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https://github.com/oobabooga/text-generation-webui.git
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54 lines
1.9 KiB
Markdown
54 lines
1.9 KiB
Markdown
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> RWKV: RNN with Transformer-level LLM Performance
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> It combines the best of RNN and transformer - great performance, fast inference, saves VRAM, fast training, "infinite" ctx_len, and free sentence embedding (using the final hidden state).
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https://github.com/BlinkDL/RWKV-LM
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https://github.com/BlinkDL/ChatRWKV
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## Using RWKV in the web UI
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#### 1. Download the model
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It is available in different sizes:
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* https://huggingface.co/BlinkDL/rwkv-4-pile-3b/
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* https://huggingface.co/BlinkDL/rwkv-4-pile-7b/
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* https://huggingface.co/BlinkDL/rwkv-4-pile-14b/
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There are also older releases with smaller sizes like:
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* https://huggingface.co/BlinkDL/rwkv-4-pile-169m/resolve/main/RWKV-4-Pile-169M-20220807-8023.pth
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Download the chosen `.pth` and put it directly in the `models` folder.
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#### 2. Download the tokenizer
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[20B_tokenizer.json](https://raw.githubusercontent.com/BlinkDL/ChatRWKV/main/v2/20B_tokenizer.json)
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Also put it directly in the `models` folder. Make sure to not rename it. It should be called `20B_tokenizer.json`.
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#### 3. Launch the web UI
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No additional steps are required. Just launch it as you would with any other model.
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```
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python server.py --listen --no-stream --model RWKV-4-Pile-169M-20220807-8023.pth
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```
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## Setting a custom strategy
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It is possible to have very fine control over the offloading and precision for the model with the `--rwkv-strategy` flag. Possible values include:
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```
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"cpu fp32" # CPU mode
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"cuda fp16" # GPU mode with float16 precision
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"cuda fp16 *30 -> cpu fp32" # GPU+CPU offloading. The higher the number after *, the higher the GPU allocation.
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"cuda fp16i8" # GPU mode with 8-bit precision
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```
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See the README for the PyPl package for more details: https://pypi.org/project/rwkv/
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## Compiling the CUDA kernel
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You can compile the CUDA kernel for the model with `--rwkv-cuda-on`. This should improve the performance a lot but I haven't been able to get it to work yet.
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