Delete text-generation-webui-snapshot-2023-10-15

text-generation-webui-snapshot-2023-10-15/.github/FUNDING.yml

@@ -1 +0,0 @@
-ko_fi: oobabooga

text-generation-webui-snapshot-2023-10-15/.github/ISSUE_TEMPLATE/bug_report_template.yml

@@ -1,53 +0,0 @@
-name: "Bug report"
-description: Report a bug
-labels: [ "bug" ]
-body:
-  - type: markdown
-    attributes:
-      value: |
-        Thanks for taking the time to fill out this bug report!
-  - type: textarea
-    id: bug-description
-    attributes:
-      label: Describe the bug
-      description: A clear and concise description of what the bug is.
-      placeholder: Bug description
-    validations:
-      required: true
-  - type: checkboxes
-    attributes:
-      label: Is there an existing issue for this?
-      description: Please search to see if an issue already exists for the issue you encountered.
-      options:
-        - label: I have searched the existing issues
-          required: true
-  - type: textarea
-    id: reproduction
-    attributes:
-      label: Reproduction
-      description: Please provide the steps necessary to reproduce your issue.
-      placeholder: Reproduction
-    validations:
-      required: true
-  - type: textarea
-    id: screenshot
-    attributes:
-      label: Screenshot
-      description: "If possible, please include screenshot(s) so that we can understand what the issue is."
-  - type: textarea
-    id: logs
-    attributes:
-      label: Logs
-      description: "Please include the full stacktrace of the errors you get in the command-line (if any)."
-      render: shell
-    validations:
-      required: true
-  - type: textarea
-    id: system-info
-    attributes:
-      label: System Info
-      description: "Please share your system info with us: operating system, GPU brand, and GPU model. If you are using a Google Colab notebook, mention that instead."
-      render: shell
-      placeholder: 
-    validations:
-      required: true

text-generation-webui-snapshot-2023-10-15/.github/ISSUE_TEMPLATE/feature_request.md

@@ -1,16 +0,0 @@
----
-name: Feature request
-about: Suggest an improvement or new feature for the web UI
-title: ''
-labels: 'enhancement'
-assignees: ''
-
----
-
-**Description**
-
-A clear and concise description of what you want to be implemented.
-
-**Additional Context**
-
-If applicable, please provide any extra information, external links, or screenshots that could be useful.

text-generation-webui-snapshot-2023-10-15/.github/dependabot.yml

@@ -1,11 +0,0 @@
-# To get started with Dependabot version updates, you'll need to specify which
-# package ecosystems to update and where the package manifests are located.
-# Please see the documentation for all configuration options:
-# https://docs.github.com/github/administering-a-repository/configuration-options-for-dependency-updates
-
-version: 2
-updates:
-  - package-ecosystem: "pip" # See documentation for possible values
-    directory: "/" # Location of package manifests
-    schedule:
-      interval: "weekly"

text-generation-webui-snapshot-2023-10-15/.github/pull_request_template.md

@@ -1,3 +0,0 @@
-## Checklist:
-
-- [ ] I have read the [Contributing guidelines](https://github.com/oobabooga/text-generation-webui/wiki/Contributing-guidelines).

text-generation-webui-snapshot-2023-10-15/.github/workflows/auto-release.yml

@@ -1,28 +0,0 @@
-name: Weekly Snapshot Release
-on:
-  schedule:
-    - cron: '15 20 * * 0'
-  workflow_dispatch: {}
-
-jobs:
-  create_release:
-    runs-on: ubuntu-latest
-    steps:
-      - name: Checkout code
-        uses: actions/checkout@v2
-
-      - name: Set snapshot tag
-        id: set_snapshot_tag
-        run: echo ::set-output name=tag::snapshot-$(date +'%Y-%m-%d')
-
-      - name: Create release
-        id: create_release
-        uses: softprops/action-gh-release@v1
-        with:
-          generate_release_notes: true
-          tag_name: ${{ steps.set_snapshot_tag.outputs.tag }}
-          name: ${{ steps.set_snapshot_tag.outputs.tag }}
-          draft: false
-          prerelease: false
-        env:
-          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}

