Instructions to use RWKV/RWKV7-G1j-13.3B-20260831 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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
```

RWKV7-G1j-13.3B-20260831 / model-00023-of-00028.safetensors

shoumenchougou

Publish RWKV7 G1j 13.3B native Transformers release

ad88793 verified 17 days ago

Download

History Blame Contribute Delete

969 MB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: f80d13f243268c2731103fd0049e4b2997751738d6526c1301ca5442a378398b
Size of remote file:: 969 MB
SHA256:: 59fe21aeb3ce67f2ef8be7a7e5b5f175b4d7f595fabf44aeba6a98c3724e06ac

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.