Instructions to use ggufs/mantis-m1-flash-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use ggufs/mantis-m1-flash-lora with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
Configuration Parsing Warning:In adapter_config.json: "peft.task_type" must be a string
Mantis M1 Flash
A concise vision-language model built by ggufs on top of Qwen2.5-VL-7B-Instruct.
What it does
- Reads up to 6 images and answers about them
- Direct, short answers — no rambling, no filler
- Identity: Mantis, built by ggufs
- Trained on 430 knowledge pairs across physics, chemistry, biology, math, CS, SVG, Three.js, and web dev
Performance
- Base median response: 1,332 chars
- Mantis median response: 154 chars (8.6x shorter)
- All answers factually correct
Thinking mode
[flash]prefix: gives direct answers with internal reasoning[think]prefix: shows full reasoning steps before the answer
Usage
from transformers import AutoModelForImageTextToText, AutoProcessor
from peft import PeftModel
model = AutoModelForImageTextToText.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct", dtype="bfloat16", device_map="auto")
model = PeftModel.from_pretrained(model, "ggufs/mantis-m1-flash-lora")
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Base model
Qwen/Qwen2.5-VL-7B-Instruct