Instructions to use RWKV-Red-Team/ARWKV-7B-Preview-0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RWKV-Red-Team/ARWKV-7B-Preview-0.1 with Transformers:

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

pipe = pipeline("text-generation", model="RWKV-Red-Team/ARWKV-7B-Preview-0.1", 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-Red-Team/ARWKV-7B-Preview-0.1", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use RWKV-Red-Team/ARWKV-7B-Preview-0.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RWKV-Red-Team/ARWKV-7B-Preview-0.1"
# 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-Red-Team/ARWKV-7B-Preview-0.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/RWKV-Red-Team/ARWKV-7B-Preview-0.1

SGLang

How to use RWKV-Red-Team/ARWKV-7B-Preview-0.1 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-Red-Team/ARWKV-7B-Preview-0.1" \
    --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-Red-Team/ARWKV-7B-Preview-0.1",
		"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-Red-Team/ARWKV-7B-Preview-0.1" \
        --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-Red-Team/ARWKV-7B-Preview-0.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use RWKV-Red-Team/ARWKV-7B-Preview-0.1 with Docker Model Runner:
```
docker model run hf.co/RWKV-Red-Team/ARWKV-7B-Preview-0.1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

ARWKV🪿

Paper Link👁️ | Github✅

ARWKV-7B-GATE-MLP (Preview 0.1)

Preview version with RWKV-7 time mixing and Transformer MLP

📌 Overview

ALL YOU NEED IS RWKV

This is an early preview of our 7B parameter RNN-based model, trained on 2k context length (only stage-2 applied, without SFT or DPO) through 3-stage knowledge distillation from Qwen2.5-7B-Instruct. While being a foundational version, it demonstrates:

✅ RWKV-7's efficient recurrence mechanism
✅ No self-attention, fully O(n)
✅ Constant VRAM usage
✅ Single-GPU trainability

Roadmap Notice: We will soon open-source different enhanced versions with:

🚀 16k+ context capability
🧮 Math-specific improvements
📚 RL enhanced reasoning model

How to use

pip3 install --upgrade rwkv-fla transformers

Before training: export WKV_MODE=chunk

from transformers import AutoModelForCausalLM, AutoTokenizer


model = AutoModelForCausalLM.from_pretrained(
    "RWKV-Red-Team/ARWKV-7B-Preview-0.1",
    device_map="auto",
    torch_dtype=torch.float16,
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(
    "RWKV-Red-Team/ARWKV-7B-Preview-0.1"
)

🔑 Key Features

Component	Specification	Note
Architecture	RWKV-7 TimeMix + SwiGLU	Hybrid design
Context Window	2048 training CTX	Preview limitation
Training Tokens	40M	Distillation-focused
Precision	FP16 inference recommended(16G Vram required)	15%↑ vs BF16

🏗️ Architecture Highlights

Core Modification Flow

Qwen2.5 Decoder Layer:
- Grouped Query Attention
+ RWKV-7 Time Mixing (Eq.3)
- RoPE Positional Encoding
+ State Recurrence
= Hybrid Layer Output

Downloads last month: 22

Safetensors

Model size

8B params

Tensor type

F32

Model tree for RWKV-Red-Team/ARWKV-7B-Preview-0.1

Base model

BlinkDL/rwkv-7-world

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(20)

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Finetunes

1 model

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Collection including RWKV-Red-Team/ARWKV-7B-Preview-0.1

ARWKV-7B-Preview-0.1

Collection

3 items • Updated Jan 29, 2025

Paper for RWKV-Red-Team/ARWKV-7B-Preview-0.1

ARWKV: Pretrain is not what we need, an RNN-Attention-Based Language Model Born from Transformer

Paper • 2501.15570 • Published Jan 26, 2025 • 25