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
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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

Downloads last month: 5

Safetensors

Model size

29B params

Tensor type

F16

Model tree for tripplet-research/majuli3.1

Base model

OddTheGreat/Mars_27B_V.1

Finetuned

(2)

this model

Quantizations

2 models