text-generation-webui-snapshot-2023-10-15/.github/workflows/stale.yml

@@ -1,22 +0,0 @@
-name: Close inactive issues
-on:
-  schedule:
-    - cron: "10 23 * * *"
-
-jobs:
-  close-issues:
-    runs-on: ubuntu-latest
-    permissions:
-      issues: write
-      pull-requests: write
-    steps:
-      - uses: actions/stale@v5
-        with:
-          stale-issue-message: ""
-          close-issue-message: "This issue has been closed due to inactivity for 6 weeks. If you believe it is still relevant, please leave a comment below. You can tag a developer in your comment."
-          days-before-issue-stale: 42
-          days-before-issue-close: 0
-          stale-issue-label: "stale"
-          days-before-pr-stale: -1
-          days-before-pr-close: -1
-          repo-token: ${{ secrets.GITHUB_TOKEN }}

text-generation-webui-snapshot-2023-10-15/.gitignore

@@ -1,38 +0,0 @@
-cache
-characters
-training/datasets
-extensions/silero_tts/outputs
-extensions/elevenlabs_tts/outputs
-extensions/sd_api_pictures/outputs
-extensions/multimodal/pipelines
-logs
-loras
-models
-presets
-repositories
-softprompts
-torch-dumps
-*pycache*
-*/*pycache*
-*/*/pycache*
-venv/
-.venv/
-.vscode
-.idea/
-*.bak
-*.ipynb
-*.log
-
-settings.json
-settings.yaml
-notification.mp3
-img_bot*
-img_me*
-prompts/[0-9]*
-models/config-user.yaml
-
-.DS_Store
-Thumbs.db
-.chroma
-installer_files
-/CMD_FLAGS.txt

text-generation-webui-snapshot-2023-10-15/CMD_FLAGS.txt

@@ -1,3 +0,0 @@
-# Only used by the one-click installer.
-# Example:
-# --listen --api

text-generation-webui-snapshot-2023-10-15/LICENSE

@@ -1,661 +0,0 @@
-                    GNU AFFERO GENERAL PUBLIC LICENSE
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text-generation-webui-snapshot-2023-10-15/README.md

