. Vision-Language-Action (VLA) models have demonstrated remarkable zero-shot generalization in robotic manipulation, yet the vast majority of pre-trained pipelines remain strictly confined to low-DoF parallel grippers. Adapting these rich se…

  80. There are two main Parameter-Efficient Fine-Tuning (PEFT) techniques for Large Language Models (LLMs). While Low-Rank Adaptation (LoRA) introduces additional weights between the LLM layers, Soft Prompting introduces additional fine-tuning-…

  87. Supervised fine-tuning with synthetic rationale data is widely assumed to improve language model performance on clinical prediction tasks by teaching models not just what to predict but why. We test this assumption on five-year Alzheimer's…

  102. Financial transaction processing requires extracting structured merchant information from noisy, abbreviated bank transaction strings at scale. Our current production system, a LoRA-fine-tuned LLaMA 3.1-8B, achieves 96.95% F1 on this task,…

  103. Hi HN! This is Arseniy from Superlog (YC P26).

  104. Global academic literature at your fingertips. Reliable Google Scholar alternative for large-scale access to academic PDFs and metadata, with full-text search and bulk download.

  108. Deploying Small Language Models (SLMs) on edge devices requires efficient fine-tuning strategies that adapt models to new tasks without degrading their general capabilities. In this study, we benchmark five sub-1B models (135M-1B) on mathe…

  109. Open-weight LLMs are increasingly fine-tuned into customized assistants, but downstream fine-tuning can weaken safety alignment and make models more vulnerable to malicious prompts, even when the training data is not intentionally harmful.…

  110. While Large Language Models (LLMs) excel in code generation, they remain prone to replicating subtle yet critical vulnerabilities endemic to their training data. Current alignment techniques, such as Supervised Fine-Tuning (SFT) and Reinfo…

  114. T2I models cannot effectively capture sentiment from various types of text, including diaries, as they primarily focus on visual object-related patterns rather than contextual emotional understanding. This paper proposes an emotion-aware t…

  115. The rapid evolution of Large Language Models (LLMs) has established cross-lingual versatility as a defining feature of modern systems. However, fine-tuning these models frequently induces negative interference across languages.

  116. In my predictions for 2030 I wrote that tech writers would be using specialized LLMs, running locally on powerful hardware. I see hints of this move to “local first” among engineering pundits, but we’re not there yet, in part because of ho…

  117. May 29, 2026 It’s been quite some time since major LLM providers introduced the behaviour that the chatbots often end their response with a question. The motivation is clear: more engagement, more data to train on.

  118. For the last month i've been trying to fine-tune jina-v5 (which has performed best on my corpus out of the box) on slovak law chunks, time and time again no matter what i do I can't get the model to learn nuance of slovak syntax. here's th…

  119. Posted this to r/MachineLearning a couple weeks ago (30K views, 100+ upvotes) and have been meaning to share it here where the fine-tuning angle is more directly relevant. I spent years building and processing a complete Usenet corpus from…

  120. A skill is external state for an agent. Instead of fine-tuning a model or hand-maintaining prompts, SkillOpt runs the frozen agent on scored batches, asks a separate optimizer model to propose structured edits, and accepts a candidate only…

  121. I've fine-tuned Qwen 3.5 0.8B on the dataset provided by Pangram with their EditLens paper. It's available via a Chrome extension; you can just click selected text and it's going to give you the probability distribution of how likely it is…

  122. My use-cases will be to test open-weight LLMs and work on harnesses, inference systems and possibly other non-ML workflows (CS-related) in the future. Fine-tuning would not be something I do locally because I can rent a B200 from RunPod fo…

  123. I think I had GPT-5.5 leak its trace during a normal conversation, and it really reads like the caveman mode fad from a few months back. Maybe we can achieve better token efficiency by taking some high-quality thinking trace from an open m…

  124. I.Introduction Transformer-based PLMs [1],[2],[3],[4],[5] have demonstrated remarkable performance across a wide range of NLP tasks. To fully harness the potential of PLMs, fine-tuning is commonly employed to adapt them to task-specific da…

  125. I've kept a personal diary since 2019. Last week I fed 200+ entries to an LLM and asked it how I've changed over 7 years.

