Instructions to use tripplet-research/majuli3.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tripplet-research/majuli3.1 with Transformers:

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

pipe = pipeline("image-text-to-text", model="tripplet-research/majuli3.1")
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, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("tripplet-research/majuli3.1")
model = AutoModelForImageTextToText.from_pretrained("tripplet-research/majuli3.1")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use tripplet-research/majuli3.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tripplet-research/majuli3.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tripplet-research/majuli3.1",
		"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/tripplet-research/majuli3.1

SGLang

How to use tripplet-research/majuli3.1 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 "tripplet-research/majuli3.1" \
    --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": "tripplet-research/majuli3.1",
		"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 "tripplet-research/majuli3.1" \
        --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": "tripplet-research/majuli3.1",
		"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 tripplet-research/majuli3.1 with Docker Model Runner:
```
docker model run hf.co/tripplet-research/majuli3.1
```

majuli3.1 / README.md

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metadata

license: apache-2.0
language:
  - en
  - ru
tags:
  - transformers
  - safetensors
  - gemma3
  - image-text-to-text
  - creative
  - roleplay
  - conversational
  - text-generation-inference
  - endpoints_compatible
base_model:
  - OddTheGreat/Mars_27B_V.1
model_name: Majuli 3.1
pipeline_tag: image-text-to-text

Majuli 3.1

By Tripplet AI (Tripplet Artificial General Intelligence Research Institute)

Majuli 3.1 is a powerful 27B parameter multimodal language model built on the Gemma 3 architecture, optimized for creative writing, roleplay, and general-purpose instruction following.

Model Details

Parameters: 28.8B
Architecture: Gemma 3 (Gemma3ForConditionalGeneration)
Context Length: 131,072 tokens
Hidden Size: 5376
Layers: 62
Attention Heads: 32 (16 KV heads)
Vision Encoder: SigLIP (896px, 27 layers)
Languages: English, Russian
Precision: bfloat16

Key Features

Long context support up to 128K tokens
Multimodal capabilities (image + text)
Hybrid attention with sliding window (1024) and full attention layers
Optimized for creative and roleplay tasks

Usage

from transformers import AutoProcessor, AutoModelForImageTextToText

model = AutoModelForImageTextToText.from_pretrained("tripplet-research/majuli-3.1")
processor = AutoProcessor.from_pretrained("tripplet-research/majuli-3.1")

messages = [
    {"role": "user", "content": "Hello, tell me about yourself."}
]

inputs = processor.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True)
output = model.generate(**inputs, max_new_tokens=512)
print(processor.decode(output[0], skip_special_tokens=True))

About Tripplet AI

Tripplet Artificial General Intelligence Research Institute is dedicated to advancing the frontiers of artificial general intelligence through open research and model development.

License

Apache 2.0