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+                    GNU AFFERO GENERAL PUBLIC LICENSE
+                       Version 3, 19 November 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
+ Everyone is permitted to copy and distribute verbatim copies
+ of this license document, but changing it is not allowed.
+
+                            Preamble
+
+  The GNU Affero General Public License is a free, copyleft license for
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+USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
+DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
+PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
+EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGES.
+
+  17. Interpretation of Sections 15 and 16.
+
+  If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+    <one line to give the program's name and a brief idea of what it does.>
+    Copyright (C) <year>  <name of author>
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU Affero General Public License as published
+    by the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU Affero General Public License for more details.
+
+    You should have received a copy of the GNU Affero General Public License
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+  If your software can interact with users remotely through a computer
+network, you should also make sure that it provides a way for users to
+get its source.  For example, if your program is a web application, its
+interface could display a "Source" link that leads users to an archive
+of the code.  There are many ways you could offer source, and different
+solutions will be better for different programs; see section 13 for the
+specific requirements.
+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU AGPL, see
+<https://www.gnu.org/licenses/>.

README.md

@@ -1,13 +1,393 @@
----
-title: Yetipy
-emoji: 🚀
-colorFrom: pink
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.43.2
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# 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.

convert-to-safetensors.py

@@ -0,0 +1,38 @@
+'''
+
+Converts a transformers model to safetensors format and shards it.
+
+This makes it faster to load (because of safetensors) and lowers its RAM usage
+while loading (because of sharding).
+
+Based on the original script by 81300:
+
+https://gist.github.com/81300/fe5b08bff1cba45296a829b9d6b0f303
+
+'''
+
+import argparse
+from pathlib import Path
+
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+parser = argparse.ArgumentParser(formatter_class=lambda prog: argparse.HelpFormatter(prog, max_help_position=54))
+parser.add_argument('MODEL', type=str, default=None, nargs='?', help="Path to the input model.")
+parser.add_argument('--output', type=str, default=None, help='Path to the output folder (default: models/{model_name}_safetensors).')
+parser.add_argument("--max-shard-size", type=str, default="2GB", help="Maximum size of a shard in GB or MB (default: %(default)s).")
+parser.add_argument('--bf16', action='store_true', help='Load the model with bfloat16 precision. Requires NVIDIA Ampere GPU.')
+args = parser.parse_args()
+
+if __name__ == '__main__':
+    path = Path(args.MODEL)
+    model_name = path.name
+
+    print(f"Loading {model_name}...")
+    model = AutoModelForCausalLM.from_pretrained(path, low_cpu_mem_usage=True, torch_dtype=torch.bfloat16 if args.bf16 else torch.float16)
+    tokenizer = AutoTokenizer.from_pretrained(path)
+
+    out_folder = args.output or Path(f"models/{model_name}_safetensors")
+    print(f"Saving the converted model to {out_folder} with a maximum shard size of {args.max_shard_size}...")
+    model.save_pretrained(out_folder, max_shard_size=args.max_shard_size, safe_serialization=True)
+    tokenizer.save_pretrained(out_folder)

