Bolin97/GongBU

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1225 stars · 90 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

GongBU is a self-hosted, no-code web platform for fine-tuning, evaluating, and deploying large language models, built on Transformers and PEFT. It is deployed via docker-compose on Linux with NVIDIA GPUs and accessed through a browser by non-technical users.

Use cases

• fine-tune an LLM on my own domain data without writing code
• run a local no-code LLM fine-tuning platform on my GPU server
• let non-technical teammates fine-tune models through a web UI
• evaluate and deploy fine-tuned language models
• adapt a base LLM to a specific domain with PEFT/LoRA
• manage datasets and models for LLM training in one place

When to choose

• you want browser-based, no-code LLM fine-tuning on your own hardware
• you have a Linux machine with NVIDIA GPUs and can use docker-compose
• your users are non-technical and need a guided UI for training and evaluation

When to avoid

• you need managed cloud training infrastructure
• you only need inference/serving without fine-tuning
• you cannot use Docker or lack NVIDIA GPU resources
• you need a lightweight scriptable training pipeline rather than a full platform

Facets

application · maturity active

llm-training machine-learning web-framework self-hosted large-language-models machine-learning artificial-intelligence self-hosted self-hosted no-code fine-tuning llm peft transformers web-ui svelte docker-compose linux docker web-server gpu

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markdown · JSON · MCP: product_card(name="Bolin97/GongBU")