Instructions to use prithivMLmods/Enesidaon-VLR-7B-no-Thinking with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/Enesidaon-VLR-7B-no-Thinking with Transformers:

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

pipe = pipeline("image-text-to-text", model="prithivMLmods/Enesidaon-VLR-7B-no-Thinking")
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("prithivMLmods/Enesidaon-VLR-7B-no-Thinking")
model = AutoModelForMultimodalLM.from_pretrained("prithivMLmods/Enesidaon-VLR-7B-no-Thinking", device_map="auto")
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 prithivMLmods/Enesidaon-VLR-7B-no-Thinking with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/Enesidaon-VLR-7B-no-Thinking"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Enesidaon-VLR-7B-no-Thinking",
		"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/prithivMLmods/Enesidaon-VLR-7B-no-Thinking

SGLang

How to use prithivMLmods/Enesidaon-VLR-7B-no-Thinking 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 "prithivMLmods/Enesidaon-VLR-7B-no-Thinking" \
    --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": "prithivMLmods/Enesidaon-VLR-7B-no-Thinking",
		"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 "prithivMLmods/Enesidaon-VLR-7B-no-Thinking" \
        --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": "prithivMLmods/Enesidaon-VLR-7B-no-Thinking",
		"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 prithivMLmods/Enesidaon-VLR-7B-no-Thinking with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/Enesidaon-VLR-7B-no-Thinking
```

prithivMLmods commited on Sep 8, 2025

Commit

270ee59

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1 Parent(s): fea085d

Update README.md

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@@ -21,7 +21,7 @@ tags:
 - thinking=0
 ---
-![1.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/l-sGjRdxsqiHm6vEa2sy5.png)
 # **Enesidaon-VLR-7B-no-Thinking**
@@ -108,16 +108,6 @@ This model is intended for:
 ## References
 * **YaRN: Efficient Context Window Extension of Large Language Models**
-  [https://arxiv.org/pdf/2309.00071](https://arxiv.org/pdf/2309.00071)
 * **Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution**
-  [https://arxiv.org/pdf/2409.12191](https://arxiv.org/pdf/2409.12191)
 * **Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond**
-  [https://arxiv.org/pdf/2308.12966](https://arxiv.org/pdf/2308.12966)
-* **A Comprehensive and Challenging OCR Benchmark for Evaluating Large Multimodal Models in Literacy**
-  [https://arxiv.org/pdf/2412.02210](https://arxiv.org/pdf/2412.02210)
-* **Ground-R1: Incentivizing Grounded Visual Reasoning via Reinforcement Learning**
-  [https://arxiv.org/pdf/2505.20272](https://arxiv.org/pdf/2505.20272)

 - thinking=0
 ---
+![1.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/yiFI0lMGM7SdkmwS09BaZ.png)
 # **Enesidaon-VLR-7B-no-Thinking**
 ## References
 * **YaRN: Efficient Context Window Extension of Large Language Models**
 * **Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution**
 * **Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond**
+* **A Comprehensive and Challenging OCR Benchmark for Evaluating Large Multimodal Models in Literacy**