AI/text-generation-webui
1
0
Fork 0
mirror of https://github.com/oobabooga/text-generation-webui.git synced 2024-10-01 01:26:03 -04:00
Code Issues Packages Projects Releases Wiki Activity
aad14174e4
text-generation-webui/README.md
oobabooga aad14174e4 Update README
2023-12-13 22:46:18 -08:00

434 lines
24 KiB
Markdown
Raw Blame History

# Text generation web UI
A Gradio web UI for Large Language Models.
Its goal is to become the [AUTOMATIC1111/stable-diffusion-webui](https://github.com/AUTOMATIC1111/stable-diffusion-webui) of text generation.
|![Image1](https://github.com/oobabooga/screenshots/raw/main/print_instruct.png) | ![Image2](https://github.com/oobabooga/screenshots/raw/main/print_chat.png) |
|:---:|:---:|
|![Image1](https://github.com/oobabooga/screenshots/raw/main/print_default.png) | ![Image2](https://github.com/oobabooga/screenshots/raw/main/print_parameters.png) |
## Features
* 3 interface modes: default (two columns), notebook, and chat.
* Multiple model backends: [Transformers](https://github.com/huggingface/transformers), [llama.cpp](https://github.com/ggerganov/llama.cpp) (through [llama-cpp-python](https://github.com/abetlen/llama-cpp-python)), [ExLlama](https://github.com/turboderp/exllama), [ExLlamaV2](https://github.com/turboderp/exllamav2), [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ), [AutoAWQ](https://github.com/casper-hansen/AutoAWQ), [GPTQ-for-LLaMa](https://github.com/qwopqwop200/GPTQ-for-LLaMa), [CTransformers](https://github.com/marella/ctransformers), [QuIP#](https://github.com/Cornell-RelaxML/quip-sharp).
* Dropdown menu for quickly switching between different models.
* Large number of extensions (built-in and user-contributed), including Coqui TTS for realistic voice outputs, Whisper STT for voice inputs, translation, [multimodal pipelines](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/multimodal), vector databases, Stable Diffusion integration, and a lot more. See [the wiki](https://github.com/oobabooga/text-generation-webui/wiki/07-%E2%80%90-Extensions) and [the extensions directory](https://github.com/oobabooga/text-generation-webui-extensions) for details.
* [Chat with custom characters](https://github.com/oobabooga/text-generation-webui/wiki/03-%E2%80%90-Parameters-Tab#character).
* Precise templates for instruction-following models, including Llama-2-chat, Alpaca, Vicuna, Mistral, and many others.
* Easy UI for training LoRAs, as well as loading/unloading them on the fly.
* HF transformers integration: load models in 4-bit or 8-bit quantization through bitsandbytes, use llama.cpp with transformers samplers (`llamacpp_HF` loader), CPU inference in 32-bit precision using PyTorch.
* OpenAI-compatible API server with Chat and Completions endpoints -- see the [examples](https://github.com/oobabooga/text-generation-webui/wiki/12-%E2%80%90-OpenAI-API#examples).
## How to install
1) Clone or [download](https://github.com/oobabooga/text-generation-webui/archive/refs/heads/main.zip) the repository.
2) Run the `start_linux.sh`, `start_windows.bat`, `start_macos.sh`, or `start_wsl.bat` script depending on your OS.
3) Select your GPU vendor when asked.
4) Once the installation ends, browse to `http://localhost:7860/?__theme=dark`.
5) Have fun!
To restart the web UI later, just run the `start_` script once again. This script will set up the necessary files for the web UI in the `installer_files` folder. In case you need to reinstall the project's requirements, you can easily do so by deleting that folder and then running the `start_` script again.
You also have the option of using command-line flags with the script. As an alternative, you can edit the `CMD_FLAGS.txt` file with a text editor and include your flags there.
To get updates in the future, run `update_linux.sh`, `update_windows.bat`, `update_macos.sh`, or `update_wsl.bat`.
