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unsloth-inference

Optimize fine-tuned LLM inference with Unsloth kernels, vLLM, or SGLang.

Updated Aug 27, 2026
Search Tags:#optimization#llm#inference#vllm#serving#sglang
One-click install
npx skills add https://github.com/cuba6112/skillfactory --skill unsloth-inference
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: unsloth-inference
Source: https://github.com/cuba6112/skillfactory/tree/main/skills/unsloth-inference
Command: npx skills add https://github.com/cuba6112/skillfactory --skill unsloth-inference

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires unsloth, torch, vllm, sglang, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill optimizes the deployment of fine-tuned large language models for production inference, significantly improving speed and reducing VRAM usage.

Core Features & Use Cases

  • Native Optimized Inference: Achieve 2x faster inference locally using FastLanguageModel.for_inference().
  • Production Serving: Merge LoRA weights for deployment with high-throughput engines like vLLM or SGLang.
  • OpenAI-Compatible API: Easily serve models locally for drop-in replacement in existing applications.
  • Use Case: Deploy a fine-tuned LLM for a customer support chatbot that needs to respond quickly and handle a high volume of user queries.

Quick Start

Load the fine-tuned model and run local optimized inference using the provided script.

Frequently Asked Questions about unsloth-inference

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I accelerate LLM inference for a fine-tuned model locally?▼

To accelerate LLM inference locally, you can use the Unsloth library's native kernel optimizations via FastLanguageModel.for_inference() to achieve up to 2x faster speeds and reduced VRAM usage.

What is the best way to serve a fine-tuned LLM in production with high throughput?▼

To serve a fine-tuned LLM in production with high throughput, merge LoRA weights and deploy using serving engines like vLLM or SGLang to handle high volumes of user queries.

Can I use vLLM or SGLang with Unsloth for optimized model serving?▼

Yes, you can use vLLM or SGLang with Unsloth. The Unsloth library provides optimized kernels, while vLLM or SGLang handle advanced production serving scenarios and high-throughput endpoints.

How do I create an OpenAI-compatible API for my local LLM?▼

To create an OpenAI-compatible API for your local LLM, serve your fine-tuned model locally using the provided scripts, allowing it to act as a drop-in replacement for existing OpenAI API applications.

Why does fine-tuned LLM inference consume so much VRAM?▼

Fine-tuned LLM inference consumes high VRAM without optimization due to standard kernel overhead. Using native optimized kernels from the Unsloth library significantly reduces this VRAM usage.

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