download-model.py

@@ -0,0 +1,275 @@
+'''
+Downloads models from Hugging Face to models/username_modelname.
+
+Example:
+python download-model.py facebook/opt-1.3b
+
+'''
+
+import argparse
+import base64
+import datetime
+import hashlib
+import json
+import os
+import re
+import sys
+from pathlib import Path
+
+import requests
+import tqdm
+from requests.adapters import HTTPAdapter
+from tqdm.contrib.concurrent import thread_map
+
+
+class ModelDownloader:
+    def __init__(self, max_retries=5):
+        self.session = requests.Session()
+        if max_retries:
+            self.session.mount('https://cdn-lfs.huggingface.co', HTTPAdapter(max_retries=max_retries))
+            self.session.mount('https://huggingface.co', HTTPAdapter(max_retries=max_retries))
+        if os.getenv('HF_USER') is not None and os.getenv('HF_PASS') is not None:
+            self.session.auth = (os.getenv('HF_USER'), os.getenv('HF_PASS'))
+        if os.getenv('HF_TOKEN') is not None:
+            self.session.headers = {'authorization': f'Bearer {os.getenv("HF_TOKEN")}'}
+
+    def sanitize_model_and_branch_names(self, model, branch):
+        if model[-1] == '/':
+            model = model[:-1]
+
+        if branch is None:
+            branch = "main"
+        else:
+            pattern = re.compile(r"^[a-zA-Z0-9._-]+$")
+            if not pattern.match(branch):
+                raise ValueError(
+                    "Invalid branch name. Only alphanumeric characters, period, underscore and dash are allowed.")
+
+        return model, branch
+
+    def get_download_links_from_huggingface(self, model, branch, text_only=False, specific_file=None):
+        base = "https://huggingface.co"
+        page = f"/api/models/{model}/tree/{branch}"
+        cursor = b""
+
+        links = []
+        sha256 = []
+        classifications = []
+        has_pytorch = False
+        has_pt = False
+        has_gguf = False
+        has_ggml = False
+        has_safetensors = False
+        is_lora = False
+        while True:
+            url = f"{base}{page}" + (f"?cursor={cursor.decode()}" if cursor else "")
+            r = self.session.get(url, timeout=10)
+            r.raise_for_status()
+            content = r.content
+
+            dict = json.loads(content)
+            if len(dict) == 0:
+                break
+
+            for i in range(len(dict)):
+                fname = dict[i]['path']
+                if specific_file not in [None, ''] and fname != specific_file:
+                    continue
+
+                if not is_lora and fname.endswith(('adapter_config.json', 'adapter_model.bin')):
+                    is_lora = True
+
+                is_pytorch = re.match(r"(pytorch|adapter|gptq)_model.*\.bin", fname)
+                is_safetensors = re.match(r".*\.safetensors", fname)
+                is_pt = re.match(r".*\.pt", fname)
+                is_gguf = re.match(r'.*\.gguf', fname)
+                is_ggml = re.match(r".*ggml.*\.bin", fname)
+                is_tokenizer = re.match(r"(tokenizer|ice|spiece).*\.model", fname)
+                is_text = re.match(r".*\.(txt|json|py|md)", fname) or is_tokenizer
+                if any((is_pytorch, is_safetensors, is_pt, is_gguf, is_ggml, is_tokenizer, is_text)):
+                    if 'lfs' in dict[i]:
+                        sha256.append([fname, dict[i]['lfs']['oid']])
+
+                    if is_text:
+                        links.append(f"https://huggingface.co/{model}/resolve/{branch}/{fname}")
+                        classifications.append('text')
+                        continue
+
+                    if not text_only:
+                        links.append(f"https://huggingface.co/{model}/resolve/{branch}/{fname}")
+                        if is_safetensors:
+                            has_safetensors = True
+                            classifications.append('safetensors')
+                        elif is_pytorch:
+                            has_pytorch = True
+                            classifications.append('pytorch')
+                        elif is_pt:
+                            has_pt = True
