Instructions to use Arijit-09/RoadXpert_AI-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Arijit-09/RoadXpert_AI-v1 with Transformers:

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

pipe = pipeline("image-text-to-text", model="Arijit-09/RoadXpert_AI-v1")
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("Arijit-09/RoadXpert_AI-v1")
model = AutoModelForMultimodalLM.from_pretrained("Arijit-09/RoadXpert_AI-v1")
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 Arijit-09/RoadXpert_AI-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Arijit-09/RoadXpert_AI-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Arijit-09/RoadXpert_AI-v1",
		"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/Arijit-09/RoadXpert_AI-v1

SGLang

How to use Arijit-09/RoadXpert_AI-v1 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 "Arijit-09/RoadXpert_AI-v1" \
    --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": "Arijit-09/RoadXpert_AI-v1",
		"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 "Arijit-09/RoadXpert_AI-v1" \
        --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": "Arijit-09/RoadXpert_AI-v1",
		"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 Arijit-09/RoadXpert_AI-v1 with Docker Model Runner:
```
docker model run hf.co/Arijit-09/RoadXpert_AI-v1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

RoadXpert-AI-V1-109B MoE

Blog Post

The Cogito v2 LLMs are instruction tuned generative models. All models are released under an open license for commercial use.

Cogito v2 models are hybrid reasoning models. Each model can answer directly (standard LLM), or self-reflect before answering (like reasoning models).
The LLMs are trained using Iterated Distillation and Amplification (IDA) - an scalable and efficient alignment strategy for superintelligence using iterative self-improvement.
The models have been optimized for coding, STEM, instruction following and general helpfulness, and have significantly higher multilingual, coding and tool calling capabilities than size equivalent counterparts.
- In both standard and reasoning modes, Cogito v2-preview models outperform their size equivalent counterparts on common industry benchmarks.
This model is trained in over 30 languages and supports long contexts (upto 10M tokens).

Evaluations

Here is the model performance on some standard industry benchmarks:

Logo

For detailed evaluations, please refer to the Blog Post.

Usage

Here is a snippet below for usage with Transformers:

License

This repository and the model weights are licensed under the Llama 4 Community License Agreement (Llama models' default license agreement). All fine-tuning, dataset curation, and optimization have been performed by Arijit Rakshit (2025) and others.

Contact

If you would like to reach out to our team, send an email to contact@deepcogito.com.

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Safetensors

Model size

109B params

Tensor type

BF16

Model tree for Arijit-09/RoadXpert_AI-v1

Base model

meta-llama/Llama-4-Scout-17B-16E

Finetuned

(20)

this model

Quantizations

2 models