Instructions to use CompiwerAI/Mtrini-SVL-2.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CompiwerAI/Mtrini-SVL-2.0 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="CompiwerAI/Mtrini-SVL-2.0")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("CompiwerAI/Mtrini-SVL-2.0")
model = AutoModelForMultimodalLM.from_pretrained("CompiwerAI/Mtrini-SVL-2.0", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use CompiwerAI/Mtrini-SVL-2.0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CompiwerAI/Mtrini-SVL-2.0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CompiwerAI/Mtrini-SVL-2.0",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/CompiwerAI/Mtrini-SVL-2.0

SGLang

How to use CompiwerAI/Mtrini-SVL-2.0 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "CompiwerAI/Mtrini-SVL-2.0" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CompiwerAI/Mtrini-SVL-2.0",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "CompiwerAI/Mtrini-SVL-2.0" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CompiwerAI/Mtrini-SVL-2.0",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use CompiwerAI/Mtrini-SVL-2.0 with Docker Model Runner:
```
docker model run hf.co/CompiwerAI/Mtrini-SVL-2.0
```

Mtrini-SVL-2.0

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---
base_model: Qwen/Qwen3.5-9B
library_name: transformers
pipeline_tag: image-text-to-text
license: apache-2.0
language:
- ary
- ar
- en
tags: [compiwer-ai, mtrini, svl, reasoning, moroccan-darija, qwen3_5]
datasets: [simplescaling/s1K-1.1, GAIR/LIMO, open-r1/codeforces-cots, open-thoughts/OpenThoughts3-1.2M, MBZUAI-Paris/Darija-SFT-Mixture]
---

# Mtrini-SVL-2.0

Mtrini-SVL-2.0 is an AI assistant built by **Compiwer AI**, fine-tuned from [Qwen3.5-9B](https://huggingface.co/Qwen/Qwen3.5-9B).
It thinks before answering, understands images, supports a 262,144-token context (extensible to about 1M), and speaks Moroccan Darija.

## Results

Quick checks run on the base model and on this model with the same questions and settings. These are small samples, not official benchmark runs.

| | Qwen3.5-9B | Before | After |
|---|---|---|---|
| AIME 2025 (30 problems, avg of 8 tries) | – | 62.1% | 61.7% |
| LiveCodeBench v6 (2025 problems, pass@1) | – | 47.5% | 48.8% |
| GPQA Diamond (pass@1) | – | 60.6% | 62.1% |
| DarijaMMLU (400 questions) | 34.5% | 64.0% | 64.2% |
| Identity (says it's Mtrini by Compiwer AI) | 0.0% | 93.8% | 93.8% |

## Training

Three rounds of LoRA SFT, each merged before the next:

1. Math and science reasoning (s1K-1.1, LIMO), Python reasoning (open-r1/codeforces-cots), Moroccan Darija (MBZUAI-Paris/Darija-SFT-Mixture), identity answers, and replay of the base model's own verified-correct reasoning.
2. A short pass on new Codeforces problems and more identity answers.
3. A larger pass (182 steps × 16 examples, 4,023 rows): OpenThoughts3 math, code and science reasoning, more Darija, identity answers and replay.

4. Round 4 (123 steps × 16 examples): a new slice of OpenThoughts3 math, code and science reasoning, more Darija and identity answers.

5. Round 7, Gated Self-Training with preference pairs (5 DPO steps): on hard math, coding problems checked by their tests, and hard science, its own right answers preferred over its own wrong ones of similar length, plus identity pairs.

## Usage

Use it like Qwen3.5-9B. Thinking is on by default; pass `enable_thinking=False` to the chat template for direct answers.

## Run it on your computer

GGUF versions for llama.cpp, LM Studio and Ollama, made by [mradermacher](https://huggingface.co/mradermacher) (thank you!), including the vision projector (`mmproj`) for images:

- [mradermacher/Mtrini-SVL-2.0-GGUF](https://huggingface.co/mradermacher/Mtrini-SVL-2.0-GGUF)
- [mradermacher/Mtrini-SVL-2.0-i1-GGUF](https://huggingface.co/mradermacher/Mtrini-SVL-2.0-i1-GGUF) (imatrix quants)

```bash
ollama run hf.co/mradermacher/Mtrini-SVL-2.0-GGUF:Q4_K_M
```

In LM Studio, search for **Mtrini-SVL-2.0**. Q4_K_M (5.6 GB) is a good default.

## Status

Experimental. Check the base model's card for limitations, and verify important answers.