+                            classifications.append('pt')
+                        elif is_gguf:
+                            has_gguf = True
+                            classifications.append('gguf')
+                        elif is_ggml:
+                            has_ggml = True
+                            classifications.append('ggml')
+
+            cursor = base64.b64encode(f'{{"file_name":"{dict[-1]["path"]}"}}'.encode()) + b':50'
+            cursor = base64.b64encode(cursor)
+            cursor = cursor.replace(b'=', b'%3D')
+
+        # If both pytorch and safetensors are available, download safetensors only
+        if (has_pytorch or has_pt) and has_safetensors:
+            for i in range(len(classifications) - 1, -1, -1):
+                if classifications[i] in ['pytorch', 'pt']:
+                    links.pop(i)
+
+        # If both GGML and GGUF are available, download GGUF only
+        if has_ggml and has_gguf:
+            for i in range(len(classifications) - 1, -1, -1):
+                if classifications[i] == 'ggml':
+                    links.pop(i)
+
+        return links, sha256, is_lora, ((has_ggml or has_gguf) and specific_file is not None)
+
+    def get_output_folder(self, model, branch, is_lora, is_llamacpp=False, base_folder=None):
+        if base_folder is None:
+            base_folder = 'models' if not is_lora else 'loras'
+
+        # If the model is of type GGUF or GGML, save directly in the base_folder
+        if is_llamacpp:
+            return Path(base_folder)
+
+        output_folder = f"{'_'.join(model.split('/')[-2:])}"
+        if branch != 'main':
+            output_folder += f'_{branch}'
+
+        output_folder = Path(base_folder) / output_folder
+        return output_folder
+
+    def get_single_file(self, url, output_folder, start_from_scratch=False):
+        filename = Path(url.rsplit('/', 1)[1])
+        output_path = output_folder / filename
+        headers = {}
+        mode = 'wb'
+        if output_path.exists() and not start_from_scratch:
+
+            # Check if the file has already been downloaded completely
+            r = self.session.get(url, stream=True, timeout=10)
+            total_size = int(r.headers.get('content-length', 0))
+            if output_path.stat().st_size >= total_size:
+                return
+
+            # Otherwise, resume the download from where it left off
+            headers = {'Range': f'bytes={output_path.stat().st_size}-'}
+            mode = 'ab'
+
+        with self.session.get(url, stream=True, headers=headers, timeout=10) as r:
+            r.raise_for_status()  # Do not continue the download if the request was unsuccessful
+            total_size = int(r.headers.get('content-length', 0))
+            block_size = 1024 * 1024  # 1MB
+            with open(output_path, mode) as f:
+                with tqdm.tqdm(total=total_size, unit='iB', unit_scale=True, bar_format='{l_bar}{bar}| {n_fmt:6}/{total_fmt:6} {rate_fmt:6}') as t:
+                    count = 0
+                    for data in r.iter_content(block_size):
+                        t.update(len(data))
+                        f.write(data)
+                        if total_size != 0 and self.progress_bar is not None:
+                            count += len(data)
+                            self.progress_bar(float(count) / float(total_size), f"{filename}")
+
+    def start_download_threads(self, file_list, output_folder, start_from_scratch=False, threads=1):
+        thread_map(lambda url: self.get_single_file(url, output_folder, start_from_scratch=start_from_scratch), file_list, max_workers=threads, disable=True)
+
+    def download_model_files(self, model, branch, links, sha256, output_folder, progress_bar=None, start_from_scratch=False, threads=1, specific_file=None, is_llamacpp=False):
+        self.progress_bar = progress_bar
+
+        # Create the folder and writing the metadata
+        output_folder.mkdir(parents=True, exist_ok=True)
+
+        if not is_llamacpp:
+            metadata = f'url: https://huggingface.co/{model}\n' \
