Instructions to use TimeMobius/Mobius-RWKV-Chat-12B-128k-v4-HF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TimeMobius/Mobius-RWKV-Chat-12B-128k-v4-HF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="TimeMobius/Mobius-RWKV-Chat-12B-128k-v4-HF", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("TimeMobius/Mobius-RWKV-Chat-12B-128k-v4-HF", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use TimeMobius/Mobius-RWKV-Chat-12B-128k-v4-HF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "TimeMobius/Mobius-RWKV-Chat-12B-128k-v4-HF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "TimeMobius/Mobius-RWKV-Chat-12B-128k-v4-HF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/TimeMobius/Mobius-RWKV-Chat-12B-128k-v4-HF
- SGLang
How to use TimeMobius/Mobius-RWKV-Chat-12B-128k-v4-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 "TimeMobius/Mobius-RWKV-Chat-12B-128k-v4-HF" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "TimeMobius/Mobius-RWKV-Chat-12B-128k-v4-HF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "TimeMobius/Mobius-RWKV-Chat-12B-128k-v4-HF" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "TimeMobius/Mobius-RWKV-Chat-12B-128k-v4-HF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use TimeMobius/Mobius-RWKV-Chat-12B-128k-v4-HF with Docker Model Runner:
docker model run hf.co/TimeMobius/Mobius-RWKV-Chat-12B-128k-v4-HF
Download tokenizer_config.json from TimeMobius/Mobius-RWKV-Chat-12B-128k-v4-HF: direct link, hf CLI and curl.
- Browser
- Download file 242 Bytes
-
https://huggingface.co/TimeMobius/Mobius-RWKV-Chat-12B-128k-v4-HF/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://TimeMobius/Mobius-RWKV-Chat-12B-128k-v4-HF/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/TimeMobius/Mobius-RWKV-Chat-12B-128k-v4-HF/resolve/main/tokenizer_config.json
242 Bytes
| { | |
| "name_or_path": "rwkv-world", | |
| "add_prefix_space": false, | |
| "tokenizer_class": "RWKVWorldTokenizer", | |
| "use_fast": false, | |
| "auto_map": { | |
| "AutoTokenizer": [ | |
| "tokenization_rwkv_world.RWKVWorldTokenizer", | |
| null | |
| ] | |
| } | |
| } |