Instructions to use chinoll/rwkv7-g1d-0.1b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use chinoll/rwkv7-g1d-0.1b with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="chinoll/rwkv7-g1d-0.1b")
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("chinoll/rwkv7-g1d-0.1b", device_map="auto")

RWKV

How to use chinoll/rwkv7-g1d-0.1b 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 chinoll/rwkv7-g1d-0.1b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "chinoll/rwkv7-g1d-0.1b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "chinoll/rwkv7-g1d-0.1b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/chinoll/rwkv7-g1d-0.1b

SGLang

How to use chinoll/rwkv7-g1d-0.1b 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 "chinoll/rwkv7-g1d-0.1b" \
    --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": "chinoll/rwkv7-g1d-0.1b",
		"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 "chinoll/rwkv7-g1d-0.1b" \
        --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": "chinoll/rwkv7-g1d-0.1b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use chinoll/rwkv7-g1d-0.1b with Docker Model Runner:
```
docker model run hf.co/chinoll/rwkv7-g1d-0.1b
```

RWKV7 G1D 0.1B Transformers Conversion

This repository contains a Hugging Face Transformers-format conversion of BlinkDL/rwkv7-g1 checkpoint rwkv7-g1d-0.1b-20260129-ctx8192.pth.

Files include:

config.json with model_type: "rwkv7" and architectures: ["Rwkv7ForCausalLM"]
model.safetensors
rwkv_vocab_v20230424.txt
tokenizer_config.json

The conversion was checked against the RWKV-LM RNN reference in bf16 and fp16 for greedy generation, logits, top-k token ordering, 8k context, diverse batch inputs, odd lengths, and sampled generation.

Note: this model requires Transformers with RWKV7 support.

Downloads last month: 9

Safetensors

Model size

0.2B params

Tensor type

BF16