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
```

zhiyuan8 commited on May 7, 2025

Commit

22c0fdc

verified ·

1 Parent(s): c912f55

Update README.md

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README.md +6 -4

README.md CHANGED Viewed

@@ -65,11 +65,9 @@ You can use this model just as any other HuggingFace models:
 from transformers import AutoModelForCausalLM, AutoTokenizer
 model = AutoModelForCausalLM.from_pretrained('fla-hub/rwkv7-2.9B-world', trust_remote_code=True)
 tokenizer = AutoTokenizer.from_pretrained('fla-hub/rwkv7-2.9B-world', trust_remote_code=True)
-model = model.cuda()
 prompt = "What is a large language model?"
 messages = [
-    {"role": "user", "content": "Who are you?"},
-    {"role": "assistant", "content": "I am a GPT-3 based model."},
     {"role": "user", "content": prompt}
 ]
 text = tokenizer.apply_chat_template(
@@ -82,7 +80,11 @@ model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
 generated_ids = model.generate(
     **model_inputs,
-    max_new_tokens=1024,
 )
 generated_ids = [
     output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)

 from transformers import AutoModelForCausalLM, AutoTokenizer
 model = AutoModelForCausalLM.from_pretrained('fla-hub/rwkv7-2.9B-world', trust_remote_code=True)
 tokenizer = AutoTokenizer.from_pretrained('fla-hub/rwkv7-2.9B-world', trust_remote_code=True)
+model = model.cuda() # Supported on Nvidia/AMD/Intel eg. model.xpu()
 prompt = "What is a large language model?"
 messages = [
     {"role": "user", "content": prompt}
 ]
 text = tokenizer.apply_chat_template(
 generated_ids = model.generate(
     **model_inputs,
+    max_new_tokens=4096,
+    do_sample=True,
+    temperature=1.0,
+    top_p=0.3,
+    repetition_penalty=1.2
 )
 generated_ids = [
     output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)