Instructions to use Skywork/Skywork-R1V3-38B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Skywork/Skywork-R1V3-38B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="Skywork/Skywork-R1V3-38B")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Skywork/Skywork-R1V3-38B", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Skywork/Skywork-R1V3-38B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Skywork/Skywork-R1V3-38B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Skywork/Skywork-R1V3-38B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Skywork/Skywork-R1V3-38B
- SGLang
How to use Skywork/Skywork-R1V3-38B 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 "Skywork/Skywork-R1V3-38B" \ --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": "Skywork/Skywork-R1V3-38B", "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 "Skywork/Skywork-R1V3-38B" \ --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": "Skywork/Skywork-R1V3-38B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Skywork/Skywork-R1V3-38B with Docker Model Runner:
docker model run hf.co/Skywork/Skywork-R1V3-38B
Update README.md
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README.md
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@@ -60,7 +60,7 @@ Skywork-R1V3 is an advanced, open-source Vision-Language Model (VLM) built on se
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- **Entropy of Critical Reasoning Tokens**: This unique indicator effectively gauges reasoning capability, guiding checkpoint selection during RL training.
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These innovations lead to Broad Reasoning Generalization, allowing our RL-powered approach to successfully extend mathematical reasoning to diverse subject areas. Additionally, our work delves into RL-specific explorations like curriculum learning and learning rate strategies, alongside a broader discussion on multimodal reasoning. For more details, refer to our [[📖 R1V3 Report](https://github.com/SkyworkAI/Skywork-R1V/blob/main/
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## 3. Evaluation
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### 🌟 Key Results
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- **Entropy of Critical Reasoning Tokens**: This unique indicator effectively gauges reasoning capability, guiding checkpoint selection during RL training.
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These innovations lead to Broad Reasoning Generalization, allowing our RL-powered approach to successfully extend mathematical reasoning to diverse subject areas. Additionally, our work delves into RL-specific explorations like curriculum learning and learning rate strategies, alongside a broader discussion on multimodal reasoning. For more details, refer to our [[📖 R1V3 Report](https://github.com/SkyworkAI/Skywork-R1V/blob/main/Skywork_R1V3.pdf)] .
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## 3. Evaluation
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### 🌟 Key Results
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