+                       f'branch: {branch}\n' \
+                       f'download date: {datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")}\n'
+
+            sha256_str = '\n'.join([f'    {item[1]} {item[0]}' for item in sha256])
+            if sha256_str:
+                metadata += f'sha256sum:\n{sha256_str}'
+
+            metadata += '\n'
+            (output_folder / 'huggingface-metadata.txt').write_text(metadata)
+
+        if specific_file:
+            print(f"Downloading {specific_file} to {output_folder}")
+        else:
+            print(f"Downloading the model to {output_folder}")
+
+        self.start_download_threads(links, output_folder, start_from_scratch=start_from_scratch, threads=threads)
+
+    def check_model_files(self, model, branch, links, sha256, output_folder):
+        # Validate the checksums
+        validated = True
+        for i in range(len(sha256)):
+            fpath = (output_folder / sha256[i][0])
+
+            if not fpath.exists():
+                print(f"The following file is missing: {fpath}")
+                validated = False
+                continue
+
+            with open(output_folder / sha256[i][0], "rb") as f:
+                bytes = f.read()
+                file_hash = hashlib.sha256(bytes).hexdigest()
+                if file_hash != sha256[i][1]:
+                    print(f'Checksum failed: {sha256[i][0]}  {sha256[i][1]}')
+                    validated = False
+                else:
+                    print(f'Checksum validated: {sha256[i][0]}  {sha256[i][1]}')
+
+        if validated:
+            print('[+] Validated checksums of all model files!')
+        else:
+            print('[-] Invalid checksums. Rerun download-model.py with the --clean flag.')
+
+
+if __name__ == '__main__':
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument('MODEL', type=str, default=None, nargs='?')
+    parser.add_argument('--branch', type=str, default='main', help='Name of the Git branch to download from.')
+    parser.add_argument('--threads', type=int, default=1, help='Number of files to download simultaneously.')
+    parser.add_argument('--text-only', action='store_true', help='Only download text files (txt/json).')
+    parser.add_argument('--specific-file', type=str, default=None, help='Name of the specific file to download (if not provided, downloads all).')
+    parser.add_argument('--output', type=str, default=None, help='The folder where the model should be saved.')
+    parser.add_argument('--clean', action='store_true', help='Does not resume the previous download.')
+    parser.add_argument('--check', action='store_true', help='Validates the checksums of model files.')
+    parser.add_argument('--max-retries', type=int, default=5, help='Max retries count when get error in download time.')
+    args = parser.parse_args()
+
+    branch = args.branch
+    model = args.MODEL
+    specific_file = args.specific_file
+
+    if model is None:
+        print("Error: Please specify the model you'd like to download (e.g. 'python download-model.py facebook/opt-1.3b').")
+        sys.exit()
+
+    downloader = ModelDownloader(max_retries=args.max_retries)
+    # Clean up the model/branch names
+    try:
+        model, branch = downloader.sanitize_model_and_branch_names(model, branch)
+    except ValueError as err_branch:
+        print(f"Error: {err_branch}")
+        sys.exit()
+
+    # Get the download links from Hugging Face
+    links, sha256, is_lora, is_llamacpp = downloader.get_download_links_from_huggingface(model, branch, text_only=args.text_only, specific_file=specific_file)
+
+    # Get the output folder
+    output_folder = downloader.get_output_folder(model, branch, is_lora, is_llamacpp=is_llamacpp, base_folder=args.output)
+
+    if args.check:
+        # Check previously downloaded files
+        downloader.check_model_files(model, branch, links, sha256, output_folder)
+    else:
+        # Download files
+        downloader.download_model_files(model, branch, links, sha256, output_folder, specific_file=specific_file, threads=args.threads, is_llamacpp=is_llamacpp)

