Instructions to use mehmetkeremturkcan/DeepSeer-R1-Vision-Distill-Qwen-1.5B_google_vit-base-patch16-224 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mehmetkeremturkcan/DeepSeer-R1-Vision-Distill-Qwen-1.5B_google_vit-base-patch16-224 with Transformers:

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

pipe = pipeline("image-text-to-text", model="mehmetkeremturkcan/DeepSeer-R1-Vision-Distill-Qwen-1.5B_google_vit-base-patch16-224")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mehmetkeremturkcan/DeepSeer-R1-Vision-Distill-Qwen-1.5B_google_vit-base-patch16-224")
model = AutoModelForCausalLM.from_pretrained("mehmetkeremturkcan/DeepSeer-R1-Vision-Distill-Qwen-1.5B_google_vit-base-patch16-224", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mehmetkeremturkcan/DeepSeer-R1-Vision-Distill-Qwen-1.5B_google_vit-base-patch16-224 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mehmetkeremturkcan/DeepSeer-R1-Vision-Distill-Qwen-1.5B_google_vit-base-patch16-224"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mehmetkeremturkcan/DeepSeer-R1-Vision-Distill-Qwen-1.5B_google_vit-base-patch16-224",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/mehmetkeremturkcan/DeepSeer-R1-Vision-Distill-Qwen-1.5B_google_vit-base-patch16-224

SGLang

How to use mehmetkeremturkcan/DeepSeer-R1-Vision-Distill-Qwen-1.5B_google_vit-base-patch16-224 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 "mehmetkeremturkcan/DeepSeer-R1-Vision-Distill-Qwen-1.5B_google_vit-base-patch16-224" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mehmetkeremturkcan/DeepSeer-R1-Vision-Distill-Qwen-1.5B_google_vit-base-patch16-224",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "mehmetkeremturkcan/DeepSeer-R1-Vision-Distill-Qwen-1.5B_google_vit-base-patch16-224" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mehmetkeremturkcan/DeepSeer-R1-Vision-Distill-Qwen-1.5B_google_vit-base-patch16-224",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use mehmetkeremturkcan/DeepSeer-R1-Vision-Distill-Qwen-1.5B_google_vit-base-patch16-224 with Docker Model Runner:
```
docker model run hf.co/mehmetkeremturkcan/DeepSeer-R1-Vision-Distill-Qwen-1.5B_google_vit-base-patch16-224
```

DeepSeer: Vision Language Models with Reasoning

Vision language models with chain-of-thought reasoning are just starting to emerge. This is a proof-of-concept to train a vision model with thinking-enabled chat templates based on DeepSeek-R1 models.

Setup

pip install git+https://github.com/facebookresearch/schedule_free.git
pip install peft
git clone https://github.com/mkturkcan/seers.git
cd seers/seers/
git clone https://huggingface.co/mehmetkeremturkcan/DeepSeer-R1-Vision-Distill-Qwen-1.5B_google_vit-base-patch16-224

Test

Run

python predict.py

Training Details

This model is a fine-tuned version of deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B on the 5CD-AI/LLaVA-CoT-o1-Instruct dataset. It has been trained using seers.

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