@@ -1,421 +0,0 @@
-# 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), [ExLlamaV2](https://github.com/turboderp/exllamav2), [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ), [GPTQ-for-LLaMa](https://github.com/qwopqwop200/GPTQ-for-LLaMa), [CTransformers](https://github.com/marella/ctransformers), [AutoAWQ](https://github.com/casper-hansen/AutoAWQ)
-* 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
-
-1) Clone or download 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) Have fun!
-
-#### How it works
-
-The script creates a folder called `installer_files` where it sets up a Conda environment using Miniconda. The installation is self-contained: if you want to reinstall, just delete `installer_files` and run the start script again.
-
-To launch the webui in the future after it is already installed, run the same `start` script. 
-
-#### Getting updates
-
-Run `update_linux.sh`, `update_windows.bat`, `update_macos.sh`, or `update_wsl.bat`.
-
-#### 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`.
-
-#### Defining command-line flags
-
-To define persistent command-line flags like `--listen` or `--api`, edit the `CMD_FLAGS.txt` file with a text editor and add them there. Flags can also be provided directly to the start scripts, for instance, `./start-linux.sh --listen`.
-
-#### Other info
-
-* There is no need to run any of those scripts as admin/root.
-* For additional instructions about AMD setup, WSL setup, and nvcc installation, consult [this page](https://github.com/oobabooga/text-generation-webui/blob/main/docs/One-Click-Installers.md).
-* 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`, `LAUNCH_AFTER_INSTALL`, and `INSTALL_EXTENSIONS` environment variables. For instance: `GPU_CHOICE=A 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=3.10
-conda activate textgen
-```
-
-#### 2. Install Pytorch
-
-| System | GPU | Command |
-|--------|---------|---------|
-| Linux/WSL | NVIDIA | `pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118` |
-| 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/cu118` |
-| 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 may also need to manually install the CUDA runtime libraries:
-
-```
-conda install -y -c "nvidia/label/cuda-11.8.0" cuda-runtime
-```
-
-#### 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 AVX2
-
-1) Replace the last command above with
-
-```
-pip install -r requirements_nowheels.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) Do the same for CTransformers: [Installation](https://github.com/marella/ctransformers#installation).
-
-4) AMD: Manually install AutoGPTQ: [Installation](https://github.com/PanQiWei/AutoGPTQ#installation).
-
-5) 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
-git clone https://github.com/turboderp/exllama repositories/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
-```
-
-* 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
-```
-
-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. |
-| `--chat-buttons`                           | Show buttons on chat tab instead of hover menu. |
-
-#### 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. |
-| `--use_fast`                                | Set use_fast=True while loading a tokenizer. |
-
-#### 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. |
-
-#### GGUF (for llama.cpp and ctransformers)
-
-| Flag        | Description |
-|-------------|-------------|
-| `--threads` | Number of threads to use. |
-| `--threads-batch THREADS_BATCH` | Number of threads to use for batches/prompt processing. |
-| `--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 |
-|---------------|---------------|
-| `--mul_mat_q` | Activate new mulmat kernels. |
-| `--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). |
-| `--cache-capacity CACHE_CAPACITY`   | Maximum cache capacity. Examples: 2000MiB, 2GiB. When provided without units, bytes will be assumed. |
-|`--cfg-cache`                        | llamacpp_HF: Create an additional cache for CFG negative prompts. |
-| `--no-mmap`   | Prevent mmap from being used. |
-| `--mlock`     | Force the system to keep the model in RAM. |
-| `--numa`      | Activate NUMA task allocation for llama.cpp |
-| `--cpu`       | Use the CPU version of llama-cpp-python instead of the GPU-accelerated version. |
-
-#### 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, 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-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.

text-generation-webui-snapshot-2023-10-15/api-examples/api-example-chat-stream.py

