Titlewen is a small specialized title-generation model based on Qwen3-0.6B, fine-tuned to turn user messages into short, descriptive conversation titles.

This repository contains the GGUF version intended for lightweight local inference with runtimes such as llama.cpp.

What it does

Titlewen takes an arbitrary user message and produces a concise title describing its main topic.

Examples:

Input	Output
`Hey we are at the coffee shop, do you want anything?`	`Coffee Shop Chat`
`CUDA out of memory. Tried to allocate 20.00 MiB...`	`CUDA Memory Allocation Error`
`how is this possible? rtx 3060...`	`RTX 3060 Performance Analysis`
`Help me make the README a bit better`	`Improving README Clarity`

The model is designed for applications such as:

automatic chat/conversation naming
message and thread titles
support-ticket titles
compact topic extraction
short UI labels derived from user input

Model details

Base model: Qwen/Qwen3-0.6B
Task: Short title generation
Architecture: Decoder-only Transformer
Training method: LoRA fine-tuning
Training dataset: approximately 20,000 title-generation examples
Output style: typically 2–6 words
Format: GGUF
Primary languages: English

efficiencyx/Titlewen-GGUF

Titlewen-GGUF

What it does

Model details

efficiencyx/
Titlewen-GGUF