Instructions to use RemySkye/rwkv7-g1g-7.2b-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RemySkye/rwkv7-g1g-7.2b-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="RemySkye/rwkv7-g1g-7.2b-GGUF",
	filename="rwkv7-g1g-7.2b-20260523-ctx8192-BF16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use RemySkye/rwkv7-g1g-7.2b-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf RemySkye/rwkv7-g1g-7.2b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf RemySkye/rwkv7-g1g-7.2b-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf RemySkye/rwkv7-g1g-7.2b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf RemySkye/rwkv7-g1g-7.2b-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 RemySkye/rwkv7-g1g-7.2b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf RemySkye/rwkv7-g1g-7.2b-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 RemySkye/rwkv7-g1g-7.2b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf RemySkye/rwkv7-g1g-7.2b-GGUF:Q4_K_M

Use Docker

docker model run hf.co/RemySkye/rwkv7-g1g-7.2b-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use RemySkye/rwkv7-g1g-7.2b-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RemySkye/rwkv7-g1g-7.2b-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": "RemySkye/rwkv7-g1g-7.2b-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/RemySkye/rwkv7-g1g-7.2b-GGUF:Q4_K_M

Ollama
How to use RemySkye/rwkv7-g1g-7.2b-GGUF with Ollama:
```
ollama run hf.co/RemySkye/rwkv7-g1g-7.2b-GGUF:Q4_K_M
```

Unsloth Studio

How to use RemySkye/rwkv7-g1g-7.2b-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for RemySkye/rwkv7-g1g-7.2b-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for RemySkye/rwkv7-g1g-7.2b-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for RemySkye/rwkv7-g1g-7.2b-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use RemySkye/rwkv7-g1g-7.2b-GGUF with Docker Model Runner:
```
docker model run hf.co/RemySkye/rwkv7-g1g-7.2b-GGUF:Q4_K_M
```

Lemonade

How to use RemySkye/rwkv7-g1g-7.2b-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull RemySkye/rwkv7-g1g-7.2b-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.rwkv7-g1g-7.2b-GGUF-Q4_K_M

List all available models

lemonade list

RWKV7-G1G 7.2B - GGUF Quantizations

GGUF conversions of BlinkDL/rwkv7-g1g-7.2b (Apache 2.0)
for use with llama.cpp, LM Studio, and other GGUF-compatible runtimes.

All quants converted from BF16 base for maximum lossless quality.
Static quantizations — no importance matrix (imatrix) was used. Check out this for imatrix quantizations: RemySkye/rwkv7-g1g-7.2b-i1-GGUF

S/M/L K-quant variants had no effect on size for RWKV architecture — only _M variants are included.

See UncheatableEval for benchmarks.

Note: Q4_0, Q3_K_M, and Q2_K were tested but produced completely unintelligible / broken output without an importance matrix. For lower BPW quantizations (below ~4.5 BPW), an imatrix would be required — these quants currently use no imatrix at all. Stick to Q4_K_M or higher for usable results.

Downloads last month: 1,001

GGUF

Model size

7B params

Architecture

rwkv7

Hardware compatibility

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for RemySkye/rwkv7-g1g-7.2b-GGUF

Base model

BlinkDL/rwkv7-g1

Quantized

(16)

this model