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)# pip install -U transformers accelerate # 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
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Download README.md from RWKV/v6-Finch-14B-HF: direct link, hf CLI and curl.
- Browser
- Download file 298 Bytes
-
https://huggingface.co/RWKV/v6-Finch-14B-HF/resolve/c262f31485217c52b435e00d7d3bdd14b0cbbf01/README.md
- Command line
-
hf download hf://RWKV/v6-Finch-14B-HF@c262f31485217c52b435e00d7d3bdd14b0cbbf01/README.md
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curl -L -o README.md https://huggingface.co/RWKV/v6-Finch-14B-HF/resolve/c262f31485217c52b435e00d7d3bdd14b0cbbf01/README.md
298 Bytes
v6-Finch-14B-HF
HF compatible model for Finch-14B. This is an early preview for testing. This is not final
origin pth weight at https://huggingface.co/BlinkDL/rwkv-6-world/blob/main/ .
More details to be done.
