Instructions to use surogate/Surogate-3.5-2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use surogate/Surogate-3.5-2B with Transformers:

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

pipe = pipeline("image-text-to-text", model="surogate/Surogate-3.5-2B")
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)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("surogate/Surogate-3.5-2B")
model = AutoModelForMultimodalLM.from_pretrained("surogate/Surogate-3.5-2B")
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=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use surogate/Surogate-3.5-2B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "surogate/Surogate-3.5-2B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "surogate/Surogate-3.5-2B",
		"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/surogate/Surogate-3.5-2B

SGLang

How to use surogate/Surogate-3.5-2B 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 "surogate/Surogate-3.5-2B" \
    --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": "surogate/Surogate-3.5-2B",
		"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 "surogate/Surogate-3.5-2B" \
        --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": "surogate/Surogate-3.5-2B",
		"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 surogate/Surogate-3.5-2B with Docker Model Runner:
```
docker model run hf.co/surogate/Surogate-3.5-2B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Surogate-3.5-2B

Surogate-3.5-2B is a Romanian-first bilingual assistant developed by Invergent to be used on the Surogate platform. It is optimized for Romanian instruction following, reasoning, math, and orthographic correctness. English remains supported.

Thinking support: explicit <think>...</think> reasoning traces in both Romanian and English, alongside direct-answer mode.

The model is compatible with the Qwen3.5 chat template and tooling.

Evaluation

Benchmark	Surogate-3.5-2B
Romanian text quality
Invented word forms / 1k ↓	1.016
English leakage / 1k ↓	0.731
Missing diacritics / 1k ↓	0.587
Knowledge & STEM
MMLU-Pro	~63.8
MMLU-Redux	~76.4
GPQA	~49.5
SuperGPQA	~36.0
Instruction following
RO IFEval prompt / instruction strict	30.25 / 57.22
EN IFEval prompt / instruction strict	61.92 / 71.94
IFEval	~74.7
IFBench	~39.2
MultiChallenge	~32.0
Math & reasoning
RO GSM8K direct / thinking strict	49.77 / 47.88
EN GSM8K strict	68.92
HMMT Feb 25 / Nov 25	~22.9 / ~19.6
PolyMATH	~26.1
Agents & long context
BFCL-V4	~41.9
TAU2-Bench	~46.8
LongBench v2	~37.2
Multilingual & translation
Translation EN to RO / RO to EN (chrF2)	47.42 / 56.17
WMT24++	~44.0
MMMLU	~60.6
MMLU-ProX	~50.2
INCLUDE	~53.2
Global PIQA	~66.5

Plain values are direct measurements. Values marked ~ are estimates derived from the source foundation model's published results and measured capability retention.

Bilingual Thinking Example

With enable_thinking=True, the reasoning trace follows the prompt language.

Romanian prompt: Un tren parcurge 180 km în 3 ore. Care este viteza sa medie?

<think>
Viteza medie este distanța împărțită la timp: 180 km / 3 h = 60 km/h.
</think>
Viteza medie este 60 km/h.

English prompt: A train travels 180 km in 3 hours. What is its average speed?

<think>
Average speed is distance divided by time: 180 km / 3 h = 60 km/h.
</think>
The average speed is 60 km/h.

Usage

from transformers import AutoModelForImageTextToText, AutoProcessor

model_id = "surogate/Surogate-3.5-2B"
processor = AutoProcessor.from_pretrained(model_id)
model = AutoModelForImageTextToText.from_pretrained(
    model_id,
    device_map="auto",
    torch_dtype="auto",
)

messages = [
    {"role": "user", "content": "Explică pe scurt de ce cerul este albastru."}
]
inputs = processor.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    enable_thinking=False,
    return_tensors="pt",
    return_dict=True,
).to(model.device)

output = model.generate(**inputs, max_new_tokens=512)
answer = processor.decode(
    output[0][inputs.input_ids.shape[1]:],
    skip_special_tokens=False,
)
print(answer)

Set enable_thinking=True to request an explicit reasoning trace. The shipped generation defaults are temperature 0.6, top-p 0.95, and top-k 20.

Training Framework

The model was trained using our high-performance Surogate Trainer

Limitations

Romanian is the primary optimization target.
English performance may vary by task.
A 2B model has limited factual depth and reliability on complex reasoning.
The model can produce spelling, reasoning, and factual errors.
Generated facts, calculations, and high-impact advice require independent verification.

Use is subject to the repository's license terms.

Contact Us

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