requirements.txt

@@ -0,0 +1,57 @@
+aiofiles==23.1.0
+fastapi==0.95.2
+gradio_client==0.2.5
+gradio==3.33.1
+
+accelerate==0.22.*
+colorama
+datasets
+einops
+markdown
+numpy==1.24
+optimum==1.12.0
+pandas
+peft==0.5.*
+Pillow>=9.5.0
+pyyaml
+requests
+safetensors==0.3.2
+transformers==4.32.*
+scipy
+sentencepiece
+tensorboard
+tqdm
+wandb
+
+# bitsandbytes
+bitsandbytes==0.41.1; platform_system != "Windows"
+https://github.com/jllllll/bitsandbytes-windows-webui/releases/download/wheels/bitsandbytes-0.41.1-py3-none-win_amd64.whl; platform_system == "Windows"
+
+# AutoGPTQ
+https://github.com/PanQiWei/AutoGPTQ/releases/download/v0.4.2/auto_gptq-0.4.2+cu117-cp310-cp310-win_amd64.whl; platform_system == "Windows"
+https://github.com/PanQiWei/AutoGPTQ/releases/download/v0.4.2/auto_gptq-0.4.2+cu117-cp310-cp310-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64"
+
+# ExLlama
+https://github.com/jllllll/exllama/releases/download/0.0.14/exllama-0.0.14+cu117-cp310-cp310-win_amd64.whl; platform_system == "Windows"
+https://github.com/jllllll/exllama/releases/download/0.0.14/exllama-0.0.14+cu117-cp310-cp310-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64"
+
+# llama-cpp-python without GPU support
+llama-cpp-python==0.1.83; platform_system != "Windows"
+https://github.com/abetlen/llama-cpp-python/releases/download/v0.1.83/llama_cpp_python-0.1.83-cp310-cp310-win_amd64.whl; platform_system == "Windows"
+
+# llama-cpp-python with CUDA support
+https://github.com/jllllll/llama-cpp-python-cuBLAS-wheels/releases/download/textgen-webui/llama_cpp_python_cuda-0.1.83+cu117-cp310-cp310-win_amd64.whl; platform_system == "Windows"
+https://github.com/jllllll/llama-cpp-python-cuBLAS-wheels/releases/download/textgen-webui/llama_cpp_python_cuda-0.1.83+cu117-cp310-cp310-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64"
+
+# llama-cpp-python with GGML support
+https://github.com/jllllll/llama-cpp-python-cuBLAS-wheels/releases/download/cpu/llama_cpp_python_ggml-0.1.78+cpuavx2-cp310-cp310-win_amd64.whl; platform_system == "Windows"
+https://github.com/jllllll/llama-cpp-python-cuBLAS-wheels/releases/download/cpu/llama_cpp_python_ggml-0.1.78+cpuavx2-cp310-cp310-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64"
+https://github.com/jllllll/llama-cpp-python-cuBLAS-wheels/releases/download/textgen-webui/llama_cpp_python_ggml_cuda-0.1.78+cu117-cp310-cp310-win_amd64.whl; platform_system == "Windows"
+https://github.com/jllllll/llama-cpp-python-cuBLAS-wheels/releases/download/textgen-webui/llama_cpp_python_ggml_cuda-0.1.78+cu117-cp310-cp310-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64"
+
+# GPTQ-for-LLaMa
+https://github.com/jllllll/GPTQ-for-LLaMa-CUDA/releases/download/0.1.0/gptq_for_llama-0.1.0+cu117-cp310-cp310-win_amd64.whl; platform_system == "Windows"
+https://github.com/jllllll/GPTQ-for-LLaMa-CUDA/releases/download/0.1.0/gptq_for_llama-0.1.0+cu117-cp310-cp310-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64"
+
+# ctransformers
+https://github.com/jllllll/ctransformers-cuBLAS-wheels/releases/download/AVX2/ctransformers-0.2.25+cu117-py3-none-any.whl