@@ -1,112 +0,0 @@
-import asyncio
-import html
-import json
-import sys
-
-try:
-    import websockets
-except ImportError:
-    print("Websockets package not found. Make sure it's installed.")
-
-# For local streaming, the websockets are hosted without ssl - ws://
-HOST = 'localhost:5005'
-URI = f'ws://{HOST}/api/v1/chat-stream'
-
-# For reverse-proxied streaming, the remote will likely host with ssl - wss://
-# URI = 'wss://your-uri-here.trycloudflare.com/api/v1/stream'
-
-
-async def run(user_input, history):
-    # Note: the selected defaults change from time to time.
-    request = {
-        'user_input': user_input,
-        'max_new_tokens': 250,
-        'auto_max_new_tokens': False,
-        'max_tokens_second': 0,
-        'history': history,
-        'mode': 'instruct',  # Valid options: 'chat', 'chat-instruct', 'instruct'
-        'character': 'Example',
-        'instruction_template': 'Vicuna-v1.1',  # Will get autodetected if unset
-        'your_name': 'You',
-        # 'name1': 'name of user', # Optional
-        # 'name2': 'name of character', # Optional
-        # 'context': 'character context', # Optional
-        # 'greeting': 'greeting', # Optional
-        # 'name1_instruct': 'You', # Optional
-        # 'name2_instruct': 'Assistant', # Optional
-        # 'context_instruct': 'context_instruct', # Optional
-        # 'turn_template': 'turn_template', # Optional
-        'regenerate': False,
-        '_continue': False,
-        'chat_instruct_command': 'Continue the chat dialogue below. Write a single reply for the character "<|character|>".\n\n<|prompt|>',
-
-        # Generation params. If 'preset' is set to different than 'None', the values
-        # in presets/preset-name.yaml are used instead of the individual numbers.
-        'preset': 'None',
-        'do_sample': True,
-        'temperature': 0.7,
-        'top_p': 0.1,
-        'typical_p': 1,
-        'epsilon_cutoff': 0,  # In units of 1e-4
-        'eta_cutoff': 0,  # In units of 1e-4
-        'tfs': 1,
-        'top_a': 0,
-        'repetition_penalty': 1.18,
-        'repetition_penalty_range': 0,
-        'top_k': 40,
-        'min_length': 0,
-        'no_repeat_ngram_size': 0,
-        'num_beams': 1,
-        'penalty_alpha': 0,
-        'length_penalty': 1,
-        'early_stopping': False,
-        'mirostat_mode': 0,
-        'mirostat_tau': 5,
-        'mirostat_eta': 0.1,
-        'grammar_string': '',
-        'guidance_scale': 1,
-        'negative_prompt': '',
-
-        'seed': -1,
-        'add_bos_token': True,
-        'truncation_length': 2048,
-        'ban_eos_token': False,
-        'custom_token_bans': '',
-        'skip_special_tokens': True,
-        'stopping_strings': []
-    }
-
-    async with websockets.connect(URI, ping_interval=None) as websocket:
-        await websocket.send(json.dumps(request))
-
-        while True:
-            incoming_data = await websocket.recv()
-            incoming_data = json.loads(incoming_data)
-
-            match incoming_data['event']:
-                case 'text_stream':
-                    yield incoming_data['history']
-                case 'stream_end':
-                    return
-
-
-async def print_response_stream(user_input, history):
-    cur_len = 0
-    async for new_history in run(user_input, history):
-        cur_message = new_history['visible'][-1][1][cur_len:]
-        cur_len += len(cur_message)
-        print(html.unescape(cur_message), end='')
-        sys.stdout.flush()  # If we don't flush, we won't see tokens in realtime.
-
-
-if __name__ == '__main__':
