Instructions to use RWKV/v6-Finch-14B-HF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RWKV/v6-Finch-14B-HF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="RWKV/v6-Finch-14B-HF", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("RWKV/v6-Finch-14B-HF", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use RWKV/v6-Finch-14B-HF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "RWKV/v6-Finch-14B-HF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "RWKV/v6-Finch-14B-HF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/RWKV/v6-Finch-14B-HF
- SGLang
How to use RWKV/v6-Finch-14B-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 "RWKV/v6-Finch-14B-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": "RWKV/v6-Finch-14B-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 "RWKV/v6-Finch-14B-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": "RWKV/v6-Finch-14B-HF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use RWKV/v6-Finch-14B-HF with Docker Model Runner:
docker model run hf.co/RWKV/v6-Finch-14B-HF
Download pytorch_model.bin from RWKV/v6-Finch-14B-HF: direct link, hf CLI and curl.
- Browser
- Download file 28.1 GB
-
https://huggingface.co/RWKV/v6-Finch-14B-HF/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://RWKV/v6-Finch-14B-HF/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/RWKV/v6-Finch-14B-HF/resolve/main/pytorch_model.bin
28.1 GB
- Xet hash:
- 87cf6ee1856039a3408ea9c7cfa0cc8ab9a211dbc12d7a7fa838d1d267fd7065
- Size of remote file:
- 28.1 GB
- SHA256:
- f5f3477146b7dc0e68a1e64faac1e3b87a257d10f69a0078908d8d9bce6aaae9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.