<details>
<summary>
Setup details and information about installing manually
</summary>
### One-click-installer
#### How it works
The script creates a folder called `installer_files` where it sets up a Conda environment using Miniconda.
#### Running commands
If you ever need to install something manually in the `installer_files` environment, you can launch an interactive shell using the cmd script: `cmd_linux.sh`, `cmd_windows.bat`, `cmd_macos.sh`, or `cmd_wsl.bat`.
#### Other info
* There is no need to run any of those scripts as admin/root.
* For additional instructions about AMD setup and WSL setup, consult [the documentation](https://github.com/oobabooga/text-generation-webui/wiki).
* The installer has been tested mostly on NVIDIA GPUs. If you can find a way to improve it for your AMD/Intel Arc/Mac Metal GPU, you are highly encouraged to submit a PR to this repository. The main file to be edited is `one_click.py`.
* For automated installation, you can use the `GPU_CHOICE`, `USE_CUDA118`, `LAUNCH_AFTER_INSTALL`, and `INSTALL_EXTENSIONS` environment variables. For instance: `GPU_CHOICE=A USE_CUDA118=FALSE LAUNCH_AFTER_INSTALL=FALSE INSTALL_EXTENSIONS=FALSE ./start_linux.sh`.
### Manual installation using Conda
Recommended if you have some experience with the command-line.
#### 0. Install Conda
https://docs.conda.io/en/latest/miniconda.html
On Linux or WSL, it can be automatically installed with these two commands ([source](https://educe-ubc.github.io/conda.html)):
```
curl -sL "https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh" > "Miniconda3.sh"
bash Miniconda3.sh
```
#### 1. Create a new conda environment
```
conda create -n textgen python=3.11
conda activate textgen
```
#### 2. Install Pytorch
| System | GPU | Command |
|--------|---------|---------|
| Linux/WSL | NVIDIA | `pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121` |
| Linux/WSL | CPU only | `pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu` |
| Linux | AMD | `pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm5.6` |
| MacOS + MPS | Any | `pip3 install torch torchvision torchaudio` |
| Windows | NVIDIA | `pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121` |
| Windows | CPU only | `pip3 install torch torchvision torchaudio` |
The up-to-date commands can be found here: https://pytorch.org/get-started/locally/.
For NVIDIA, you also need to install the CUDA runtime libraries:
```
conda install -y -c "nvidia/label/cuda-12.1.1" cuda-runtime
```
If you need `nvcc` to compile some library manually, replace the command above with
```
conda install -y -c "nvidia/label/cuda-12.1.1" cuda
```
#### 3. Install the web UI
```
git clone https://github.com/oobabooga/text-generation-webui
cd text-generation-webui
pip install -r <requirements file according to table below>
```
Requirements file to use:
| GPU | CPU | requirements file to use |
|--------|---------|---------|
| NVIDIA | has AVX2 | `requirements.txt` |
| NVIDIA | no AVX2 | `requirements_noavx2.txt` |
| AMD | has AVX2 | `requirements_amd.txt` |
| AMD | no AVX2 | `requirements_amd_noavx2.txt` |
| CPU only | has AVX2 | `requirements_cpu_only.txt` |
| CPU only | no AVX2 | `requirements_cpu_only_noavx2.txt` |
| Apple | Intel | `requirements_apple_intel.txt` |
| Apple | Apple Silicon | `requirements_apple_silicon.txt` |
### Start the web UI
conda activate textgen
cd text-generation-webui
python server.py
Then browse to
`http://localhost:7860/?__theme=dark`
##### AMD GPU on Windows
1) Use `requirements_cpu_only.txt` or `requirements_cpu_only_noavx2.txt` in the command above.
2) Manually install llama-cpp-python using the appropriate command for your hardware: [Installation from PyPI](https://github.com/abetlen/llama-cpp-python#installation-with-hardware-acceleration).
* Use the `LLAMA_HIPBLAS=on` toggle.
