Instructions to use RWKV/RWKV7-G1j-13.3B-20260831 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RWKV/RWKV7-G1j-13.3B-20260831 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="RWKV/RWKV7-G1j-13.3B-20260831", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("RWKV/RWKV7-G1j-13.3B-20260831", trust_remote_code=True, device_map="auto") - RWKV
How to use RWKV/RWKV7-G1j-13.3B-20260831 with RWKV:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use RWKV/RWKV7-G1j-13.3B-20260831 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "RWKV/RWKV7-G1j-13.3B-20260831" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "RWKV/RWKV7-G1j-13.3B-20260831", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/RWKV/RWKV7-G1j-13.3B-20260831
- SGLang
How to use RWKV/RWKV7-G1j-13.3B-20260831 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 "RWKV/RWKV7-G1j-13.3B-20260831" \ --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": "RWKV/RWKV7-G1j-13.3B-20260831", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "RWKV/RWKV7-G1j-13.3B-20260831" \ --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": "RWKV/RWKV7-G1j-13.3B-20260831", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use RWKV/RWKV7-G1j-13.3B-20260831 with Docker Model Runner:
docker model run hf.co/RWKV/RWKV7-G1j-13.3B-20260831
- Xet hash:
- 534bf340398658ddb01237cb47863047927465c417545fe0c79301e37f5671b1
- Size of remote file:
- 984 MB
- SHA256:
- 1cffa08fe9d08e2f3fdec939862e6a9b7a8c071e71175ed1978b17bc4daca0b2
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