Instructions to use RWKV-Red-Team/ARWKV-7B-Preview-0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RWKV-Red-Team/ARWKV-7B-Preview-0.1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="RWKV-Red-Team/ARWKV-7B-Preview-0.1", 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-Red-Team/ARWKV-7B-Preview-0.1", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use RWKV-Red-Team/ARWKV-7B-Preview-0.1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "RWKV-Red-Team/ARWKV-7B-Preview-0.1" # 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-Red-Team/ARWKV-7B-Preview-0.1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/RWKV-Red-Team/ARWKV-7B-Preview-0.1
- SGLang
How to use RWKV-Red-Team/ARWKV-7B-Preview-0.1 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-Red-Team/ARWKV-7B-Preview-0.1" \ --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-Red-Team/ARWKV-7B-Preview-0.1", "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-Red-Team/ARWKV-7B-Preview-0.1" \ --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-Red-Team/ARWKV-7B-Preview-0.1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use RWKV-Red-Team/ARWKV-7B-Preview-0.1 with Docker Model Runner:
docker model run hf.co/RWKV-Red-Team/ARWKV-7B-Preview-0.1
| import torch | |
| from typing import Any, Dict, Optional, Union | |
| from transformers.cache_utils import DynamicCache | |
| class AttnState: | |
| def __init__(self, shift_state: torch.Tensor, wkv_state: torch.Tensor): | |
| self.shift_state = shift_state | |
| self.wkv_state = wkv_state | |
| class FfnState: | |
| def __init__(self, shift_state: torch.Tensor): | |
| self.shift_state = shift_state | |
| class BlockState: | |
| def __init__( | |
| self, | |
| attn_state: AttnState, | |
| ffn_state: FfnState | |
| ): | |
| self.attn_state = attn_state | |
| self.ffn_state = ffn_state | |
| class HybridCache(DynamicCache): | |
| def __init__(self) -> None: | |
| super().__init__() | |
| self.rwkv_layers = set() | |
| self.key_cache_nums = 0 | |
| self.v_first_cache = None | |
| def update( | |
| self, | |
| key_states: Union[int, torch.Tensor], | |
| value_states: Union[torch.Tensor, BlockState], | |
| layer_idx: int, | |
| cache_kwargs: Optional[Dict[str, Any]] = None | |
| ): | |
| if isinstance(key_states, int) and isinstance(value_states, BlockState): | |
| self.rwkv_layers.add(layer_idx) | |
| if layer_idx >= self.key_cache_nums: | |
| self.key_cache.append([]) | |
| self.value_cache.append([]) | |
| self.key_cache[layer_idx].append(key_states) | |
| self.value_cache[layer_idx].append(value_states) | |
| self.key_cache_nums += 1 | |
| else: | |
| self.key_cache[layer_idx][0] += key_states | |
| self.value_cache[layer_idx][0] = value_states | |
| return key_states, value_states | |
| return super().update(key_states, value_states, layer_idx, cache_kwargs) | |
| def update_v_first(self, v_first: torch.Tensor): | |
| self.v_first_cache = v_first | |
| def get_v_first(self): | |
| return self.v_first_cache | |
| def get_seq_length(self, layer_idx: Optional[int] = 0): | |
| if layer_idx in self.rwkv_layers: | |
| return self.key_cache[layer_idx][0] | |
| return super().get_seq_length(layer_idx) | |
| def reorder_cache(self, beam_idx): | |
| return super().reorder_cache(beam_idx) | |
| def __getitem__(self, item): | |
| if item in self.rwkv_layers: | |
| return self.value_cache[item] | |
| return super().__getitem__(item) | |