Instructions to use fla-hub/rwkv7-2.9B-world with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use fla-hub/rwkv7-2.9B-world with Transformers:

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

pipe = pipeline("text-generation", model="fla-hub/rwkv7-2.9B-world", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("fla-hub/rwkv7-2.9B-world", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use fla-hub/rwkv7-2.9B-world with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "fla-hub/rwkv7-2.9B-world"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "fla-hub/rwkv7-2.9B-world",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/fla-hub/rwkv7-2.9B-world

SGLang

How to use fla-hub/rwkv7-2.9B-world 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 "fla-hub/rwkv7-2.9B-world" \
    --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": "fla-hub/rwkv7-2.9B-world",
		"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 "fla-hub/rwkv7-2.9B-world" \
        --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": "fla-hub/rwkv7-2.9B-world",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use fla-hub/rwkv7-2.9B-world with Docker Model Runner:
```
docker model run hf.co/fla-hub/rwkv7-2.9B-world
```

Add library_name to metadata

by nielsr HF Staff - opened Mar 19, 2025

base: refs/heads/main

←

from: refs/pr/2

Discussion Files changed

-4

nielsr

Mar 19, 2025

This PR adds the library_name: transformers metadata tag to the model card. This is necessary for proper functionality and discoverability on the Hugging Face Hub. The model uses the transformers library, as evidenced by the provided usage examples and the presence of a config.json file.

Add library_name to metadatafb60df0b

deleted changed pull request status to merged Mar 20, 2025

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