  126. If you look at the cosine sim between the embeddings of "a 500 hp car", "a 1,200 hp car" and "a 73 hp car", you'll soon see that embedding models have no sense of number ordering at all. (I tested Qwen and ModernBERT-based embeddings) It m…

  127. Fine-Tuning NVIDIA Cosmos Predict 2.5 with LoRA/DoRA for Robot Video Generation Motivation NVIDIA Cosmos Predict 2.5 is a large-scale world model capable of generating physically plausible videos conditioned on text, images, or video clips…

  128. RAG vs. Fine-Tuning — The Question Every AI Builder Gets Wrong AI models don't know your private data.

  129. Qwopus3.5-9B-coder is specially optimized and fine-tuned for high-performance 🤖 Agentic Coding, complex Tool Calling, and logical reasoning. 💡 Why the 9B Dense Model?

  130. I proposed two architectures for enabling LLMs to learn daily from personal interactions: Internal KV-Sphere Architecture (IKSA) Background Micro Fine-Tuning (BMFT) Both work with zero GPU and zero catastrophic forgetting. Full paper: in c…

  131. Liquid Harness is an autonomous agent by Liquid AI that takes a plain-English spec and ships a fine-tuned Liquid Foundation Model. Spec, data, eval, training, deployment — all in one run.

  132. I was MLOps lead at an AI company managing 5000+ GPUs across GCP and CoreWeave. Left to start my own thing and now I'm back to renting GPUs like everyone else.

  133. Been fine-tuning llama 3.1 8b with Qlora for a classification task using about 8k samples. I was getting bad eval results for a while and kept thinking something was wrong with my data.

  134. Open source repo: https://github.com/grctest/finetuned-gemmatranslate-cy 5% of the fine-tuning took 40 minutes and cost a couple dollars to prove the process works. Looking forwards to Flash Attention v4 to leave beta, to test fine-tuning…

  135. Hello there people. So I have noticed that people are pretty much ignoring Llama 3 plus 3.1, 3.2, and 3.3 these days.

  136. How to Fine-Tune LLMs on AMD Strix Halo (Ryzen AI MAX+ 395) and Other Exotic AMD Hardware A Complete Windows and Linux Guide to Full SFT and LoRA Training This guide covers full SFT and LoRA fine-tuning on AMD hardware that sits outside th…

  137. GenZ LLM A post-trained language model that responds in GenZ slang, built on top of Qwen2.5-0.5B-Instruct using Supervised Fine-Tuning (SFT) followed by Reinforcement Learning with GRPO. The fine-tuned model is available on Hugging Face: a…

  138. Hey everyone I’m Jaguar, building Jungle Grid. We just open-sourced our MCP server for agentic GPU workload execution.

  139. Most "AI progress" talk lives on one layer: models. Bigger model, smaller model, new benchmark, repeat.

  140. I want to move past the "democratization" slogans. What is the most practical contribution consumer-grade hardware can make to the ecosystem right now?

  141. ​ Fine-tuning UI with AI right now: "Make the shadow softer." "Stronger." "No, less." "Go back." "A bit more." 17 messages later, you've spent more tokens than the shadow is soft. I built something that breaks the loop.

  142. MedQA: Fine-Tuning a Clinical AI on AMD ROCm — No CUDA Required The Idea Medical question answering is one of those tasks where the stakes are genuinely high. A model that confidently picks the wrong answer on a clinical MCQ isn't just wro…

  143. Got an email today about the announcement. > OpenAI is winding down the fine-tuning API and platform.

  144. Fine-tuning is one of today's most computationally intensive workloads, and it continues to push hardware to its limits. NVIDIA GPUs are purpose-built for these workloads: they break complex problems into pieces and process them in paralle…

  145. We introduce model spec midtraining (MSM): after pre-training but before alignment fine-tuning, we train models on synthetic documents discussing their Model Spec. This shapes how models generalize from subsequent alignment training.

