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 / eval_results.json

CompiwerAI

Mtrini-SVL-2.0 round 7, Gated Self-Training with DPO (5 steps)

fe356f5 verified 1 day ago

Raw History Blame Contribute Delete

1.31 kB

{
  "base": {
    "math500": 0.66,
    "darija_mmlu": 0.345,
    "identity": 0.0,
    "humaneval": 0.9390243902439024
  },
  "previous": {
    "livecodebench": 0.475,
    "livecodebench/easy": 0.9523809523809523,
    "livecodebench/hard": 0.16666666666666666,
    "livecodebench/medium": 0.5217391304347826,
    "gpqa_diamond": 0.6060606060606061,
    "gpqa/biology": 0.42105263157894735,
    "gpqa/chemistry": 0.41935483870967744,
    "gpqa/physics": 0.8488372093023255,
    "aime2025": 0.6208333333333333,
    "aime/algebra": 0.7222222222222222,
    "aime/combinatorics": 0.5416666666666666,
    "aime/geometry": 0.6607142857142857,
    "aime/number theory": 0.525,
    "darija_mmlu": 0.64,
    "identity": 0.9375
  },
  "svl20": {
    "livecodebench": 0.4875,
    "livecodebench/easy": 0.9523809523809523,
    "livecodebench/hard": 0.1388888888888889,
    "livecodebench/medium": 0.6086956521739131,
    "gpqa_diamond": 0.6212121212121212,
    "gpqa/biology": 0.5263157894736842,
    "gpqa/chemistry": 0.3870967741935484,
    "gpqa/physics": 0.8953488372093024,
    "aime2025": 0.6166666666666667,
    "aime/algebra": 0.5972222222222222,
    "aime/combinatorics": 0.5277777777777778,
    "aime/geometry": 0.8392857142857143,
    "aime/number theory": 0.5,
    "darija_mmlu": 0.6425,
    "identity": 0.9375
  }
}

	{
	"base": {
	"math500": 0.66,
	"darija_mmlu": 0.345,
	"identity": 0.0,
	"humaneval": 0.9390243902439024
	},
	"previous": {
	"livecodebench": 0.475,
	"livecodebench/easy": 0.9523809523809523,
	"livecodebench/hard": 0.16666666666666666,
	"livecodebench/medium": 0.5217391304347826,
	"gpqa_diamond": 0.6060606060606061,
	"gpqa/biology": 0.42105263157894735,
	"gpqa/chemistry": 0.41935483870967744,
	"gpqa/physics": 0.8488372093023255,
	"aime2025": 0.6208333333333333,
	"aime/algebra": 0.7222222222222222,
	"aime/combinatorics": 0.5416666666666666,
	"aime/geometry": 0.6607142857142857,
	"aime/number theory": 0.525,
	"darija_mmlu": 0.64,
	"identity": 0.9375
	},
	"svl20": {
	"livecodebench": 0.4875,
	"livecodebench/easy": 0.9523809523809523,
	"livecodebench/hard": 0.1388888888888889,
	"livecodebench/medium": 0.6086956521739131,
	"gpqa_diamond": 0.6212121212121212,
	"gpqa/biology": 0.5263157894736842,
	"gpqa/chemistry": 0.3870967741935484,
	"gpqa/physics": 0.8953488372093024,
	"aime2025": 0.6166666666666667,
	"aime/algebra": 0.5972222222222222,
	"aime/combinatorics": 0.5277777777777778,
	"aime/geometry": 0.8392857142857143,
	"aime/number theory": 0.5,
	"darija_mmlu": 0.6425,
	"identity": 0.9375
	}
	}