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README.md

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-# 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), [ExLlama](https://github.com/turboderp/exllama), [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ), [GPTQ-for-LLaMa](https://github.com/qwopqwop200/GPTQ-for-LLaMa), [ctransformers](https://github.com/marella/ctransformers)
-* Dropdown menu for quickly switching between different models
-* LoRA: load and unload LoRAs on the fly, train a new LoRA using QLoRA
-* Precise instruction templates for chat mode, including Llama-2-chat, Alpaca, Vicuna, WizardLM, StableLM, and many others
-* 4-bit, 8-bit, and CPU inference through the transformers library
-* Use llama.cpp models with transformers samplers (`llamacpp_HF` loader)
-* [Multimodal pipelines, including LLaVA and MiniGPT-4](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/multimodal)
-* [Extensions framework](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)
-* 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
-
-### One-click installers
-
-| Windows | Linux | macOS | WSL |
-|--------|--------|--------|--------|
-| [oobabooga-windows.zip](https://github.com/oobabooga/text-generation-webui/releases/download/installers/oobabooga_windows.zip) | [oobabooga-linux.zip](https://github.com/oobabooga/text-generation-webui/releases/download/installers/oobabooga_linux.zip) |[oobabooga-macos.zip](https://github.com/oobabooga/text-generation-webui/releases/download/installers/oobabooga_macos.zip) | [oobabooga-wsl.zip](https://github.com/oobabooga/text-generation-webui/releases/download/installers/oobabooga_wsl.zip) |
-
-Just download the zip above, extract it, and double-click on "start". The web UI and all its dependencies will be installed in the same folder.
-
-* The source codes and more information can be found here: https://github.com/oobabooga/one-click-installers
-* There is no need to run the installers as admin.
-* Huge thanks to [@jllllll](https://github.com/jllllll), [@ClayShoaf](https://github.com/ClayShoaf), and [@xNul](https://github.com/xNul) for their contributions to these installers.
-
-### 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=3.10.9
-conda activate textgen
-```
-
-#### 2. Install Pytorch
-
-| System | GPU | Command |
-|--------|---------|---------|
-| Linux/WSL | NVIDIA | `pip3 install torch torchvision torchaudio` |
-| 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/rocm5.4.2` |
-| MacOS + MPS | Any | `pip3 install torch torchvision torchaudio` |
-| Windows | NVIDIA | `pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu117` |
-| Windows | CPU only | `pip3 install torch torchvision torchaudio` |
-
-The up-to-date commands can be found here: https://pytorch.org/get-started/locally/. 
-
-#### 3. Install the web UI
-
-```
-git clone https://github.com/oobabooga/text-generation-webui
-cd text-generation-webui
-pip install -r requirements.txt
-```
-
-#### AMD, Metal, Intel Arc, and CPUs without AVCX2
-
-1) Replace the last command above with
-
-```
-pip install -r requirements_nocuda.txt
-```
-
-2) Manually install llama-cpp-python using the appropriate command for your hardware: [Installation from PyPI](https://github.com/abetlen/llama-cpp-python#installation-from-pypi).
-
-3) AMD: Manually install AutoGPTQ: [Installation](https://github.com/PanQiWei/AutoGPTQ#installation).
-
-4) AMD: 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):
-
-```
-cd text-generation-webui
-mkdir repositories
-cd repositories
-git clone https://github.com/turboderp/exllama
-```
-
-#### bitsandbytes on older NVIDIA GPUs
-
-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`
-
-### Alternative: Docker
-
-```
-ln -s docker/{Dockerfile,docker-compose.yml,.dockerignore} .
-cp docker/.env.example .env
-# Edit .env and set TORCH_CUDA_ARCH_LIST based on your GPU model
-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/blob/main/docs/Docker.md) 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.txt --upgrade
-```
-
-## Downloading models
-
-Models should be placed in the `text-generation-webui/models` folder. They are usually downloaded from [Hugging Face](https://huggingface.co/models?pipeline_tag=text-generation&sort=downloads).
-
-* Transformers or 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
-```
-
-* GGML/GGUF models are a single file and should be placed directly into `models`. Example:
-
-```
-text-generation-webui
-├── models
-│   ├── llama-13b.ggmlv3.q4_K_M.bin
-```
-
-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` (use `--help` to see all the options).
-
-#### GPT-4chan
-
-<details>
-<summary>
-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)
-* Direct download: [16-bit](https://theswissbay.ch/pdf/_notpdf_/gpt4chan_model_float16/) / [32-bit](https://theswissbay.ch/pdf/_notpdf_/gpt4chan_model/)
-
-The 32-bit version is only relevant if you intend to run the model in CPU mode. Otherwise, you should use the 16-bit version.
