RWKV Runner

Default configs has enabled custom CUDA kernel acceleration, which is much faster and consumes much less VRAM. If you encounter possible compatibility issues, go to the Configs page and turn off Use Custom CUDA kernel to Accelerate.

If Windows Defender claims this is a virus, you can try downloading v1.0.8/v1.0.9 and letting it update automatically to the latest version, or add it to the trusted list.

For different tasks, adjusting API parameters can achieve better results. For example, for translation tasks, you can try setting Temperature to 1 and Top_P to 0.3.

Features

• RWKV model management and one-click startup
• Fully compatible with the OpenAI API, making every ChatGPT client an RWKV client. After starting the model, open http://127.0.0.1:8000/docs to view more details.
• Automatic dependency installation, requiring only a lightweight executable program
• Configs with 2G to 32G VRAM are included, works well on almost all computers
• User-friendly chat and completion interaction interface included
• Easy-to-understand and operate parameter configuration
• Built-in model conversion tool
• Built-in download management and remote model inspection
• Multilingual localization
• Theme switching
• Automatic updates

API Concurrency Stress Testing

ab -p body.json -T application/json -c 20 -n 100 -l http://127.0.0.1:8000/chat/completions

body.json:

{
  "messages": [
    {
      "role": "user",
      "content": "Hello"
    }
  ]
}

Embeddings API Example

If you are using langchain, just use OpenAIEmbeddings(openai_api_base="http://127.0.0.1:8000", openai_api_key="sk-")

import numpy as np
import requests


def cosine_similarity(a, b):
    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))


values = [
    "I am a girl",
    "我是个女孩",
    "私は女の子です",
    "广东人爱吃福建人",
    "我是个人类",
    "I am a human",
    "that dog is so cute",
    "私はねこむすめです、にゃん♪",
    "宇宙级特大事件！号外号外！"
]

embeddings = []
for v in values:
    r = requests.post("http://127.0.0.1:8000/embeddings", json={"input": v})
    embedding = r.json()["data"][0]["embedding"]
    embeddings.append(embedding)

compared_embedding = embeddings[0]

embeddings_cos_sim = [cosine_similarity(compared_embedding, e) for e in embeddings]

for i in np.argsort(embeddings_cos_sim)[::-1]:
    print(f"{embeddings_cos_sim[i]:.10f} - {values[i]}")

Todo

• [ ] Model training functionality
• [x] CUDA operator int8 acceleration
• [x] macOS support
• [x] Linux support
• [ ] Local State Cache DB

Related Repositories:

• RWKV-4-Raven: https://huggingface.co/BlinkDL/rwkv-4-raven/tree/main
• ChatRWKV: https://github.com/BlinkDL/ChatRWKV
• RWKV-LM: https://github.com/BlinkDL/RWKV-LM

Preview

Homepage

Chat

Completion

Configuration

Model Management

Download Management

Settings