requirements_nocuda.txt

@@ -0,0 +1,28 @@
+aiofiles==23.1.0
+fastapi==0.95.2
+gradio_client==0.2.5
+gradio==3.33.1
+
+accelerate==0.22.*
+colorama
+datasets
+einops
+markdown
+numpy==1.24
+optimum==1.12.0
+pandas
+peft==0.5.*
+Pillow>=9.5.0
+pyyaml
+requests
+safetensors==0.3.2
+transformers==4.32.*
+scipy
+sentencepiece
+tensorboard
+tqdm
+wandb
+
+# bitsandbytes
+bitsandbytes==0.41.1; platform_system != "Windows"
+https://github.com/jllllll/bitsandbytes-windows-webui/releases/download/wheels/bitsandbytes-0.41.1-py3-none-win_amd64.whl; platform_system == "Windows"

server.py

@@ -0,0 +1,238 @@
+import os
+import warnings
+
+from modules.logging_colors import logger
+from modules.block_requests import OpenMonkeyPatch, RequestBlocker
+
+os.environ['GRADIO_ANALYTICS_ENABLED'] = 'False'
+os.environ['BITSANDBYTES_NOWELCOME'] = '1'
+warnings.filterwarnings('ignore', category=UserWarning, message='TypedStorage is deprecated')
+
+with RequestBlocker():
+    import gradio as gr
+
+import matplotlib
+matplotlib.use('Agg')  # This fixes LaTeX rendering on some systems
+
+import json
+import os
+import sys
+import time
+from functools import partial
+from pathlib import Path
+from threading import Lock
+
+import yaml
+
+import modules.extensions as extensions_module
+from modules import (
+    chat,
+    shared,
+    training,
+    ui,
+    ui_chat,
+    ui_default,
+    ui_file_saving,
+    ui_model_menu,
+    ui_notebook,
+    ui_parameters,
+    ui_session,
+    utils,
+)
+from modules.extensions import apply_extensions
+from modules.LoRA import add_lora_to_model
+from modules.models import load_model
+from modules.models_settings import (
+    get_model_settings_from_yamls,
+    update_model_parameters
+)
+from modules.utils import gradio
+
+
+def create_interface():
+
+    title = 'Text generation web UI'
+
+    # Password authentication
+    auth = []
+    if shared.args.gradio_auth:
+        auth.extend(x.strip() for x in shared.args.gradio_auth.strip('"').replace('\n', '').split(',') if x.strip())
+    if shared.args.gradio_auth_path:
+        with open(shared.args.gradio_auth_path, 'r', encoding="utf8") as file:
+            auth.extend(x.strip() for line in file for x in line.split(',') if x.strip())
+    auth = [tuple(cred.split(':')) for cred in auth]
+
+    # Import the extensions and execute their setup() functions
+    if shared.args.extensions is not None and len(shared.args.extensions) > 0:
+        extensions_module.load_extensions()
+
+    # Force some events to be triggered on page load
+    shared.persistent_interface_state.update({
+        'loader': shared.args.loader or 'Transformers',
+        'mode': shared.settings['mode'],
+        'character_menu': shared.args.character or shared.settings['character'],
+        'instruction_template': shared.settings['instruction_template'],
+        'prompt_menu-default': shared.settings['prompt-default'],
+        'prompt_menu-notebook': shared.settings['prompt-notebook'],
+    })
+
+    if Path("cache/pfp_character.png").exists():
+        Path("cache/pfp_character.png").unlink()
+
+    # css/js strings
+    css = ui.css
+    js = ui.js
+    css += apply_extensions('css')
+    js += apply_extensions('js')
+
+    # Interface state elements
+    shared.input_elements = ui.list_interface_input_elements()
+
+    with gr.Blocks(css=css, analytics_enabled=False, title=title, theme=ui.theme) as shared.gradio['interface']:
+
+        # Interface state
+        shared.gradio['interface_state'] = gr.State({k: None for k in shared.input_elements})
+
+        # Audio notification
+        if Path("notification.mp3").exists():
+            shared.gradio['audio_notification'] = gr.Audio(interactive=False, value="notification.mp3", elem_id="audio_notification", visible=False)
+
+        # Floating menus for saving/deleting files
+        ui_file_saving.create_ui()
+
+        # Temporary clipboard for saving files
+        shared.gradio['temporary_text'] = gr.Textbox(visible=False)
+
+        # Text Generation tab
+        ui_chat.create_ui()
+        ui_default.create_ui()
+        ui_notebook.create_ui()
+
+        ui_parameters.create_ui(shared.settings['preset'])  # Parameters tab
+        ui_model_menu.create_ui()  # Model tab
+        training.create_ui()  # Training tab
+        ui_session.create_ui()  # Session tab
+
+        # Generation events
+        ui_chat.create_event_handlers()
+        ui_default.create_event_handlers()
+        ui_notebook.create_event_handlers()
+
+        # Other events
+        ui_file_saving.create_event_handlers()
+        ui_parameters.create_event_handlers()
+        ui_model_menu.create_event_handlers()
+
+        # Interface launch events
+        if shared.settings['dark_theme']:
+            shared.gradio['interface'].load(lambda: None, None, None, _js="() => document.getElementsByTagName('body')[0].classList.add('dark')")
+
+        shared.gradio['interface'].load(lambda: None, None, None, _js=f"() => {{{js}}}")
+        shared.gradio['interface'].load(None, gradio('show_controls'), None, _js=f'(x) => {{{ui.show_controls_js}; toggle_controls(x)}}')
+        shared.gradio['interface'].load(partial(ui.apply_interface_values, {}, use_persistent=True), None, gradio(ui.list_interface_input_elements()), show_progress=False)
