Fine-Tuning on English AI Terms Dictionary

Prefix Tuning

Sat, 18 Jul 2026 10:11:14 +0000

Definition

Prefix Tuning is a parameter-efficient adaptation technique for pre-trained transformers. Instead of updating all model weights, it prepends a sequence of trainable continuous vectors (the prefix) to the input embeddings of each layer. These prefixes act as soft prompts that guide the model’s behavior for specific downstream tasks while keeping the base model frozen. This approach significantly reduces memory and computational costs compared to full fine-tuning, making it suitable for resource-constrained environments.

P-Tuning

Sat, 18 Jul 2026 10:10:06 +0000

Definition

P-Tuning (Prompt Tuning) is a technique designed to adapt large pre-trained language models to specific downstream tasks with minimal computational cost. Instead of fine-tuning all model parameters, it introduces trainable virtual tokens (embeddings) at the input layer. The pre-trained model’s weights remain frozen, and only these prompt embeddings are updated during training. This approach significantly reduces memory usage and training time while maintaining performance comparable to full fine-tuning on many NLP tasks.

Dataset:Jackrong/Qwen3.5 Reasoning 700X

Sat, 18 Jul 2026 09:53:44 +0000

Definition

This entry refers to a specific dataset repository identified by the identifier ‘Jackrong/Qwen3.5 Reasoning 700X’. It is typically used in the context of supervised fine-tuning (SFT) or reinforcement learning from human feedback (RLHF) to improve the logical deduction and problem-solving skills of base models. The dataset likely contains high-quality reasoning traces, chain-of-thought examples, or mathematical/logical puzzles designed to push the boundaries of a model’s analytical performance, specifically targeting the Qwen architecture family.

QLoRA

Sat, 18 Jul 2026 09:42:48 +0000

Definition

QLoRA combines Low-Rank Adaptation (LoRA) with 4-bit quantization to significantly reduce the memory footprint required for fine-tuning massive models. By storing weights in 4-bit format and adding trainable low-rank decomposition matrices, it enables fine-tuning of models with billions of parameters on consumer-grade hardware. This technique maintains performance comparable to full-precision fine-tuning while drastically lowering computational costs and increasing accessibility.

Summary

Quantized Low-Rank Adaptation, a method for efficiently fine-tuning large language models using 4-bit quantization and low-rank adapters.

Supervised Fine-tuning

Sat, 18 Jul 2026 09:42:48 +0000

Definition

Supervised Fine-tuning (SFT) involves taking a large pre-trained model, such as a language model, and continuing its training on a smaller, high-quality dataset labeled for a specific downstream task. Unlike initial pre-training which learns general patterns, SFT aligns the model’s behavior with human preferences or specific instructions, significantly improving performance on niche tasks without requiring training from scratch.

Summary

The process of further training a pre-trained model on a specific dataset to adapt it to a particular task or domain.

Adapter

Sat, 18 Jul 2026 09:39:58 +0000

Definition

Adapters are a parameter-efficient fine-tuning technique used primarily in large language models and transformers. Instead of updating all model weights, which is computationally expensive, adapters introduce small, task-specific neural network layers between existing layers. This allows the model to retain its general knowledge while adapting to new tasks with minimal additional parameters. It significantly reduces memory usage and storage requirements, making it feasible to deploy multiple specialized models on top of a single base model without catastrophic forgetting.

Reinforcement Learning from Human Feedback

Sat, 18 Jul 2026 09:36:45 +0000

Definition

Reinforcement Learning from Human Feedback (RLHF) is a method used to fine-tune large language models so their outputs align better with human values and expectations. It typically involves three steps: collecting human preference data, training a separate reward model based on this data, and then using reinforcement learning (often Proximal Policy Optimization) to adjust the main model to maximize the reward predicted by the model. This results in more helpful, honest, and harmless responses.

LoRA

Sat, 18 Jul 2026 09:33:34 +0000

Definition

LoRA freezes pre-trained model weights and inserts trainable decomposition matrices into each layer of the Transformer architecture. By optimizing only these low-rank matrices, LoRA significantly reduces the number of trainable parameters, memory footprint, and computational cost during fine-tuning. This technique allows for rapid adaptation to specific downstream tasks while maintaining the general knowledge of the base model, making it highly popular for efficient custom model training.

Summary

Low-Rank Adaptation is a parameter-efficient fine-tuning method that injects trainable rank decomposition matrices into existing model weights.