Instructions to use shoumenchougou/RWKV7-G1j-2.9B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- RWKV
How to use shoumenchougou/RWKV7-G1j-2.9B-GGUF 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
- llama.cpp
How to use shoumenchougou/RWKV7-G1j-2.9B-GGUF with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf shoumenchougou/RWKV7-G1j-2.9B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf shoumenchougou/RWKV7-G1j-2.9B-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf shoumenchougou/RWKV7-G1j-2.9B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf shoumenchougou/RWKV7-G1j-2.9B-GGUF:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf shoumenchougou/RWKV7-G1j-2.9B-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf shoumenchougou/RWKV7-G1j-2.9B-GGUF:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf shoumenchougou/RWKV7-G1j-2.9B-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf shoumenchougou/RWKV7-G1j-2.9B-GGUF:Q4_K_M
Use Docker
docker model run hf.co/shoumenchougou/RWKV7-G1j-2.9B-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use shoumenchougou/RWKV7-G1j-2.9B-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "shoumenchougou/RWKV7-G1j-2.9B-GGUF" # 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-G1j-2.9B-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/shoumenchougou/RWKV7-G1j-2.9B-GGUF:Q4_K_M
- Ollama
How to use shoumenchougou/RWKV7-G1j-2.9B-GGUF with Ollama:
ollama run hf.co/shoumenchougou/RWKV7-G1j-2.9B-GGUF:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use shoumenchougou/RWKV7-G1j-2.9B-GGUF with Docker Model Runner:
docker model run hf.co/shoumenchougou/RWKV7-G1j-2.9B-GGUF:Q4_K_M
- Lemonade
How to use shoumenchougou/RWKV7-G1j-2.9B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull shoumenchougou/RWKV7-G1j-2.9B-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.RWKV7-G1j-2.9B-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Download NOTICE from shoumenchougou/RWKV7-G1j-2.9B-GGUF: direct link, hf CLI and curl.
- Browser
- Download file 430 Bytes
-
https://huggingface.co/shoumenchougou/RWKV7-G1j-2.9B-GGUF/resolve/main/NOTICE
- Command line
-
hf download hf://shoumenchougou/RWKV7-G1j-2.9B-GGUF/NOTICE
-
curl -L -o NOTICE https://huggingface.co/shoumenchougou/RWKV7-G1j-2.9B-GGUF/resolve/main/NOTICE
430 Bytes
| RWKV-7 G1j GGUF model release | |
| Source checkpoint: BlinkDL/rwkv7-g1, rwkv7-g1j-2.9b-20260831-ctx16384.pth. | |
| Model weights are distributed under the Apache License 2.0; see LICENSE. | |
| The RWKV logo in assets/rwkv-logo.webp is a repository-local snapshot of the | |
| RWKV Hugging Face organization avatar. RWKV names and logos may be subject to | |
| separate trademark or brand-use rules; the Apache License 2.0 does not grant | |
| trademark rights. | |