Instructions to use RWKV/RWKV7-Goose-World3-2.9B-HF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RWKV/RWKV7-Goose-World3-2.9B-HF with Transformers:

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

pipe = pipeline("text-generation", model="RWKV/RWKV7-Goose-World3-2.9B-HF", 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/RWKV7-Goose-World3-2.9B-HF", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use RWKV/RWKV7-Goose-World3-2.9B-HF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RWKV/RWKV7-Goose-World3-2.9B-HF"
# 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/RWKV7-Goose-World3-2.9B-HF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/RWKV/RWKV7-Goose-World3-2.9B-HF

SGLang

How to use RWKV/RWKV7-Goose-World3-2.9B-HF 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/RWKV7-Goose-World3-2.9B-HF" \
    --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/RWKV7-Goose-World3-2.9B-HF",
		"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/RWKV7-Goose-World3-2.9B-HF" \
        --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/RWKV7-Goose-World3-2.9B-HF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use RWKV/RWKV7-Goose-World3-2.9B-HF with Docker Model Runner:
```
docker model run hf.co/RWKV/RWKV7-Goose-World3-2.9B-HF
```

RWKV7-Goose-World3-2.9B-HF

File size: 1,101 Bytes

ed9ca75

{
  "add_prefix_space": false,
  "added_tokens_decoder": {
    "0": {
      "content": "<|rwkv_tokenizer_end_of_text|>",
      "lstrip": false,
      "normalized": false,
      "rstrip": false,
      "single_word": false,
      "special": true
    }
  },
  "auto_map": {
    "AutoTokenizer": [
      "hf_rwkv_tokenizer.RwkvTokenizer",
      null
    ]
  },
  "bos_token": "<|rwkv_tokenizer_end_of_text|>",
  "pad_token": "<|rwkv_tokenizer_end_of_text|>",
  "clean_up_tokenization_spaces": false,
  "eos_token": "\n\n",
  "model_max_length": 1000000000000000019884624838656,
  "tokenizer_class": "RwkvTokenizer",
  "unk_token": "<|rwkv_tokenizer_end_of_text|>",
  "use_fast": false,
  "chat_template": "{{ '<|rwkv_tokenizer_end_of_text|>' }}{% for message in messages %}{% if message['role'] == 'user' %}{{'User: ' + message['content'] + '\n\n'}}{% elif message['role'] == 'system' %}{{'System: ' + message['content'] + '\n\n'}}{% elif message['role'] == 'assistant' %}{{'Assistant: ' + message['content'] + '\n\n'}}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ 'Assistant:' }}{% endif %}"
}