-    user_input = "Please give me a step-by-step guide on how to plant a tree in my backyard."
-
-    # Basic example
-    history = {'internal': [], 'visible': []}
-
-    # "Continue" example. Make sure to set '_continue' to True above
-    # arr = [user_input, 'Surely, here is']
-    # history = {'internal': [arr], 'visible': [arr]}
-
-    asyncio.run(print_response_stream(user_input, history))

text-generation-webui-snapshot-2023-10-15/api-examples/api-example-chat.py

@@ -1,92 +0,0 @@
-import html
-import json
-
-import requests
-
-# For local streaming, the websockets are hosted without ssl - http://
-HOST = 'localhost:5000'
-URI = f'http://{HOST}/api/v1/chat'
-
-# For reverse-proxied streaming, the remote will likely host with ssl - https://
-# URI = 'https://your-uri-here.trycloudflare.com/api/v1/chat'
-
-
-def run(user_input, history):
-    request = {
-        'user_input': user_input,
-        'max_new_tokens': 250,
-        'auto_max_new_tokens': False,
-        'max_tokens_second': 0,
-        'history': history,
-        'mode': 'instruct',  # Valid options: 'chat', 'chat-instruct', 'instruct'
-        'character': 'Example',
-        'instruction_template': 'Vicuna-v1.1',  # Will get autodetected if unset
-        'your_name': 'You',
-        # 'name1': 'name of user', # Optional
-        # 'name2': 'name of character', # Optional
-        # 'context': 'character context', # Optional
-        # 'greeting': 'greeting', # Optional
-        # 'name1_instruct': 'You', # Optional
-        # 'name2_instruct': 'Assistant', # Optional
-        # 'context_instruct': 'context_instruct', # Optional
-        # 'turn_template': 'turn_template', # Optional
-        'regenerate': False,
-        '_continue': False,
-        'chat_instruct_command': 'Continue the chat dialogue below. Write a single reply for the character "<|character|>".\n\n<|prompt|>',
-
-        # Generation params. If 'preset' is set to different than 'None', the values
-        # in presets/preset-name.yaml are used instead of the individual numbers.
-        'preset': 'None',
-        'do_sample': True,
-        'temperature': 0.7,
-        'top_p': 0.1,
-        'typical_p': 1,
-        'epsilon_cutoff': 0,  # In units of 1e-4
-        'eta_cutoff': 0,  # In units of 1e-4
-        'tfs': 1,
-        'top_a': 0,
-        'repetition_penalty': 1.18,
-        'repetition_penalty_range': 0,
-        'top_k': 40,
-        'min_length': 0,
-        'no_repeat_ngram_size': 0,
-        'num_beams': 1,
-        'penalty_alpha': 0,
-        'length_penalty': 1,
-        'early_stopping': False,
-        'mirostat_mode': 0,
-        'mirostat_tau': 5,
-        'mirostat_eta': 0.1,
-        'grammar_string': '',
-        'guidance_scale': 1,
-        'negative_prompt': '',
-
-        'seed': -1,
-        'add_bos_token': True,
-        'truncation_length': 2048,
-        'ban_eos_token': False,
-        'custom_token_bans': '',
-        'skip_special_tokens': True,
-        'stopping_strings': []
-    }
-
-    response = requests.post(URI, json=request)
-
-    if response.status_code == 200:
-        result = response.json()['results'][0]['history']
-        print(json.dumps(result, indent=4))
-        print()
-        print(html.unescape(result['visible'][-1][1]))
-
-
-if __name__ == '__main__':
-    user_input = "Please give me a step-by-step guide on how to plant a tree in my backyard."
-
-    # Basic example
-    history = {'internal': [], 'visible': []}
-
-    # "Continue" example. Make sure to set '_continue' to True above
-    # arr = [user_input, 'Surely, here is']
-    # history = {'internal': [arr], 'visible': [arr]}
-
-    run(user_input, history)