  146. I'm part of the team building Transformer Lab, an open source ML research platform. We put together a short demo of how to run text to speech training, which you can do on your own hardware using a Local provider.

  147. Anthropic's alignment team published a paper this week called Model Spec Midtraining (MSM) and I think it's one of the more practically interesting alignment results I've seen in a while. The core problem they're solving: Current alignment…

  148. Two weeks before a Fortune 500 product launch, we told a client to scrap their fine-tuned model and rebuild with RAG instead. They lost eight weeks and $180K.

  149. No, I won't tell you how. No this is not for anyone who is not already a proven contributor to the fine-tuning space.

  150. Import AI 455: AI systems are about to start building themselves. Jack Clark thinks there’s a ~30% chance by the end of 2027 and a ~60%+ chance by the end of 2028 that AI research becomes automated, with models eventually helping train the…

  151. I've been using LLMs to help write my thesis, but the output feels dry and uses awkward phrasing (especially in translation). I'm looking to fine-tune an accessible LLM to better match natural academic writing in my language.

  152. https://huggingface.co/datasets/angrygiraffe/claude-opus-4.6-4.7-reasoning-8.7k A synthetic fine-tuning dataset created from Claude 4.6/4.7. 8,706 total examples all with reasoning.

  153. I have (free) access to a SLURM cluster with 8x NVIDIA A100 80GB GPUs (=640 GB VRAM) on a single task, and I want to run an open-weight model locally with llama.cpp for data generation, not coding. My use case is generating teacher data fo…

  154. We tested prompting, fine-tuning, RL, and grounded evaluation across ~1,500 labeled flashcards—and found models catch obvious misses but not plausible failures.

  155. Hey Everyone, Over the last few months, I noticed a massive gap in how we learn about Agentic AI. There are a million theoretical blog posts and dense whitepapers on RAG, tool calling, and swarms, but almost nowhere to just sit down, run a…

  156. Hi HN, I'm Danilo. I've been struggling with the limitations of AdamW when fine-tuning LLMs locally.

  157. Hey Everyone, Over the last few months, I noticed a massive gap in how we learn about Agentic AI. There are a million theoretical blog posts and dense whitepapers on RAG, tool calling, and swarms, but almost nowhere to just sit down, run a…

  158. I put together a hands-on tutorial that takes you from problem framing to fine-tuning, step by step. I decided to build a wildfire prevention system that uses satellite images and a Small Vision-Language Model (LFM2.5-VL-450M) to extract r…

  159. We find that a small amount of fine-tuning on instruction following in the CoT generalizes to meaningful increases in CoT controllability on an out-of-distribution set of tasks. We fine-tune four reasoning models on small datasets of instr…

  160. Spent a week doing LoRA fine-tuning on Gemma 4 E2B (~5.1B total params, ~2B active in text decoder) for a narrow Python code-generation task. Bad outputs went from ~5% to 0% (greedy) and 1.5% (sampled) across 134 tests.

  161. Spent a week doing LoRA fine-tuning on Gemma 4 E2B (gemma-4-e2b-it, ~5.1B total params, ~2B active in the text decoder) for a narrow Python code-generation task. Setup: Model: Gemma 4 E2B, bf16, language_model only (vision + audio towers f…

  162. Unlike LoRA and its variants, which inject trainable parameters directly into the weights of the Transformer, requiring tight coupling with the backbone. ShadowPEFT instead enhances the frozen large base model by adding a lightweight, cent…

  163. We want to set up the following: A Local LLM environment for AI development, used by multiple software developers Infrastructure for training Vision AI models Capabilities for AI model fine-tuning I’m currently struggling to decide between…

  164. A 15B-parameter token-mixer supernet with 8 optimized deployment presets spanning 1.0× to 10.7× decode throughput at 32K sequence length, all from a single checkpoint. Derived from Apriel-1.6 through stochastic distillation and targeted su…

  165. I've built software for clients for 38 years and kept hitting the same wall: weeks spent scaffolding the data layer and the Clean Architecture around it before any real work begins. I asked Claude to estimate how long it would take to gene…

  166. +30% avg accuracy lift on classification & extraction tasks vs. base Gemma ~7 days until your first auto-improvement run lands in production 0 lines of fine-tuning code you have to write, ever $0/retrain starting price.

