CompiwerAI/Mtrini-SVL-1.0-GGUF

🧠 Mtrini-SVL-1.0 (GGUF)

Mtrini (متربّي) comes from Darija — Moroccan Arabic — and means "trained" or "well-fit." 💪 This is the easy-to-run version of that trained model: one file, no Python required. 🚀

🤔 What is this, in plain English?

This is a single downloadable file containing a complete AI model that can:

• —💻 Code — write functions, fix bugs, explain code
• —🧮 Do math — solve problems and show its reasoning
• —👀 See images — describe pictures, read screenshots, answer visual questions

It's the merged + compressed version of our fine-tuned model, so you can run it locally with a tool called llama.cpp — no GPU required if you have enough RAM (though a GPU makes it faster). ⚡

📖 What's in the name?

Mtrini (متربّي) in Darija means trained or well-fit. The entire story of this model is training: a strong foundation model, further trained on coding and math data, then merged and quantized so anyone can run it.

🗣️ Say it like: "em-tree-nee."

✨ Why you'll like this release

🚀 Quick start (2 steps)

Step 1 — Get llama.cpp

Download the llama.cpp tools from github.com/ggml-org/llama.cpp/releases and build them (or download a prebuilt binary if your OS has one).

Step 2 — Run the model

💬 Chat (text only):

llama-cli -m Mtrini-SVL-1.0-Q8_0.gguf -p "Write a Python function that checks if a number is prime."

🖼️ Chat with an image:

llama-mtmd-cli \
  -m Mtrini-SVL-1.0-Q8_0.gguf \
  -p "Describe this image." \
  --image path/to/your/image.jpg

🎉 That's it. The vision parts are already inside this one file — no extra --mmproj needed.

📦 File details

🧪 What is quantization? (quick version)

The original model is ~17.6 GB in full precision. Quantization compresses the numbers so the model still works great but takes half the space. Q8_0 is one of the higher-quality compression levels — a great balance of size and smarts. 🧠⚖️

⚠️ Keep in mind

• —This is a fine-tune trained on a focused coding/math mix.
• —It inherits the strengths and limitations of the base model.
• —🧪 Test on your own use case before production.

📜 License

Apache 2.0 — same as the base model. ✅