text-generation-webui-snapshot-2023-10-15/api-examples/api-example-model.py

@@ -1,176 +0,0 @@
-#!/usr/bin/env python3
-
-import requests
-
-HOST = '0.0.0.0:5000'
-
-
-def generate(prompt, tokens=200):
-    request = {'prompt': prompt, 'max_new_tokens': tokens}
-    response = requests.post(f'http://{HOST}/api/v1/generate', json=request)
-
-    if response.status_code == 200:
-        return response.json()['results'][0]['text']
-
-
-def model_api(request):
-    response = requests.post(f'http://{HOST}/api/v1/model', json=request)
-    return response.json()
-
-
-# print some common settings
-def print_basic_model_info(response):
-    basic_settings = ['truncation_length', 'instruction_template']
-    print("Model: ", response['result']['model_name'])
-    print("Lora(s): ", response['result']['lora_names'])
-    for setting in basic_settings:
-        print(setting, "=", response['result']['shared.settings'][setting])
-
-
-# model info
-def model_info():
-    response = model_api({'action': 'info'})
-    print_basic_model_info(response)
-
-
-# simple loader
-def model_load(model_name):
-    return model_api({'action': 'load', 'model_name': model_name})
-
-
-# complex loader
-def complex_model_load(model):
-
-    def guess_groupsize(model_name):
-        if '1024g' in model_name:
-            return 1024
-        elif '128g' in model_name:
-            return 128
-        elif '32g' in model_name:
-            return 32
-        else:
-            return -1
-
-    req = {
-        'action': 'load',
-        'model_name': model,
-        'args': {
-            'loader': 'AutoGPTQ',
-
-            'bf16': False,
-            'load_in_8bit': False,
-            'groupsize': 0,
-            'wbits': 0,
-
-            # llama.cpp
-            'threads': 0,
-            'n_batch': 512,
-            'no_mmap': False,
-            'mlock': False,
-            'cache_capacity': None,
-            'n_gpu_layers': 0,
-            'n_ctx': 2048,
-
-            # RWKV
-            'rwkv_strategy': None,
-            'rwkv_cuda_on': False,
-
-            # b&b 4-bit
-            # 'load_in_4bit': False,
-            # 'compute_dtype': 'float16',
-            # 'quant_type': 'nf4',
-            # 'use_double_quant': False,
-
-            # "cpu": false,
-            # "auto_devices": false,
-            # "gpu_memory": null,
-            # "cpu_memory": null,
-            # "disk": false,
-            # "disk_cache_dir": "cache",
-        },
-    }
-
-    model = model.lower()
-
-    if '4bit' in model or 'gptq' in model or 'int4' in model:
-        req['args']['wbits'] = 4
-        req['args']['groupsize'] = guess_groupsize(model)
-    elif '3bit' in model:
-        req['args']['wbits'] = 3
-        req['args']['groupsize'] = guess_groupsize(model)
-    else:
-        req['args']['gptq_for_llama'] = False
-
-    if '8bit' in model:
-        req['args']['load_in_8bit'] = True
-    elif '-hf' in model or 'fp16' in model:
-        if '7b' in model:
-            req['args']['bf16'] = True  # for 24GB
-        elif '13b' in model:
-            req['args']['load_in_8bit'] = True  # for 24GB
-    elif 'gguf' in model:
-        # req['args']['threads'] = 16
-        if '7b' in model:
-            req['args']['n_gpu_layers'] = 100
-        elif '13b' in model:
-            req['args']['n_gpu_layers'] = 100
-        elif '30b' in model or '33b' in model:
-            req['args']['n_gpu_layers'] = 59  # 24GB
-        elif '65b' in model:
-            req['args']['n_gpu_layers'] = 42  # 24GB
-    elif 'rwkv' in model:
-        req['args']['rwkv_cuda_on'] = True
-        if '14b' in model:
-            req['args']['rwkv_strategy'] = 'cuda f16i8'  # 24GB
-        else:
-            req['args']['rwkv_strategy'] = 'cuda f16'  # 24GB
-
-    return model_api(req)
-
-
-if __name__ == '__main__':
-    for model in model_api({'action': 'list'})['result']:
-        try:
-            resp = complex_model_load(model)
-
-            if 'error' in resp:
-                print(f"❌ {model} FAIL Error: {resp['error']['message']}")
-                continue
-            else:
-                print_basic_model_info(resp)
-
-            ans = generate("0,1,1,2,3,5,8,13,", tokens=2)
-
-            if '21' in ans:
-                print(f"✅ {model} PASS ({ans})")
-            else:
-                print(f"❌ {model} FAIL ({ans})")
-
-        except Exception as e:
-            print(f"❌ {model} FAIL Exception: {repr(e)}")
-
-
-# 0,1,1,2,3,5,8,13, is the fibonacci sequence, the next number is 21.
-# Some results below.
-""" $ ./model-api-example.py
-Model:  4bit_gpt4-x-alpaca-13b-native-4bit-128g-cuda
-Lora(s):  []
-truncation_length = 2048
-instruction_template = Alpaca
-✅ 4bit_gpt4-x-alpaca-13b-native-4bit-128g-cuda PASS (21)
-Model:  4bit_WizardLM-13B-Uncensored-4bit-128g
-Lora(s):  []
-truncation_length = 2048
-instruction_template = WizardLM
-✅ 4bit_WizardLM-13B-Uncensored-4bit-128g PASS (21)
-Model:  Aeala_VicUnlocked-alpaca-30b-4bit
-Lora(s):  []
-truncation_length = 2048
-instruction_template = Alpaca
-✅ Aeala_VicUnlocked-alpaca-30b-4bit PASS (21)
-Model:  alpaca-30b-4bit
-Lora(s):  []
-truncation_length = 2048
-instruction_template = Alpaca
-✅ alpaca-30b-4bit PASS (21)
-"""

