BiOS Blog
Insights, guides, and best practices for AI fine-tuning, alignment, and model training
What is BiOS: The Complete AI Training Platform
BiOS is a managed AI training platform supporting 250k+ open models with 15+ training methods and 6 alignment algorithms. Learn everything about BIOS.
How BIOS Compares to Other AI Fine-Tuning Platforms
Compare BIOS to other AI fine-tuning platforms. BIOS offers 15+ training methods, 6 alignment algorithms, vision-language model support, and per-second billing.
Complete Guide to Supervised Fine-Tuning (SFT) for LLMs
Learn supervised fine-tuning (SFT) for LLMs from scratch. Covers dataset format, adapter selection, hyperparameter tuning, and training on BIOS with real-time metric monitoring.
LoRA vs QLoRA: Parameter-Efficient Fine-Tuning Explained
A practical comparison of LoRA and QLoRA for parameter-efficient LLM fine-tuning. Compare memory requirements, quality tradeoffs, and learn how to configure them on BIOS.
Full Fine-Tuning: When and Why to Train Every Parameter
Understand when full fine-tuning outperforms LoRA and QLoRA. Learn VRAM requirements, dataset size thresholds, cost optimization strategies, and how to configure full fine-tuning on BIOS.
DPO: Direct Preference Optimization for LLM Alignment
Learn how DPO (Direct Preference Optimization) aligns LLMs with human preferences without a reward model. Covers dataset format, the DPO loss function, training on BIOS, and best practices.
SimPO: Simple Preference Optimization Without Reference Models
Learn about SimPO, a preference optimization algorithm that eliminates the reference model requirement. Understand how it reduces memory usage while matching DPO quality.
ORPO: Odds Ratio Preference Optimization
Learn about ORPO, which combines supervised fine-tuning and preference alignment in a single training stage. Understand when ORPO is more efficient than separate SFT + DPO.
CPO: Contrastive Preference Optimization for LLM Alignment
Understand CPO, a contrastive approach to preference optimization that keeps chosen response probabilities high while suppressing rejected responses.
KTO: Kahneman-Tversky Optimization for AI Alignment
Understand KTO, an alignment method based on prospect theory that works with single-response feedback (thumbs up/down) instead of paired preferences.
Reward Modeling for RLHF: Training Custom Reward Functions
Learn how to train reward models for RLHF. Understand the reward model pipeline, dataset preparation, evaluation metrics, and online RL methods coming to BIOS.
Continued Pre-Training: Domain Adaptation for Large Language Models
Learn when and how to use continued pre-training to adapt LLMs to specialized domains like medical, legal, financial, and code. Covers dataset prep and BIOS configuration.
VLM Fine-Tuning: How to Train Vision-Language Models
Learn how to fine-tune vision-language models including InternVL, Qwen-VL, LLaVA, and DeepSeek-VL. Covers VLM dataset formats, training methods, and use cases on BIOS.
LLM vs VLM Fine-Tuning: Key Differences and When to Choose Each
Compare text-only LLM fine-tuning with vision-language model (VLM) fine-tuning. Understand dataset formats, training differences, memory needs, and use cases.
Dataset Preparation for AI Fine-Tuning: Formats and Best Practices
Complete guide to preparing datasets for LLM and VLM fine-tuning. Covers JSONL, Parquet, CSV formats, SFT and preference structures, quality guidelines, and BIOS validation.
Adapter Types Compared: LoRA, QLoRA, Full Fine-Tune and Beyond
Compare all adapter types for LLM fine-tuning: LoRA, QLoRA, full fine-tune, AdaLoRA, LoHa, BOFT, and ReFT. Learn which adapter fits your use case and budget.
RLHF Methods: Offline Alignment vs Online Reinforcement Learning
Compare offline alignment methods (DPO, SimPO, ORPO, CPO, KTO) with online RL (PPO, GRPO, GKD). Understand when to use each and what is coming to BIOS.
Per-Second GPU Billing: How to Optimize AI Training Costs
Learn how per-second billing works on BiOS and how to optimize training costs. Compare with hourly billing, estimate costs, and choose the right GPU tier.