Can this skill help with LoRA configuration?

Yes, it provides specific recommendations for LoRA rank (r) and alpha values based on your dataset size to help prevent overfitting.

Is the information up to date?

The skill includes knowledge and trends projected through early 2026, including the rise of MoE models and the cost-efficiency of DeepSeek architectures.

Does it cover recent model architectures?

Yes, the knowledge base includes details on modern architectures such as Dense, MoE (Mixture of Experts), and MLA (Multi-head Latent Attention).

What models are covered in this knowledge base?

It includes detailed information on Qwen, DeepSeek, Llama, and Phi-4, focusing on their specific strengths, use cases, and performance metrics.

How does it help with troubleshooting training?

It provides a quick-lookup table for common symptoms like high evaluation loss or output format errors, offering immediate potential solutions and configuration adjustments.

LLM Fine-Tuning Knowledge Base

Name: LLM Fine-Tuning Knowledge Base
Author: p988744

byp988744

0•

Data Science & ML

Provides structured guidance and best practices for Large Language Model (LLM) fine-tuning, model selection, and troubleshooting.

This skill serves as a specialized domain-specific reference for developers working with Large Language Models, offering immediate access to structured data on model architectures, training methods, and optimization strategies. It provides actionable advice on configuring techniques like LoRA, QLoRA, and DPO, while offering comparative insights into popular models such as Qwen, DeepSeek, and Llama. By integrating deep technical knowledge directly into the workflow, it helps developers resolve training issues like overfitting or loss divergence and helps in selecting the most cost-effective models for specific tasks like reasoning or Chinese NLP.

Key Features

01Model selection guides for Qwen, DeepSeek, Llama, and Phi series

02Comparative analysis of alignment methods including DPO, ORPO, KTO, and SimPO

03Detailed configuration templates for LoRA, QLoRA, and DoRA optimization

040 GitHub stars

05Troubleshooting matrices for common training issues like overfitting and loss divergence

06Architecture deep-dives into Dense, Mixture of Experts (MoE), and MLA

Use Cases

01Implementing preference optimization workflows to align models with human feedback

02Optimizing LoRA hyperparameters (rank, alpha) based on specific dataset sizes

03Selecting the most cost-effective and performant model for multilingual or reasoning-heavy tasks

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add p988744/nlp-skills llm-knowledge

For use in Claude.ai and ChatGPT

Download Skill