Making LLMs lighter with AutoGPTQ and transformers

Optimizing your LLM in production

Fine-tuning LLMs to 1.58bit: extreme quantization made easy

Make LLM Fine-tuning 2x faster with Unsloth and 🤗 TRL

Timm ❤️ Transformers: Use any timm model with transformers

Preference Tuning LLMs with Direct Preference Optimization Methods

Introducing AutoRound: Intel’s Advanced Quantization for LLMs and VLMs

Making LLMs even more accessible with bitsandbytes, 4-bit quantization and QLoRA

Releasing Swift Transformers: Run On-Device LLMs in Apple Devices

Fine-tuning 20B LLMs with RLHF on a 24GB consumer GPU

Understanding and Coding the KV Cache in LLMs from Scratch

KV caches are one of the most critical techniques for efficient inference in LLMs in production.

Recent Developments in LLM Architectures: KV Sharing, mHC, and Compressed Attention

From Gemma 4 to DeepSeek V4, How New Open-Weight LLMs Are Reducing Long-Context Costs

Rethinking LLM Evaluation with 3C3H: AraGen Benchmark and Leaderboard