+        shared.gradio['interface'].load(chat.redraw_html, gradio(ui_chat.reload_arr), gradio('display'))
+
+        extensions_module.create_extensions_tabs()  # Extensions tabs
+        extensions_module.create_extensions_block()  # Extensions block
+
+    # Launch the interface
+    shared.gradio['interface'].queue(concurrency_count=64)
+    with OpenMonkeyPatch():
+        shared.gradio['interface'].launch(
+            prevent_thread_lock=True,
+            share=shared.args.share,
+            server_name=None if not shared.args.listen else (shared.args.listen_host or '0.0.0.0'),
+            server_port=shared.args.listen_port,
+            inbrowser=shared.args.auto_launch,
+            auth=auth or None,
+            ssl_verify=False if (shared.args.ssl_keyfile or shared.args.ssl_certfile) else True,
+            ssl_keyfile=shared.args.ssl_keyfile,
+            ssl_certfile=shared.args.ssl_certfile
+        )
+
+
+if __name__ == "__main__":
+
+    # Load custom settings
+    settings_file = None
+    if shared.args.settings is not None and Path(shared.args.settings).exists():
+        settings_file = Path(shared.args.settings)
+    elif Path('settings.yaml').exists():
+        settings_file = Path('settings.yaml')
+    elif Path('settings.json').exists():
+        settings_file = Path('settings.json')
+
+    if settings_file is not None:
+        logger.info(f"Loading settings from {settings_file}...")
+        file_contents = open(settings_file, 'r', encoding='utf-8').read()
+        new_settings = json.loads(file_contents) if settings_file.suffix == "json" else yaml.safe_load(file_contents)
+        shared.settings.update(new_settings)
+
+    # Fallback settings for models
+    shared.model_config['.*'] = {
+        'wbits': 'None',
+        'model_type': 'None',
+        'groupsize': 'None',
+        'pre_layer': 0,
+        'skip_special_tokens': shared.settings['skip_special_tokens'],
+        'custom_stopping_strings': shared.settings['custom_stopping_strings'],
+        'truncation_length': shared.settings['truncation_length'],
+        'n_gqa': 0,
+        'rms_norm_eps': 0,
+        'rope_freq_base': 0,
+    }
+
+    shared.model_config.move_to_end('.*', last=False)  # Move to the beginning
+
+    # Activate the extensions listed on settings.yaml
+    extensions_module.available_extensions = utils.get_available_extensions()
+    for extension in shared.settings['default_extensions']:
+        shared.args.extensions = shared.args.extensions or []
+        if extension not in shared.args.extensions:
+            shared.args.extensions.append(extension)
+
+    available_models = utils.get_available_models()
+
+    # Model defined through --model
+    if shared.args.model is not None:
+        shared.model_name = shared.args.model
+
+    # Select the model from a command-line menu
+    elif shared.args.model_menu:
+        if len(available_models) == 0:
+            logger.error('No models are available! Please download at least one.')
+            sys.exit(0)
+        else:
+            print('The following models are available:\n')
+            for i, model in enumerate(available_models):
+                print(f'{i+1}. {model}')
+
+            print(f'\nWhich one do you want to load? 1-{len(available_models)}\n')
+            i = int(input()) - 1
+            print()
+
+        shared.model_name = available_models[i]
+
+    # If any model has been selected, load it
+    if shared.model_name != 'None':
+        model_settings = get_model_settings_from_yamls(shared.model_name)
+        shared.settings.update(model_settings)  # hijacking the interface defaults
+        update_model_parameters(model_settings, initial=True)  # hijacking the command-line arguments
+
+        # Load the model
+        shared.model, shared.tokenizer = load_model(shared.model_name)
+        if shared.args.lora:
+            add_lora_to_model(shared.args.lora)
+
+    shared.generation_lock = Lock()
+
+    # Launch the web UI
+    create_interface()
+    while True:
+        time.sleep(0.5)
+        if shared.need_restart:
+            shared.need_restart = False
+            time.sleep(0.5)
+            shared.gradio['interface'].close()
+            time.sleep(0.5)
+            create_interface()

settings-template.yaml

@@ -0,0 +1,36 @@
+dark_theme: true
+show_controls: true
+start_with: ''
+mode: chat
+chat_style: TheEncrypted777
+character: None
+prompt-default: QA
+prompt-notebook: QA
+preset: simple-1
+max_new_tokens: 200
+max_new_tokens_min: 1
+max_new_tokens_max: 4096
+seed: -1
+negative_prompt: ''
+truncation_length: 2048
+truncation_length_min: 0
+truncation_length_max: 16384
+custom_stopping_strings: ''
+auto_max_new_tokens: false
+max_tokens_second: 0
+ban_eos_token: false
+add_bos_token: true
+skip_special_tokens: true
+stream: true
+name1: You
+name2: Assistant
+context: This is a conversation with your Assistant. It is a computer program designed to help you with various tasks such as answering questions, providing recommendations, and helping with decision making. You can ask it anything you want and it will do its best to give you accurate and relevant information.
+greeting: ''
+instruction_template: Alpaca
+chat-instruct_command: |-
+  Continue the chat dialogue below. Write a single reply for the character "<|character|>".
+
+  <|prompt|>
+autoload_model: false
+default_extensions:
+- gallery