* Note the [Windows remarks](https://github.com/abetlen/llama-cpp-python#windows-remarks).
3) Manually install AutoGPTQ: [Installation](https://github.com/PanQiWei/AutoGPTQ#install-from-source).
* Perform the from-source installation - there are no prebuilt ROCm packages for Windows.
4) Manually install [ExLlama](https://github.com/turboderp/exllama) by simply cloning it into the `repositories` folder (it will be automatically compiled at runtime after that):
```sh
cd text-generation-webui
git clone https://github.com/turboderp/exllama repositories/exllama
```
##### Older NVIDIA GPUs
1) For Kepler GPUs and older, you will need to install CUDA 11.8 instead of 12:
```
pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
conda install -y -c "nvidia/label/cuda-11.8.0" cuda-runtime
```
2) bitsandbytes >= 0.39 may not work. In that case, to use `--load-in-8bit`, you may have to downgrade like this:
* Linux: `pip install bitsandbytes==0.38.1`
* Windows: `pip install https://github.com/jllllll/bitsandbytes-windows-webui/raw/main/bitsandbytes-0.38.1-py3-none-any.whl`
##### Manual install
The requirements*.txt above contain various precompiled wheels. If you wish to compile things manually, or if you need to because no suitable wheels are available for your hardware, you can use `requirements_nowheels.txt` and then install your desired loaders manually.
### Alternative: Docker
```
ln -s docker/{nvidia/Dockerfile,docker-compose.yml,.dockerignore} .
cp docker/.env.example .env
# Edit .env and set:
# TORCH_CUDA_ARCH_LIST based on your GPU model
# APP_RUNTIME_GID your host user's group id (run `id -g` in a terminal)
# BUILD_EXTENIONS optionally add comma separated list of extensions to build
docker compose up --build
```
* You need to have Docker Compose v2.17 or higher installed. See [this guide](https://github.com/oobabooga/text-generation-webui/wiki/09-%E2%80%90-Docker) for instructions.
* For additional docker files, check out [this repository](https://github.com/Atinoda/text-generation-webui-docker).
### Updating the requirements
From time to time, the `requirements*.txt` changes. To update, use these commands:
```
conda activate textgen
cd text-generation-webui
pip install -r <requirements file that you have used> --upgrade
```
</details>
<details>
<summary>
List of command-line flags
</summary>
#### Basic settings
| Flag | Description |
|--------------------------------------------|-------------|
| `-h`, `--help` | show this help message and exit |
| `--multi-user` | Multi-user mode. Chat histories are not saved or automatically loaded. WARNING: this is likely not safe for sharing publicly. |
| `--character CHARACTER` | The name of the character to load in chat mode by default. |
| `--model MODEL` | Name of the model to load by default. |
| `--lora LORA [LORA ...]` | The list of LoRAs to load. If you want to load more than one LoRA, write the names separated by spaces. |
| `--model-dir MODEL_DIR` | Path to directory with all the models. |
| `--lora-dir LORA_DIR` | Path to directory with all the loras. |
| `--model-menu` | Show a model menu in the terminal when the web UI is first launched. |
| `--settings SETTINGS_FILE` | Load the default interface settings from this yaml file. See `settings-template.yaml` for an example. If you create a file called `settings.yaml`, this file will be loaded by default without the need to use the `--settings` flag. |
| `--extensions EXTENSIONS [EXTENSIONS ...]` | The list of extensions to load. If you want to load more than one extension, write the names separated by spaces. |
| `--verbose` | Print the prompts to the terminal. |
| `--chat-buttons` | Show buttons on the chat tab instead of a hover menu. |
#### Model loader
| Flag | Description |
|--------------------------------------------|-------------|
