H2O LLM Studio
Ein No-Code-GUI-Framework für das Fine-Tuning von Large Language Models mit intuitivem Dashboard und Experiment-Tracking
Überblick
Anforderungen
Empfohlener VPS
Hostinger · KVM 4
4 vCPU · 16384 MB · 200 GB
Hostinger · KVM 4
4 vCPU · 16384 MB · 200 GB
Hostinger · KVM 8
8 vCPU · 32256 MB · 400 GB
Affiliate-Hinweis
Docker Compose
# Generated by Run This Ai — docker-compose.yml
services:
h2o-llmstudio:
image: glavin001/h2o-llmstudio:latest
restart: unless-stopped
ports:
- 8080:8080
volumes:
- ./data/h2o-llmstudio:/data
Verwandte Tools
NextChat
Cross-platform ChatGPT web UI with multi-model support for Ollama, Claude, Gemini, and more
Lobe Chat
Extensible, open-source ChatGPT alternative with plugins, knowledge base, and multi-LLM support
Open WebUI
User-friendly WebUI for LLMs (Ollama, OpenAI API)
text-generation-webui
Run local LLMs with a powerful web interface — text, vision, tool-calling, and OpenAI-compatible API
Streamlit
Build and share data apps in pure Python — fast
Gradio
Build and share delightful machine learning apps in Python
Anleitungen & Artikel
H2O LLM Studio Review: No-Code LLM Fine-Tuning Made Easy
An honest hands-on review of H2O LLM Studio. Performance benchmarks, strengths, limitations, and whether it is right for your LLM fine-tuning workflow.
How to Fine-Tune LLMs with H2O LLM Studio: A Complete Guide
Learn how to fine-tune large language models using H2O LLM Studio's no-code interface. Step-by-step guide covering datasets, training, and deployment.