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https://github.com/oobabooga/text-generation-webui.git
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156 lines
5.0 KiB
Markdown
156 lines
5.0 KiB
Markdown
## Audio notification
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If your computer takes a long time to generate each response for the model that you are using, you can enable an audio notification for when the response is completed. This feature was kindly contributed by HappyWorldGames in [#1277](https://github.com/oobabooga/text-generation-webui/pull/1277).
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### Installation
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Simply place a file called "notification.mp3" in the same folder as `server.py`. Here you can find some examples:
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* https://pixabay.com/sound-effects/search/ding/?duration=0-30
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* https://pixabay.com/sound-effects/search/notification/?duration=0-30
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Source: https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/1126
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This file will be automatically detected the next time you start the web UI.
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## Using LoRAs with GPTQ-for-LLaMa
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This requires using a monkey patch that is supported by this web UI: https://github.com/johnsmith0031/alpaca_lora_4bit
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To use it:
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Install alpaca_lora_4bit using pip
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```
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git clone https://github.com/johnsmith0031/alpaca_lora_4bit.git
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cd alpaca_lora_4bit
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git fetch origin winglian-setup_pip
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git checkout winglian-setup_pip
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pip install .
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```
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Start the UI with the --monkey-patch flag:
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```
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python server.py --model llama-7b-4bit-128g --listen --lora tloen_alpaca-lora-7b --monkey-patch
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```
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## DeepSpeed
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`DeepSpeed ZeRO-3` is an alternative offloading strategy for full-precision (16-bit) transformers models.
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With this, I have been able to load a 6b model (GPT-J 6B) with less than 6GB of VRAM. The speed of text generation is very decent and much better than what would be accomplished with `--auto-devices --gpu-memory 6`.
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As far as I know, DeepSpeed is only available for Linux at the moment.
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### How to use it
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1. Install DeepSpeed:
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```
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conda install -c conda-forge mpi4py mpich
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pip install -U deepspeed
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```
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2. Start the web UI replacing `python` with `deepspeed --num_gpus=1` and adding the `--deepspeed` flag. Example:
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```
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deepspeed --num_gpus=1 server.py --deepspeed --chat --model gpt-j-6B
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```
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> RWKV: RNN with Transformer-level LLM Performance
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>
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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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### Hugging Face weights
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Simply download the weights from https://huggingface.co/RWKV and load them as you would for any other model.
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There is a bug in transformers==4.29.2 that prevents RWKV from being loaded in 8-bit mode. You can install the dev branch to solve this bug: `pip install git+https://github.com/huggingface/transformers`
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### Original .pth weights
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The instructions below are from before RWKV was supported in transformers, and they are kept for legacy purposes. The old implementation is possibly faster, but it lacks the full range of samplers that the transformers library offers.
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#### 0. Install the RWKV library
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```
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pip install rwkv
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```
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`0.7.3` was the last version that I tested. If you experience any issues, try ```pip install rwkv==0.7.3```.
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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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## Miscellaneous info
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### You can train LoRAs in CPU mode
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Load the web UI with
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```
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python server.py --cpu
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```
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and start training the LoRA from the training tab as usual.
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### You can check the sha256sum of downloaded models with the download script
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```
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python download-model.py facebook/galactica-125m --check
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```
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### The download script continues interrupted downloads by default
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It doesn't start over.
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