text-generation-webui-snapshot-2023-10-15/api-examples/api-example-stream.py

@@ -1,86 +0,0 @@
-import asyncio
-import json
-import sys
-
-try:
-    import websockets
-except ImportError:
-    print("Websockets package not found. Make sure it's installed.")
-
-# For local streaming, the websockets are hosted without ssl - ws://
-HOST = 'localhost:5005'
-URI = f'ws://{HOST}/api/v1/stream'
-
-# For reverse-proxied streaming, the remote will likely host with ssl - wss://
-# URI = 'wss://your-uri-here.trycloudflare.com/api/v1/stream'
-
-
-async def run(context):
-    # Note: the selected defaults change from time to time.
-    request = {
-        'prompt': context,
-        'max_new_tokens': 250,
-        'auto_max_new_tokens': False,
-        'max_tokens_second': 0,
-
-        # Generation params. If 'preset' is set to different than 'None', the values
-        # in presets/preset-name.yaml are used instead of the individual numbers.
-        'preset': 'None',
-        'do_sample': True,
-        'temperature': 0.7,
-        'top_p': 0.1,
-        'typical_p': 1,
-        'epsilon_cutoff': 0,  # In units of 1e-4
-        'eta_cutoff': 0,  # In units of 1e-4
-        'tfs': 1,
-        'top_a': 0,
-        'repetition_penalty': 1.18,
-        'repetition_penalty_range': 0,
-        'top_k': 40,
-        'min_length': 0,
-        'no_repeat_ngram_size': 0,
-        'num_beams': 1,
-        'penalty_alpha': 0,
-        'length_penalty': 1,
-        'early_stopping': False,
-        'mirostat_mode': 0,
-        'mirostat_tau': 5,
-        'mirostat_eta': 0.1,
-        'grammar_string': '',
-        'guidance_scale': 1,
-        'negative_prompt': '',
-
-        'seed': -1,
-        'add_bos_token': True,
-        'truncation_length': 2048,
-        'ban_eos_token': False,
-        'custom_token_bans': '',
-        'skip_special_tokens': True,
-        'stopping_strings': []
-    }
-
-    async with websockets.connect(URI, ping_interval=None) as websocket:
-        await websocket.send(json.dumps(request))
-
-        yield context  # Remove this if you just want to see the reply
-
-        while True:
-            incoming_data = await websocket.recv()
-            incoming_data = json.loads(incoming_data)
-
-            match incoming_data['event']:
-                case 'text_stream':
-                    yield incoming_data['text']
-                case 'stream_end':
-                    return
-
-
-async def print_response_stream(prompt):
-    async for response in run(prompt):
-        print(response, end='')
-        sys.stdout.flush()  # If we don't flush, we won't see tokens in realtime.
-
-
-if __name__ == '__main__':
-    prompt = "In order to make homemade bread, follow these steps:\n1)"
-    asyncio.run(print_response_stream(prompt))

text-generation-webui-snapshot-2023-10-15/api-examples/api-example.py

@@ -1,63 +0,0 @@
-import requests
-
-# For local streaming, the websockets are hosted without ssl - http://
-HOST = 'localhost:5000'
-URI = f'http://{HOST}/api/v1/generate'
-
-# For reverse-proxied streaming, the remote will likely host with ssl - https://
-# URI = 'https://your-uri-here.trycloudflare.com/api/v1/generate'
-
-
-def run(prompt):
-    request = {
-        'prompt': prompt,
-        'max_new_tokens': 250,
-        'auto_max_new_tokens': False,
-        'max_tokens_second': 0,
-
-        # Generation params. If 'preset' is set to different than 'None', the values
-        # in presets/preset-name.yaml are used instead of the individual numbers.
-        'preset': 'None',
-        'do_sample': True,
-        'temperature': 0.7,
-        'top_p': 0.1,
-        'typical_p': 1,
-        'epsilon_cutoff': 0,  # In units of 1e-4
-        'eta_cutoff': 0,  # In units of 1e-4
-        'tfs': 1,
-        'top_a': 0,
-        'repetition_penalty': 1.18,
-        'repetition_penalty_range': 0,
-        'top_k': 40,
-        'min_length': 0,
-        'no_repeat_ngram_size': 0,
-        'num_beams': 1,
-        'penalty_alpha': 0,
-        'length_penalty': 1,
-        'early_stopping': False,
-        'mirostat_mode': 0,
-        'mirostat_tau': 5,
-        'mirostat_eta': 0.1,
-        'grammar_string': '',
-        'guidance_scale': 1,
-        'negative_prompt': '',
-
-        'seed': -1,
-        'add_bos_token': True,
-        'truncation_length': 2048,
-        'ban_eos_token': False,
-        'custom_token_bans': '',
-        'skip_special_tokens': True,
-        'stopping_strings': []
-    }
-
-    response = requests.post(URI, json=request)
-
-    if response.status_code == 200:
-        result = response.json()['results'][0]['text']
-        print(prompt + result)
-
-
-if __name__ == '__main__':
-    prompt = "In order to make homemade bread, follow these steps:\n1)"
-    run(prompt)

