Instructions to use paperfun/rwkv with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use paperfun/rwkv with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="paperfun/rwkv", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("paperfun/rwkv", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use paperfun/rwkv with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "paperfun/rwkv" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "paperfun/rwkv", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/paperfun/rwkv
- SGLang
How to use paperfun/rwkv 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 "paperfun/rwkv" \ --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": "paperfun/rwkv", "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 "paperfun/rwkv" \ --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": "paperfun/rwkv", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use paperfun/rwkv with Docker Model Runner:
docker model run hf.co/paperfun/rwkv
Upload config_7b.json
Browse files- config_7b.json +25 -0
config_7b.json
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{
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"architectures": [
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"Rwkv6ForCausalLM"
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],
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"auto_map": {
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"AutoConfig": "configuration_rwkv6.Rwkv6Config",
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"AutoModelForCausalLM": "modeling_rwkv6.Rwkv6ForCausalLM"
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},
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"attention_hidden_size": 4096,
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"bos_token_id": 0,
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"eos_token_id": 0,
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"head_size": 64,
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"head_size_divisor": 8,
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"hidden_size": 4096,
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"intermediate_size": null,
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"layer_norm_epsilon": 1e-05,
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"model_type": "rwkv6",
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"num_attention_heads": 64,
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"num_hidden_layers": 32,
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"rescale_every": 6,
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"tie_word_embeddings": false,
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"transformers_version": "4.34.0",
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"use_cache": true,
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"vocab_size": 65536
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}
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