  167. Memory-augmented Large Language Models (LLMs) are essential for developing capable, long-term AI agents. Recently, applying Reinforcement Learning (RL) to optimize memory operations, such as extraction, updating, and retrieval, has emerged…

  168. Hey, I've been building side projects with Claude Code for a few months, but I'm completely new to fine-tuning — started experimenting maybe a week ago. From day one I wanted a GUI for the dataset side of the workflow, so this desktop app…

  169. Hey everyone, We just open-sourced our reasoning model, Chaperone-Thinking-LQ-1.0, on Hugging Face. It's built on DeepSeek-R1-Distill-Qwen-32B but goes well beyond a simple quantization — here's what we actually did: The pipeline: 4-bit GP…

  177. I've been building an open Q&A dataset for the Swedish construction industry (byggbransch) over the last few weeks — something that's been a gap in Swedish-language domain-specific datasets. Finally hit a milestone worth sharing.

  178. Writing a fine-tuning and deployment pipeline isn't as easy as it looks (Gemma 4 Version) Fine-tune and deploy Gemma 4 on Oxen.ai Google's Gemma 4 dropped in April 2026 with multimodal support (text, image, video, audio), a novel hybrid KV…

  179. Summary of Findings This issue documents what we learned making Gemma4 26B-A4B-it train on consumer hardware (RTX 4090, 24GB VRAM). No A100.

  180. I have significantly distilled my AI Agents and Skills definitions. My goal is to reduce the context size and token usage without impacting the quality of my development team.

  181. Hey HN! I'm a solo dev and I just wanted to share my latest Android game — Rollquation.

  182. Hey r/LocalLLaMA, I've been running a small 4-node DGX Spark cluster on a 400µT fabric switch and got frustrated with the usual raw Ray/vLLM scripts and EXO basically ignoring pure NVIDIA paths. I started from the solid foundation in [eugr…

  183. We’ve released a 100,000-sample Chain-of-Thought (CoT) dataset for fine-tuning local reasoning models. Each sample includes explicit intermediate reasoning traces, rather than answer-only supervision.

  184. Friday — A 24/7 AI Assistant Built Entirely on Claude Code An always-on personal AI system using only Claude Code CLI ($100/month) and Telegram — no custom AI, no cloud VMs, no fine-tuning. Live page: missingus3r.github.io/friday-showcase…

  185. A guide to model quantization in fine-tuning (and how to pick the right GGUF) About this post Fine-tuning with Unsloth and Axolotl is, on the whole, a well thought-out experience where a lot of the complexity is handled for you. However on…

  186. TL;DR: Fine-tuned Chatterbox-Multilingual for Telugu, Kannada, Bengali, Tamil, Malayalam, Marathi, Gujarati, and Hindi using LoRA adapters + tokenizer extension. Only 7.8M / 544M parameters trained.

  187. 🌟 Gemopus-4-26B-A4B-it [!NOTE] Gemopus is an attempt at fine-tuning Gemma 4 with a core philosophy of "stability first". While preserving the original reasoning order of Gemma 4 as much as possible, we conducted targeted refinements for an…

  188. Your brand voice is probably a PDF nobody reads, or it's trapped in one founder's head, or it's scattered across a thousand ChatGPT histories. I wanted to treat it like code instead — a file you can version, share, diff, and plug into any…

  189. Was fine-tuning a Japanese ASR model (based on Qwen3-ASR) to handle technical terminology better. The model clearly improved — "Next.js" comes out as "Next.js" instead of "ネクストジェイズ", punctuation works, etc.

  190. Trained a 125M LM from scratch (custom tokenizer) + released instruct checkpoint and SFT framework so others can fine-tune their own variants I’ve been experimenting with training small language models fully from scratch (no GPT-2 init, no…

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