Instructions to use vicharai/Dhruv-27B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use vicharai/Dhruv-27B with Transformers:

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

pipe = pipeline("image-text-to-text", model="vicharai/Dhruv-27B")
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("vicharai/Dhruv-27B")
model = AutoModelForImageTextToText.from_pretrained("vicharai/Dhruv-27B")
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

vLLM

How to use vicharai/Dhruv-27B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "vicharai/Dhruv-27B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vicharai/Dhruv-27B",
		"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/vicharai/Dhruv-27B

SGLang

How to use vicharai/Dhruv-27B 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 "vicharai/Dhruv-27B" \
    --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": "vicharai/Dhruv-27B",
		"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 "vicharai/Dhruv-27B" \
        --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": "vicharai/Dhruv-27B",
		"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 vicharai/Dhruv-27B with Docker Model Runner:
```
docker model run hf.co/vicharai/Dhruv-27B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Model Card for Dhruv-27b-preview

Model Overview

Dhruv-27B-preview is a powerful 27-billion parameter language model based on Gemma3, fine-tuned for advanced reasoning tasks. Dhruv-27B is part of the Gemini Reasoning Series, optimized for high performance on academic, logical, and factual evaluations. It is capable of deep contextual reasoning and chain-of-thought generation, making it suitable for research, enterprise applications, and AI agents requiring robust general knowledge understanding.

Key Metrics

Dhruv-27b-preview achieves strong performance on key benchmarks:

+------------------------+--------+
|        Metric         | Score  |
+------------------------+--------+
| MMLU (5-shot)          | 80.0%  |
| GPQA (0-shot)          | 52.0%  |
+------------------------+--------+

Model Architecture

Base Model: Gemma3
Model Size: 27B parameters
Type: Decoder-only Transformer (causal LM)
Precision: bf16 with int8 quantization for inference efficiency
Training Objective: Instruction-tuned with emphasis on reasoning, question answering, and factual correctness

Intended Use

Research and academic QA tasks
General-purpose reasoning agents
Multilingual factual reasoning
Enterprise AI tools requiring high factual accuracy and depth

USAGE

Use a System Template

Limitations

Not optimized for creative generation or dialog tasks.
May hallucinate in areas with limited training data.
Requires fine-grained prompt engineering for complex instructions.

Citation

@misc{vicharai_dhruv27b,
  title={Dhruv-27B: Gemma3 Reasoning Model},
  author={vicharai},
  year={2025},
  publisher={vicharai},
  url={https://vichar.io}
}

Developed by vichar ai Huggingface Website

Downloads last month: 6

Safetensors

Model size

27B params

Tensor type

BF16

Model tree for vicharai/Dhruv-27B

Base model

google/gemma-3-27b-pt

Finetuned

(76)

this model

Quantizations

2 models