-
-After downloading the model, follow these steps:
-
-1. Place the files under `models/gpt4chan_model_float16` or `models/gpt4chan_model`.
-2. Place GPT-J 6B's config.json file in that same folder: [config.json](https://huggingface.co/EleutherAI/gpt-j-6B/raw/main/config.json).
-3. Download GPT-J 6B's tokenizer files (they will be automatically detected when you attempt to load GPT-4chan):
-
-```
-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:
-
-![Image3](https://github.com/oobabooga/screenshots/raw/main/gpt4chan.png)
-
-</details>
-
-## Starting the web UI
-
-    conda activate textgen
-    cd text-generation-webui
-    python server.py
-
-Then browse to 
-
-`http://localhost:7860/?__theme=dark`
-
-Optionally, you can use the following command-line flags:
-
-#### 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 highly experimental. |
-| `--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. |
-
-#### Model loader
-
-| Flag                                       | Description |
-|--------------------------------------------|-------------|
-| `--loader LOADER`                          | Choose the model loader manually, otherwise, it will get autodetected. Valid options: transformers, autogptq, gptq-for-llama, exllama, exllama_hf, llamacpp, rwkv, ctransformers |
-
-#### 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 the VRAM usage a bit with a performance cost. |
-| `--xformers`                                | Use xformer's memory efficient attention. This should increase your tokens/s. |
-| `--sdp-attention`                           | Use torch 2.0's sdp attention. |
-| `--trust-remote-code`                       | Set trust_remote_code=True while loading a model. Necessary for ChatGLM and Falcon. |
-
-#### 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). |
-| `--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. |
-| `--use_double_quant`                        | use_double_quant for 4-bit. |
-
-#### GGML/GGUF (for llama.cpp and ctransformers)
-
-| Flag        | Description |
-|-------------|-------------|
-| `--threads` | Number of threads to use. |
-| `--n_batch` | Maximum number of prompt tokens to batch together when calling llama_eval. |
-| `--n-gpu-layers N_GPU_LAYERS` | Number of layers to offload to the GPU. Only works if llama-cpp-python was compiled with BLAS. Set this to 1000000000 to offload all layers to the GPU. |
-| `--n_ctx N_CTX` | Size of the prompt context. |
-
-#### llama.cpp
-
-| Flag          | Description |
-|---------------|---------------|
-| `--no-mmap`   | Prevent mmap from being used. |
-| `--mlock`     | Force the system to keep the model in RAM. |
-| `--mul_mat_q` | Activate new mulmat kernels. |
-| `--cache-capacity CACHE_CAPACITY`   | Maximum cache capacity. Examples: 2000MiB, 2GiB. When provided without units, bytes will be assumed. |
-| `--tensor_split TENSOR_SPLIT`  | Split the model across multiple GPUs, comma-separated list of proportions, e.g. 18,17 |
-| `--llama_cpp_seed SEED`        | Seed for llama-cpp models. Default 0 (random). |
-| `--n_gqa N_GQA`                | GGML only (not used by GGUF): Grouped-Query Attention. Must be 8 for llama-2 70b. |
-| `--rms_norm_eps RMS_NORM_EPS`  | GGML only (not used by GGUF): 5e-6 is a good value for llama-2 models. |
-| `--cpu`                        | Use the CPU version of llama-cpp-python instead of the GPU-accelerated version. |
-|`--cfg-cache`                   | llamacpp_HF: Create an additional cache for CFG negative prompts. |
-
-#### 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. |
-
-#### 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. |
-
-#### ExLlama
-
-| Flag             | Description |
-|------------------|-------------|
-|`--gpu-split`     | Comma-separated list of VRAM (in GB) to use per GPU device for model layers, e.g. `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. |
-
-#### 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.
-
-#### 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, 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-host LISTEN_HOST`           | The hostname that the server will use. |
-| `--listen-port LISTEN_PORT`           | The listening port 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 like "username:password"; or comma-delimit multiple like "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 this format: "u1:p1,u2:p2,u3:p3" |
-| `--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-blocking-port BLOCKING_PORT`   | The listening port for the blocking API. |
-| `--api-streaming-port STREAMING_PORT` | The listening port for the streaming API. |
-
-#### Multimodal
-
-| Flag                                  | Description |
-|---------------------------------------|-------------|
-| `--multimodal-pipeline PIPELINE`      | The multimodal pipeline to use. Examples: `llava-7b`, `llava-13b`. |
-
-## Presets
-
-Inference settings presets can be created under `presets/` as yaml files. These files are detected automatically at startup.
-
-The presets that are included by default are the result of a contest that received 7215 votes. More details can be found [here](https://github.com/oobabooga/oobabooga.github.io/blob/main/arena/results.md).
-
-## 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
-
-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.
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