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 / README.md

CompiwerAI

Add language tags (ary, ar, en) and links to the GGUF versions

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metadata

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. 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:

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.
A short pass on new Codeforces problems and more identity answers.
A larger pass (182 steps × 16 examples, 4,023 rows): OpenThoughts3 math, code and science reasoning, more Darija, identity answers and replay.
Round 4 (123 steps × 16 examples): a new slice of OpenThoughts3 math, code and science reasoning, more Darija and identity answers.
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 (thank you!), including the vision projector (mmproj) for images:

mradermacher/Mtrini-SVL-2.0-GGUF
mradermacher/Mtrini-SVL-2.0-i1-GGUF (imatrix quants)

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.