Instructions to use StarRing2022/RWKV-430M-Pile-Alpaca with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use StarRing2022/RWKV-430M-Pile-Alpaca with Transformers:

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

pipe = pipeline("text-generation", model="StarRing2022/RWKV-430M-Pile-Alpaca")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("StarRing2022/RWKV-430M-Pile-Alpaca")
model = AutoModelForCausalLM.from_pretrained("StarRing2022/RWKV-430M-Pile-Alpaca", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use StarRing2022/RWKV-430M-Pile-Alpaca with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "StarRing2022/RWKV-430M-Pile-Alpaca"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "StarRing2022/RWKV-430M-Pile-Alpaca",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/StarRing2022/RWKV-430M-Pile-Alpaca

SGLang

How to use StarRing2022/RWKV-430M-Pile-Alpaca 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 "StarRing2022/RWKV-430M-Pile-Alpaca" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "StarRing2022/RWKV-430M-Pile-Alpaca",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "StarRing2022/RWKV-430M-Pile-Alpaca" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "StarRing2022/RWKV-430M-Pile-Alpaca",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use StarRing2022/RWKV-430M-Pile-Alpaca with Docker Model Runner:
```
docker model run hf.co/StarRing2022/RWKV-430M-Pile-Alpaca
```

使用HF的接口很方便地对RWKV在Alpaca格式数据集上进行全量微调及部署服务

底座模型：RWKV-430M-pile（sgugger/rwkv-430M-pile）

数据集：test.json，测试用

硬件设备：4090单卡，64G内存

训练轮数：100轮

训练耗时：5分钟左右

HF空间：https://huggingface.co/spaces/StarRing2022/Rwkv-430M-pile-Alpaca-Run

GIT开源地址：https://github.com/StarRing2022/HF-For-RWKVRaven-Alpaca/

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