#parameter-efficient training
173 related articles
·7 min
Complete Guide to Training Small Models on Kaggle's Free Compute: GPU Quotas and Practical Tips
In-depth guide to Kaggle's free-tier compute: P100/T4 GPU with 30 hours/week quota, 12-hour sessions, suitable models like CNN and BERT fine-tuning, plus tips like mixed precision and checkpointing to start deep learning at zero cost.
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·6 min
Samsung's Chip Profit in a Single Year Exceeds 40 Years Combined: The AI Wave Triggers a Memory Price Crisis
Samsung's chip division is set to earn more profit in one year than in its past 40 years combined, with quarterly profit surging 19x YoY, surpassing NVIDIA. AI data centers are seizing HBM and DRAM capacity, driving up DDR5 and SSD prices.
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·6 min
What Is an FDE? Decoding the Most Sought-After Hybrid Engineer in the AI Deployment Era
FDE (Forward Deployed Engineer) is the hottest emerging role in the AI deployment wave, combining a technical CTO, full-stack AI engineer, and business consultant. Learn the two FDE tracks, core skills, and how to transition into one.
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·2 min
Unsloth v0.1.462-beta: Keyboard Navigation and Accessibility Improvements for Model Picker
Unsloth v0.1.462-beta adds full keyboard navigation to the Studio Model Picker, fixes Tab focus order, and improves accessibility for LLM fine-tuning workflows.
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·4 min
AI Weekly: GPT-5.6 Launches in Three Tiers, Open-Source Inference Frameworks and Robot Safety Break Through Across the Board
This week in AI: OpenAI launches GPT-5.6 in three tiers (Sol/Terra/Luna) hitting 91.9% on coding benchmarks; DeepSeek and PKU open-source DSpark for 85% faster inference; Prime Intellect trains trillion-param models on just 28 H200s; Anthropic Claude enters Slack.
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·4 min
FTPO: A Training-Level Solution to AI Repetition Loops
FTPO (Final Token Preference Optimization) tackles AI "Doom Loops" at the training level rather than patching them at inference time — exploring its principles, value for small/quantized models, and open challenges.
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·6 min
Introduction to Prompt Engineering: A Complete Guide from Understanding to Implementation
Prompt engineering is more than messaging AI. This guide breaks down the four core functions of prompts, the six-step prompt engineering process, and key limitations to help you build the right foundation.
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·6 min
Prompt Engineering in Practice: A Programmer's Core Methodology for Tuning Prompts
An in-depth analysis of prompt engineering from a programmer's perspective: understand token probability generation, master the three principles—specific, rich, low-ambiguity—and learn iterative prompt tuning.
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·7 min
Prompt Engineering in Practice: A Programmer's Core Methodology for Prompt Tuning
A deep dive into the underlying logic of prompt engineering from a programmer's perspective: understand token-probability generation, master the three principles of specific, rich, and low-ambiguity, and learn iterative prompt tuning.
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·7 min
The Agent Engineer Transition Roadmap: A Deep Dive into Three Core Skill Systems
Want to become an Agent engineer? This article systematically covers three core skill tracks—LLM fundamentals, LangChain architecture development, and enterprise deployment—to help you avoid detours.
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·5 min
Embedded Linux vs. AI Agent Development: A Comprehensive Comparison of Salary, Job Availability, and Career Stability
Embedded Linux or AI Agent development? This in-depth comparison covers salary, job availability, and career stability to help developers pick the right path.
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·7 min
Getting Started with OpenAI LLM Development: A Practical Guide to Model Selection, Token Pricing, and API Usage
A systematic guide to the three cores of OpenAI LLM app development: GPT-4/GPT-3.5 model selection, token billing and cost-saving tips, and practical use of the Models, Completion, and Chat Completion APIs.
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·4 min
Career Switch to LLM Development: A Four-Level Skill Progression Roadmap
Want to switch careers into LLM development but don't know where to start? This guide breaks down a four-level skill roadmap — from basics and API calls to RAG, fine-tuning, Agent development, and multimodal — to help you build real AI career value.
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·7 min
HuggingFace Open-Sources ml-intern: An AI Engineer That Reads Papers and Trains Models Autonomously
Hugging Face's open-source ml-intern autonomously reads papers, writes training scripts, and finetunes LLMs, deeply integrating the HF ecosystem and smolagents. Explore its features and impact on ML careers.
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·4 min
ByteDance Open-Sources Bernini Video Editing: Local Deployment Guide for Character Replacement
ByteDance open-sources Bernini, a video editing model supporting character replacement, outfit swapping, and video blending via ComfyUI. Full local deployment guide.
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·3 min
Unsloth v0.1.45-beta Released: 2x Faster LLM Fine-Tuning with 70% Less VRAM
Unsloth v0.1.45-beta (PyPI: 2026.6.2) delivers 2x faster LLM fine-tuning and up to 70% VRAM reduction. Now at 67.9k GitHub stars, upgrade via pip install.
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·2 min
Training Just One Transformer Layer Can Match Full-Parameter RL Training?
A controversial study shows training just one Transformer layer can match full-parameter RL training. We analyze the technical principles, engineering value, and limitations of this approach.
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·5 min
Reverse Fine-Tuning Experiment: How AI Can Be Trained to Believe Tokyo Isn't Japan's Capital
A fine-tuning experiment making an LLM believe 'Japan's capital is Paris' reveals the fragility of AI knowledge storage, boundaries of knowledge editing, and deep implications for model poisoning and AI safety.
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·3 min
Unsloth Major Update: DiffusionGemma & Gemma 4 MTP Support, 2x Inference Speed Boost
Unsloth v0.1.464-beta adds DiffusionGemma, Gemma 4 MTP, and MiniMax-M3 support, delivering ~2x inference speed boost, new Hub, RAG Q&A, tensor parallelism, and full CUDA/ROCm/Windows coverage.
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·1 min
Unsloth v0.1.47-beta Released: 2x Faster LLM Fine-Tuning with 70% Less VRAM
Unsloth v0.1.47-beta is out. This 67.9k-star open-source framework fine-tunes Llama, Mistral, and Qwen 2x faster with 70% less VRAM on consumer GPUs.
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