Update README

This commit is contained in:
oobabooga 2023-06-05 15:16:06 -03:00
parent 6a75bda419
commit 632571a009
5 changed files with 23 additions and 39 deletions

View file

@ -10,28 +10,23 @@ Its goal is to become the [AUTOMATIC1111/stable-diffusion-webui](https://github.
## Features
* Dropdown menu for switching between models
* Notebook mode that resembles OpenAI's playground
* Chat mode for conversation and role-playing
* Instruct mode compatible with various formats, including Alpaca, Vicuna, Open Assistant, Dolly, Koala, ChatGLM, MOSS, RWKV-Raven, Galactica, StableLM, WizardLM, Baize, Ziya, Chinese-Vicuna, MPT, INCITE, Wizard Mega, KoAlpaca, Vigogne, Bactrian, h2o, and OpenBuddy
* 3 interface modes: default, notebook, and chat
* Dropdown menu for quickly switching between different models
* LoRA: load and unload LoRAs on the fly, load multiple LoRAs at the same time, train a new LoRA
* Precise instruction templates in chat mode, including Alpaca, Vicuna, Open Assistant, Dolly, Koala, ChatGLM, MOSS, RWKV-Raven, Galactica, StableLM, WizardLM, Baize, Ziya, Chinese-Vicuna, MPT, INCITE, Wizard Mega, KoAlpaca, Vigogne, Bactrian, h2o, and OpenBuddy
* [Multimodal pipelines, including LLaVA and MiniGPT-4](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/multimodal)
* Multiple model backends: tranformers, llama.cpp, AutoGPTQ, GPTQ-for-LLaMa, RWKV, FlexGen
* 8-bit and 4-bit inference through bitsandbytes
* CPU mode for transformers models
* [DeepSpeed ZeRO-3 inference](docs/DeepSpeed.md)
* [Extension support](docs/Extensions.md)
* [Custom chat characters](docs/Chat-mode.md)
* Very efficient text streaming
* Markdown output with LaTeX rendering, to use for instance with [GALACTICA](https://github.com/paperswithcode/galai)
* Nice HTML output for GPT-4chan
* [Custom chat characters](docs/Chat-mode.md)
* Advanced chat features (send images, get audio responses with TTS)
* Very efficient text streaming
* Parameter presets
* [LLaMA model](docs/LLaMA-model.md)
* [4-bit GPTQ mode](docs/GPTQ-models-(4-bit-mode).md)
* [LoRA (loading and training)](docs/Using-LoRAs.md)
* [llama.cpp](docs/llama.cpp-models.md)
* 8-bit and 4-bit through bitsandbytes
* Layers splitting across GPU(s), CPU, and disk
* CPU mode
* [FlexGen](docs/FlexGen.md)
* [DeepSpeed ZeRO-3](docs/DeepSpeed.md)
* API [with](https://github.com/oobabooga/text-generation-webui/blob/main/api-example-stream.py) streaming and [without](https://github.com/oobabooga/text-generation-webui/blob/main/api-example.py) streaming
* [Extensions](docs/Extensions.md) - see the [user extensions list](https://github.com/oobabooga/text-generation-webui-extensions)
* API, including endpoints for websocket streaming ([see the examples](https://github.com/oobabooga/text-generation-webui/blob/main/api-examples))
To learn how to use the various features, check out the Documentation: https://github.com/oobabooga/text-generation-webui/tree/main/docs
## Installation
@ -95,14 +90,6 @@ cd text-generation-webui
pip install -r requirements.txt
```
#### 4. Install GPTQ
The base installation covers [transformers](https://github.com/huggingface/transformers) models (`AutoModelForCausalLM` and `AutoModelForSeq2SeqLM` specifically) and [llama.cpp](https://github.com/ggerganov/llama.cpp) (GGML) models.
To use GPTQ models, the additional installation steps below are necessary:
[GPTQ models (4 bit mode)](https://github.com/oobabooga/text-generation-webui/blob/main/docs/GPTQ-models-(4-bit-mode).md)
#### llama.cpp with GPU acceleration
Requires the additional compilation step described here: [GPU acceleration](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp-models.md#gpu-acceleration).
@ -154,9 +141,7 @@ For example:
python download-model.py facebook/opt-1.3b
* If you want to download a model manually, note that all you need are the json, txt, and pytorch\*.bin (or model*.safetensors) files. The remaining files are not necessary.
* Set env vars `HF_USER` and `HF_PASS` to your Hugging Face username and password (or [User Access Token](https://huggingface.co/settings/tokens)) to download a protected model. The model's terms must first be accepted on the HF website.
To download a protected model, set env vars `HF_USER` and `HF_PASS` to your Hugging Face username and password (or [User Access Token](https://huggingface.co/settings/tokens)). The model's terms must first be accepted on the HF website.
#### GGML models
@ -164,6 +149,10 @@ You can drop these directly into the `models/` folder, making sure that the file
#### GPT-4chan
<details>
<summary>
Download instructions
</summary>
[GPT-4chan](https://huggingface.co/ykilcher/gpt-4chan) has been shut down from Hugging Face, so you need to download it elsewhere. You have two options:
* Torrent: [16-bit](https://archive.org/details/gpt4chan_model_float16) / [32-bit](https://archive.org/details/gpt4chan_model)
@ -181,6 +170,9 @@ After downloading the model, follow these steps:
python download-model.py EleutherAI/gpt-j-6B --text-only
```
When you load this model in default or notebook modes, the "HTML" tab will show the generated text in 4chan format.
</details>
## Starting the web UI
conda activate textgen
@ -331,15 +323,7 @@ Out of memory errors? [Check the low VRAM guide](docs/Low-VRAM-guide.md).
Inference settings presets can be created under `presets/` as yaml files. These files are detected automatically at startup.
By default, 10 presets based on NovelAI and KoboldAI presets are included. These were selected out of a sample of 43 presets after applying a K-Means clustering algorithm and selecting the elements closest to the average of each cluster.
[Visualization](https://user-images.githubusercontent.com/112222186/228956352-1addbdb9-2456-465a-b51d-089f462cd385.png)
## Documentation
Make sure to check out the documentation for an in-depth guide on how to use the web UI.
https://github.com/oobabooga/text-generation-webui/tree/main/docs
By default, 10 presets based on NovelAI and KoboldAI presets are included. These were selected out of a sample of 43 presets after applying a K-Means clustering algorithm and selecting the elements closest to the average of each cluster: [tSNE visualization](https://user-images.githubusercontent.com/112222186/228956352-1addbdb9-2456-465a-b51d-089f462cd385.png).
## Contributing