| `--loader LOADER` | Choose the model loader manually, otherwise, it will get autodetected. Valid options: Transformers, llama.cpp, llamacpp_HF, ExLlama_HF, ExLlamav2_HF, AutoGPTQ, AutoAWQ, GPTQ-for-LLaMa, ExLlama, ExLlamav2, ctransformers, QuIP#. |
#### Accelerate/transformers
| Flag | Description |
|---------------------------------------------|-------------|
| `--cpu` | Use the CPU to generate text. Warning: Training on CPU is extremely slow. |
| `--auto-devices` | Automatically split the model across the available GPU(s) and CPU. |
| `--gpu-memory GPU_MEMORY [GPU_MEMORY ...]` | Maximum GPU memory in GiB to be allocated per GPU. Example: --gpu-memory 10 for a single GPU, --gpu-memory 10 5 for two GPUs. You can also set values in MiB like --gpu-memory 3500MiB. |
| `--cpu-memory CPU_MEMORY` | Maximum CPU memory in GiB to allocate for offloaded weights. Same as above. |
| `--disk` | If the model is too large for your GPU(s) and CPU combined, send the remaining layers to the disk. |
| `--disk-cache-dir DISK_CACHE_DIR` | Directory to save the disk cache to. Defaults to "cache". |
| `--load-in-8bit` | Load the model with 8-bit precision (using bitsandbytes). |
| `--bf16` | Load the model with bfloat16 precision. Requires NVIDIA Ampere GPU. |
| `--no-cache` | Set `use_cache` to `False` while generating text. This reduces VRAM usage slightly, but it comes at a performance cost. |
| `--xformers` | Use xformer's memory efficient attention. This is really old and probably doesn't do anything. |
| `--sdp-attention` | Use PyTorch 2.0's SDP attention. Same as above. |
| `--trust-remote-code` | Set `trust_remote_code=True` while loading the model. Necessary for some models. |
| `--no_use_fast` | Set use_fast=False while loading the tokenizer (it's True by default). Use this if you have any problems related to use_fast. |
| `--use_flash_attention_2` | Set use_flash_attention_2=True while loading the model. |
#### Accelerate 4-bit
⚠️ Requires minimum compute of 7.0 on Windows at the moment.
| Flag | Description |
|---------------------------------------------|-------------|
| `--load-in-4bit` | Load the model with 4-bit precision (using bitsandbytes). |
| `--use_double_quant` | use_double_quant for 4-bit. |
| `--compute_dtype COMPUTE_DTYPE` | compute dtype for 4-bit. Valid options: bfloat16, float16, float32. |
| `--quant_type QUANT_TYPE` | quant_type for 4-bit. Valid options: nf4, fp4. |
#### llama.cpp
| Flag | Description |
|-------------|-------------|
| `--n_ctx N_CTX` | Size of the prompt context. |
| `--threads` | Number of threads to use. |
| `--threads-batch THREADS_BATCH` | Number of threads to use for batches/prompt processing. |
| `--no_mul_mat_q` | Disable the mulmat kernels. |
| `--n_batch` | Maximum number of prompt tokens to batch together when calling llama_eval. |
| `--no-mmap` | Prevent mmap from being used. |
| `--mlock` | Force the system to keep the model in RAM. |
| `--n-gpu-layers N_GPU_LAYERS` | Number of layers to offload to the GPU. |
| `--tensor_split TENSOR_SPLIT` | Split the model across multiple GPUs. Comma-separated list of proportions. Example: 18,17. |
| `--numa` | Activate NUMA task allocation for llama.cpp. |
| `--logits_all`| Needs to be set for perplexity evaluation to work. Otherwise, ignore it, as it makes prompt processing slower. |
| `--cache-capacity CACHE_CAPACITY` | Maximum cache capacity (llama-cpp-python). Examples: 2000MiB, 2GiB. When provided without units, bytes will be assumed. |
#### ExLlama
| Flag | Description |
|------------------|-------------|
|`--gpu-split` | Comma-separated list of VRAM (in GB) to use per GPU device for model layers. Example: 20,7,7. |
|`--max_seq_len MAX_SEQ_LEN` | Maximum sequence length. |
