Table of Contents

Fine-tuning

LoRA and QLoRA

• Fine-tune Mixtral-8x7B Quantized with AQLM (2-bit) on Your GPU*

• QLoRa: Fine-Tune a Large Language Model on Your GPU*

• Fine-tune LLMs on Your CPU with QLoRA*

• Fine-tune a Chat Model on Your Data with QLoRa*

• QA-LoRA: Quantization-Aware Fine-tuning for Large Language Models

• Fine-tune Quantized Llama 2 on Your GPU with QA-LoRA*

• LQ-LoRA: Jointly Fine-tune and Quantize Large Language Models*

• Combine Multiple LoRA Adapters for Llama 2

• LoRA Adapters: When a Naive Merge Leads to Poor Performance*

• Don't Merge Your LoRA Adapter Into a 4-bit LLM*

• DoRA: Better and Faster than LoRA?*

Direct Preference Optimization (DPO) and Identity Preference Optimization (IPO)

• Fine-tune Better Chat Models with Distilled Identity Preference Optimization (IPO)

• Fine-tune Your Own Instruct Version of Mistral 7B with Direct Preference Optimization (DPO)*

• Zephyr 7B Beta: A Good Teacher Is All You Need

• A Cheap Zephyr 7B Beta: Distilled DPO on Consumer Hardware*

Reinforcement Learning with Human Feedback (RLHF)

• Train Instruct LLMs On Your GPU with DeepSpeed Chat — Step #1: Supervised Fine-tuning

• Train Instruct LLMs On Your GPU with DeepSpeed Chat — Step #2: Training a Reward Model*

• Train Instruct LLMs On Your GPU with DeepSpeed Chat — Step #3: Reinforcement Learning with Human Feedback*

Optimization

• Use FlashAttention-2 for Faster Fine-tuning and Inference*

Quantization

AQLM

• Run a 7.7x Smaller Mixtral-8x7B on Your GPU with AQLM 2-bit Quantization

GPTQ

• From 16-bit to 2-bit: Finding the Best Trade-off Between Memory-Efficiency and Accuracy*

• Quantization of Llama 2 with GTPQ for Fast Inference on Your Computer*

• GPTQ or bitsandbytes: Which Quantization Method to Use for LLMs — Examples with Llama 2*

• Quantize and Fine-tune LLMs with GPTQ Using Transformers and TRL*

AWQ

• Fast and Small Llama 2 with Activation-Aware Quantization (AWQ)*

• Simple, Fast, and Memory-Efficient Inference for Mistral 7B with Activation-Aware Quantization (AWQ)

bitsandbytes NF4

• Run Llama 2 Chat Models on Your Computer

ExLlama

• Run Llama 2 70B on Your GPU with ExLlamaV2*

SqueezeLLM

• SqueezeLLM: Better 3-bit and 4-bit Quantization for Large Language Models

Efficient Loading and Inference

• GGUF Quantization for Fast and Memory-Efficient Inference on Your CPU*

• vLLM: Serve Fast Mistral 7B and Llama 2 Models from Your Computer*

• Speculative Decoding for Faster Inference with Mixtral-8x7B and Gemma*

• unsloth: Faster and Memory-Efficient QLoRA Fine-tuning*

• Device Map: Avoid Out-of-Memory Errors When Running Large Language Models

• Run Very Large Language Models on Your Computer

• Safe, Fast, and Memory Efficient Loading of LLMs with Safetensors*

• High-Speed Inference with llama.cpp and Vicuna on CPU*

• vLLM: PagedAttention for 24x Faster LLM Inference

• Serve Large Language Models from Your Computer with Text Generation Inference

• Deploy Your Local GPT Server With Triton

Pre-training

• TinyLlama: Pre-training a Small Llama 2 from Scratch

• ReLoRa: Pre-train a Large Language Model on Your GPU*

Merge and Mixture of Expert

• The Mayonnaise: Rank First on the Open LLM Leaderboard with TIES-Merging*

• Fine-tune a Mixture of Experts on Your Computer*

• Maixtchup: Make Your Own Mixture of Experts with Mergekit*

• Run Mixtral-8x7B on Consumer Hardware with Expert Offloading*

• Mixtral-8x7B: Understanding and Running the Sparse Mixture of Experts by Mistral AI*

Benchmarking

• Optimum-Benchmark: How Fast and Memory-Efficient Is Your LLM?*

• Behind the OpenLLM Leaderboard: The Evaluation Harness*

LLM Focus

Llama 2

• Fine-tune Llama 2 on Your Computer with QLoRa and TRL*

• Llama 2 on a Budget

• Platypus: Dataset Curation and Adapters for Better Large Language Models

• What You Cannot Do With Llama 2

Falcon

• Insights from the Falcons

• Fine-tune Falcon-7B on Your GPU with TRL and QLoRa

• Simple and Quick Fine-Tuning of Falcon Models with QLoRA

• Introduction to the Open LLM Falcon-40B: Performance, Training Data, and Architecture*

• Falcon 180B: Can It Run on Your Computer?

• Can You Use the Falcon Models For Commercial Applications?

Mistral 7B

• Mistral 7B: Recipes for Fine-tuning and Quantization on Your Computer

Microsoft phi-1.5 and phi-2

• Phi-2: A Small Model Easy to Fine-tune on Your GPU

• How to Fine-tune, Quantize, and Run Microsoft phi-1.5

Google Gemma

• Google's Gemma: Fine-tuning, Quantization, and Inference on Your Computer*

Qwen

• Fine-tuning and Quantization of Qwen1.5 LLMs on Your Computer

Machine Translation

Fine-tuning

• Llama 2 MT: Turn Llama 2 into a Translation System with QLoRA

GPT

• GPT-3.5 Translates Paragraphs Better

• Translate with ChatGPT

• Translate with GPT-3

Evaluation

• BLEU: A Misunderstood Metric from Another Age

• 12 Critical Flaws of BLEU

• Traditional Versus Neural Metrics for Machine Translation Evaluation

• Scientific Credibility in Machine Translation Research: Pitfalls and Promising Trends

• How Good Is Google PaLM at Translation?

Data Processing

• Datasets to Train, Validate, and Evaluate Machine Translation

• Data Preprocessing for Machine Translation

*: Articles with sections only accessible to paid subscribers