Instructions to use shoumenchougou/RWKV7-G1k-2.9B-20260930-FLA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shoumenchougou/RWKV7-G1k-2.9B-20260930-FLA with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="shoumenchougou/RWKV7-G1k-2.9B-20260930-FLA") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("shoumenchougou/RWKV7-G1k-2.9B-20260930-FLA", device_map="auto") - RWKV
How to use shoumenchougou/RWKV7-G1k-2.9B-20260930-FLA 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 shoumenchougou/RWKV7-G1k-2.9B-20260930-FLA with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "shoumenchougou/RWKV7-G1k-2.9B-20260930-FLA" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "shoumenchougou/RWKV7-G1k-2.9B-20260930-FLA", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/shoumenchougou/RWKV7-G1k-2.9B-20260930-FLA
- SGLang
How to use shoumenchougou/RWKV7-G1k-2.9B-20260930-FLA 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 "shoumenchougou/RWKV7-G1k-2.9B-20260930-FLA" \ --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": "shoumenchougou/RWKV7-G1k-2.9B-20260930-FLA", "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 "shoumenchougou/RWKV7-G1k-2.9B-20260930-FLA" \ --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": "shoumenchougou/RWKV7-G1k-2.9B-20260930-FLA", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use shoumenchougou/RWKV7-G1k-2.9B-20260930-FLA with Docker Model Runner:
docker model run hf.co/shoumenchougou/RWKV7-G1k-2.9B-20260930-FLA
Download NOTICE from shoumenchougou/RWKV7-G1k-2.9B-20260930-FLA: direct link, hf CLI and curl.
- Browser
- Download file 528 Bytes
-
https://huggingface.co/shoumenchougou/RWKV7-G1k-2.9B-20260930-FLA/resolve/main/NOTICE
- Command line
-
hf download hf://shoumenchougou/RWKV7-G1k-2.9B-20260930-FLA/NOTICE
-
curl -L -o NOTICE https://huggingface.co/shoumenchougou/RWKV7-G1k-2.9B-20260930-FLA/resolve/main/NOTICE
528 Bytes
| RWKV-7 G1k model release | |
| Source checkpoint: BlinkDL/rwkv7-g1/rwkv7-g1k-2.9b-20260930-ctx25600.pth | |
| Source revision: a1ddf9e96df23d5d7db65137ad83a4701044cd05 | |
| Source SHA-256: d8f8ecd4af8d706f867ac8a74284f35c0e3b9f863e7b09bb4d4031480c8b28b2 | |
| FLA implementation revision: 8e84ed4a6727be082c34a3855c60623fd11411e9 | |
| The RWKV logo in assets/rwkv-logo.webp is the audited Hugging Face RWKV organization avatar snapshot. RWKV names and logos may be subject to separate trademark rules; Apache-2.0 does not grant trademark rights. | |