|`--cfg-cache` | ExLlama_HF: Create an additional cache for CFG negative prompts. Necessary to use CFG with that loader, but not necessary for CFG with base ExLlama. |
|`--no_flash_attn` | Force flash-attention to not be used. |
|`--cache_8bit` | Use 8-bit cache to save VRAM. |
#### AutoGPTQ
| Flag | Description |
|------------------|-------------|
| `--triton` | Use triton. |
| `--no_inject_fused_attention` | Disable the use of fused attention, which will use less VRAM at the cost of slower inference. |
| `--no_inject_fused_mlp` | Triton mode only: disable the use of fused MLP, which will use less VRAM at the cost of slower inference. |
| `--no_use_cuda_fp16` | This can make models faster on some systems. |
| `--desc_act` | For models that don't have a quantize_config.json, this parameter is used to define whether to set desc_act or not in BaseQuantizeConfig. |
| `--disable_exllama` | Disable ExLlama kernel, which can improve inference speed on some systems. |
#### GPTQ-for-LLaMa
| Flag | Description |
|---------------------------|-------------|
| `--wbits WBITS` | Load a pre-quantized model with specified precision in bits. 2, 3, 4 and 8 are supported. |
| `--model_type MODEL_TYPE` | Model type of pre-quantized model. Currently LLaMA, OPT, and GPT-J are supported. |
| `--groupsize GROUPSIZE` | Group size. |
| `--pre_layer PRE_LAYER [PRE_LAYER ...]` | The number of layers to allocate to the GPU. Setting this parameter enables CPU offloading for 4-bit models. For multi-gpu, write the numbers separated by spaces, eg `--pre_layer 30 60`. |
| `--checkpoint CHECKPOINT` | The path to the quantized checkpoint file. If not specified, it will be automatically detected. |
| `--monkey-patch` | Apply the monkey patch for using LoRAs with quantized models. |
#### ctransformers
| Flag | Description |
|-------------|-------------|
| `--model_type MODEL_TYPE` | Model type of pre-quantized model. Currently gpt2, gptj, gptneox, falcon, llama, mpt, starcoder (gptbigcode), dollyv2, and replit are supported. |
#### DeepSpeed
| Flag | Description |
|---------------------------------------|-------------|
| `--deepspeed` | Enable the use of DeepSpeed ZeRO-3 for inference via the Transformers integration. |
| `--nvme-offload-dir NVME_OFFLOAD_DIR` | DeepSpeed: Directory to use for ZeRO-3 NVME offloading. |
| `--local_rank LOCAL_RANK` | DeepSpeed: Optional argument for distributed setups. |
#### RWKV
| Flag | Description |
|---------------------------------|-------------|
| `--rwkv-strategy RWKV_STRATEGY` | RWKV: The strategy to use while loading the model. Examples: "cpu fp32", "cuda fp16", "cuda fp16i8". |
| `--rwkv-cuda-on` | RWKV: Compile the CUDA kernel for better performance. |
#### RoPE (for llama.cpp, ExLlama, ExLlamaV2, and transformers)
| Flag | Description |
|------------------|-------------|
| `--alpha_value ALPHA_VALUE` | Positional embeddings alpha factor for NTK RoPE scaling. Use either this or `compress_pos_emb`, not both. |
| `--rope_freq_base ROPE_FREQ_BASE` | If greater than 0, will be used instead of alpha_value. Those two are related by `rope_freq_base = 10000 * alpha_value ^ (64 / 63)`. |
| `--compress_pos_emb COMPRESS_POS_EMB` | Positional embeddings compression factor. Should be set to `(context length) / (model's original context length)`. Equal to `1/rope_freq_scale`. |
#### Gradio
| Flag | Description |
|---------------------------------------|-------------|
| `--listen` | Make the web UI reachable from your local network. |
| `--listen-port LISTEN_PORT` | The listening port that the server will use. |
| `--listen-host LISTEN_HOST` | The hostname that the server will use. |
| `--share` | Create a public URL. This is useful for running the web UI on Google Colab or similar. |
| `--auto-launch` | Open the web UI in the default browser upon launch. |
