🧠 Mtrini-SVL-1.0 (Adapter)

Mtrini (متربّي) comes from Darija — Moroccan Arabic — and means "trained" or "well-fit." 💪 That's exactly what this model is: a smart base model that we trained a little further to be great at coding and math — while still being able to see images. 👀

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🤔 What is this, in plain English?

There are two pieces here:

1. A big, powerful base model 🏗️ — Qwen3-VL-8B-Instruct. It already knows a lot and can understand both text and images.

2. A small "adapter" 🧩 — this is the Mtrini part. Instead of retraining the whole giant model (expensive! 💸), we trained a lightweight add-on that nudges the base model to be better at coding and math.

This repo is the adapter 🧩 — the little piece that plugs into the big model.

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📖 What's in the name?

Mtrini (متربّي) in Darija means trained or well-fit — the root m-r-b carries the sense of being raised, cultivated, and prepared. 🌱

That's literally what happened here: a capable foundation model, further trained on focused data to become well-fit for reasoning tasks.

🗣️ Say it like: "em-tree-nee."

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✨ What can it do?

Skill	Description
💻 Coding	Write functions, fix bugs, explain how code works
🧮 Math	Solve problems step by step and show its reasoning
👀 Vision	Describe images, read screenshots, and answer questions about pictures

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📚 What was it trained on?

Two public datasets, chosen to make it sharp at reasoning:

📦 Dataset	🎯 Focus
Bespoke-Stratos-17k	Coding & problem-solving
OpenR1-Math-220k	Mathematical reasoning

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🔧 Technical details (for the curious)

Setting	Value
🏗️ Base model	Qwen/Qwen3-VL-8B-Instruct (8.81B params)
🧩 Adapter type	LoRA (rank 16)
🎯 Target modules	Attention + MLP projections
📏 Context length	2048
🚀 Learning rate	2e-4
🔢 Max steps	1000

LoRA stands for Low-Rank Adaptation — a clever way to fine-tune a big model by training only a tiny fraction of its weights. That's why the adapter is small and fast to share. 🪶

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🚀 How to use it

import torch
from transformers import Qwen3VLForConditionalGeneration, AutoProcessor
from peft import PeftModel

# 1️⃣ Load the big base model
base = Qwen3VLForConditionalGeneration.from_pretrained(
    "Qwen/Qwen3-VL-8B-Instruct",
    dtype=torch.bfloat16,
    device_map="auto",
    attn_implementation="sdpa",
)

# 2️⃣ Plug in the Mtrini adapter
model = PeftModel.from_pretrained(base, "CompiwerAI/Mtrini-SVL-1.0")

# 3️⃣ Load the processor (handles text + images)
processor = AutoProcessor.from_pretrained("CompiwerAI/Mtrini-SVL-1.0")

model.eval()

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📦 Want to skip the setup? Use the GGUF!

If all that Python looks intimidating, we've got you covered. 🙂 We merged the adapter into the base model and quantized it into a single file you can run locally with no Python at all:

👉 CompiwerAI/Mtrini-SVL-1.0-GGUF

• 🗜️ One file, ~8.7 GB
• 🖥️ Runs with llama.cpp
• 🖼️ Vision included — no extra files needed

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⚠️ Things to keep in mind

• It's trained for a relatively short schedule (1000 steps) on a focused mix.
• It inherits both the strengths and the limitations of the base model.
• 🧪 Always test on your use case before relying on it in production.

📜 License

Apache 2.0 — same as the base model. ✅