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
```

rwkv7-2.9B-world / README.md

nielsr HF Staff

Add library_name to metadata

fb60df0 verified over 1 year ago

2.94 kB

base_model:
  - BlinkDL/rwkv-7-world
language:
  - en
  - zh
  - ja
  - ko
  - fr
  - ar
  - es
  - pt
license: apache-2.0
metrics:
  - accuracy
pipeline_tag: text-generation
library_name: transformers

# rwkv7-2.9B-world 

<!-- Provide a quick summary of what the model is/does. -->

This is RWKV-7 model under flash-linear attention format.

## Model Details


### Model Description

<!-- Provide a longer summary of what this model is. -->

- **Developed by:** Bo Peng, Yu Zhang, Songlin Yang, Ruichong Zhang
- **Funded by:** RWKV Project (Under LF AI & Data Foundation)
- **Model type:** RWKV7
- **Language(s) (NLP):** English
- **License:** Apache-2.0
- **Parameter count:** 2.9B
- **Tokenizer:** RWKV World tokenizer
- **Vocabulary size:** 65,536

### Model Sources

<!-- Provide the basic links for the model. -->

- **Repository:** https://github.com/fla-org/flash-linear-attention ; https://github.com/BlinkDL/RWKV-LM
- **Paper:** https://arxiv.org/abs/2503.14456

## Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
Install `flash-linear-attention` and the latest version of `transformers` before using this model:

```bash
pip install git+https://github.com/fla-org/flash-linear-attention
pip install 'transformers>=4.48.0'

Direct Use

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(
    messages,
    tokenize=False,
    add_generation_prompt=True
)

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)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=False)[0]
print(response)

Training Data

This model is trained on the World v3 with a total of 3.119 trillion tokens.

Training Hyperparameters

Training regime: bfloat16, lr 4e-4 to 1e-5 "delayed" cosine decay, wd 0.1 (with increasing batch sizes during the middle)
Final Loss: 1.8745
Token Count: 3.119 trillion

FAQ

Q: safetensors metadata is none.

A: upgrade transformers to >=4.48.0: pip install 'transformers>=4.48.0' ```