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 generation_config.json from TimeMobius/Mobius-RWKV-Chat-12B-128k-v4-HF: direct link, hf CLI and curl.
- Browser
- Download file 260 Bytes
-
https://huggingface.co/TimeMobius/Mobius-RWKV-Chat-12B-128k-v4-HF/resolve/1964fc834df8298384a4ae844b74ce2d737e6e6e/generation_config.json
- Command line
-
hf download hf://TimeMobius/Mobius-RWKV-Chat-12B-128k-v4-HF@1964fc834df8298384a4ae844b74ce2d737e6e6e/generation_config.json
-
curl -L -o generation_config.json https://huggingface.co/TimeMobius/Mobius-RWKV-Chat-12B-128k-v4-HF/resolve/1964fc834df8298384a4ae844b74ce2d737e6e6e/generation_config.json
260 Bytes
| { | |
| "chat_format": "chatml", | |
| "eos_token_id": 0, | |
| "pad_token_id": 0, | |
| "max_window_size": 4096, | |
| "max_new_tokens": 4096, | |
| "do_sample": true, | |
| "top_k": 0, | |
| "top_p": 0.1, | |
| "repetition_penalty": 1.0, | |
| "transformers_version": "4.31.1" | |
| } |