| `--gradio-auth USER:PWD` | Set Gradio authentication password in the format "username:password". Multiple credentials can also be supplied with "u1:p1,u2:p2,u3:p3". |
| `--gradio-auth-path GRADIO_AUTH_PATH` | Set the Gradio authentication file path. The file should contain one or more user:password pairs in the same format as above. |
| `--ssl-keyfile SSL_KEYFILE` | The path to the SSL certificate key file. |
| `--ssl-certfile SSL_CERTFILE` | The path to the SSL certificate cert file. |
#### API
| Flag | Description |
|---------------------------------------|-------------|
| `--api` | Enable the API extension. |
| `--public-api` | Create a public URL for the API using Cloudfare. |
| `--public-api-id PUBLIC_API_ID` | Tunnel ID for named Cloudflare Tunnel. Use together with public-api option. |
| `--api-port API_PORT` | The listening port for the API. |
| `--api-key API_KEY` | API authentication key. |
| `--admin-key ADMIN_KEY` | API authentication key for admin tasks like loading and unloading models. If not set, will be the same as --api-key. |
| `--nowebui` | Do not launch the Gradio UI. Useful for launching the API in standalone mode. |
#### Multimodal
| Flag | Description |
|---------------------------------------|-------------|
| `--multimodal-pipeline PIPELINE` | The multimodal pipeline to use. Examples: `llava-7b`, `llava-13b`. |
</details>
## Documentation
https://github.com/oobabooga/text-generation-webui/wiki
## Downloading models
Models should be placed in the folder `text-generation-webui/models`. They are usually downloaded from [Hugging Face](https://huggingface.co/models?pipeline_tag=text-generation&sort=downloads).
* GGUF models are a single file and should be placed directly into `models`. Example:
```
text-generation-webui
└── models
└── llama-2-13b-chat.Q4_K_M.gguf
```
* Other models (like 16-bit transformers models and GPTQ models) are made of several files and must be placed in a subfolder. Example:
```
text-generation-webui
├── models
│   ├── lmsys_vicuna-33b-v1.3
│   │   ├── config.json
│   │   ├── generation_config.json
│   │   ├── pytorch_model-00001-of-00007.bin
│   │   ├── pytorch_model-00002-of-00007.bin
│   │   ├── pytorch_model-00003-of-00007.bin
│   │   ├── pytorch_model-00004-of-00007.bin
│   │   ├── pytorch_model-00005-of-00007.bin
│   │   ├── pytorch_model-00006-of-00007.bin
│   │   ├── pytorch_model-00007-of-00007.bin
│   │   ├── pytorch_model.bin.index.json
│   │   ├── special_tokens_map.json
│   │   ├── tokenizer_config.json
│   │   └── tokenizer.model
```
In both cases, you can use the "Model" tab of the UI to download the model from Hugging Face automatically. It is also possible to download via the command-line with
```
python download-model.py organization/model
```
Run `python download-model.py --help` to see all the options.
## Google Colab notebook
https://colab.research.google.com/github/oobabooga/text-generation-webui/blob/main/Colab-TextGen-GPU.ipynb
## Contributing
If you would like to contribute to the project, check out the [Contributing guidelines](https://github.com/oobabooga/text-generation-webui/wiki/Contributing-guidelines).
## Community
* Subreddit: https://www.reddit.com/r/oobabooga/
* Discord: https://discord.gg/jwZCF2dPQN
## Acknowledgment & support
In August 2023, [Andreessen Horowitz](https://a16z.com/) (a16z) provided a generous grant to encourage and support my independent work on this project. I am **extremely** grateful for their trust and recognition, which will allow me to dedicate more time towards realizing the full potential of text-generation-webui.
If you find this project useful, I have a [Ko-fi page](https://ko-fi.com/oobabooga) where you can make a donation. Your support helps me continue maintaining and improving this project.
Reference in New Issue View Git Blame Copy Permalink