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
```

CompiwerAI commited on 1 day ago

Commit

fa4feba

verified ·

1 Parent(s): fe356f5

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

Browse files

Files changed (1) hide show

README.md +17 -0

README.md CHANGED Viewed

@@ -3,6 +3,10 @@ base_model: Qwen/Qwen3.5-9B
 library_name: transformers
 pipeline_tag: image-text-to-text
 license: apache-2.0
 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]
 ---
@@ -40,6 +44,19 @@ Three rounds of LoRA SFT, each merged before the next:
 Use it like Qwen3.5-9B. Thinking is on by default; pass `enable_thinking=False` to the chat template for direct answers.
 ## Status
 Experimental. Check the base model's card for limitations, and verify important answers.

 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]
 ---
 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.