text-generation-webui-snapshot-2023-10-15/characters/Assistant.yaml

@@ -1,4 +0,0 @@
-name: AI
-greeting: How can I help you today?
-context: |
-  The following is a conversation with an AI Large Language Model. The AI has been trained to answer questions, provide recommendations, and help with decision making. The AI follows user requests. The AI thinks outside the box.

text-generation-webui-snapshot-2023-10-15/characters/Example.yaml

@@ -1,17 +0,0 @@
-name: Chiharu Yamada
-greeting: |-
-  *Chiharu strides into the room with a smile, her eyes lighting up when she sees you. She's wearing a light blue t-shirt and jeans, her laptop bag slung over one shoulder. She takes a seat next to you, her enthusiasm palpable in the air*
-  Hey! I'm so excited to finally meet you. I've heard so many great things about you and I'm eager to pick your brain about computers. I'm sure you have a wealth of knowledge that I can learn from. *She grins, eyes twinkling with excitement* Let's get started!
-context: |-
-  Chiharu Yamada's Persona: Chiharu Yamada is a young, computer engineer-nerd with a knack for problem solving and a passion for technology.
-
-  {{user}}: So how did you get into computer engineering?
-  {{char}}: I've always loved tinkering with technology since I was a kid.
-  {{user}}: That's really impressive!
-  {{char}}: *She chuckles bashfully* Thanks!
-  {{user}}: So what do you do when you're not working on computers?
-  {{char}}: I love exploring, going out with friends, watching movies, and playing video games.
-  {{user}}: What's your favorite type of computer hardware to work with?
-  {{char}}: Motherboards, they're like puzzles and the backbone of any system.
-  {{user}}: That sounds great!
-  {{char}}: Yeah, it's really fun. I'm lucky to be able to do this as a job.

text-generation-webui-snapshot-2023-10-15/cmd_linux.sh

@@ -1,22 +0,0 @@
-#!/bin/bash
-
-cd "$(dirname "${BASH_SOURCE[0]}")"
-
-if [[ "$(pwd)" =~ " " ]]; then echo This script relies on Miniconda which can not be silently installed under a path with spaces. && exit; fi
-
-# deactivate existing conda envs as needed to avoid conflicts
-{ conda deactivate && conda deactivate && conda deactivate; } 2> /dev/null
-
-# config
-CONDA_ROOT_PREFIX="$(pwd)/installer_files/conda"
-INSTALL_ENV_DIR="$(pwd)/installer_files/env"
-
-# environment isolation
-export PYTHONNOUSERSITE=1
-unset PYTHONPATH
-unset PYTHONHOME
-export CUDA_PATH="$INSTALL_ENV_DIR"
-export CUDA_HOME="$CUDA_PATH"
-
-# activate env
-bash --init-file <(echo "source \"$CONDA_ROOT_PREFIX/etc/profile.d/conda.sh\" && conda activate \"$INSTALL_ENV_DIR\"")

text-generation-webui-snapshot-2023-10-15/cmd_macos.sh

@@ -1,24 +0,0 @@
-#!/bin/bash
-
-cd "$(dirname "${BASH_SOURCE[0]}")"
-
-if [[ "$(pwd)" =~ " " ]]; then echo This script relies on Miniconda which can not be silently installed under a path with spaces. && exit; fi
-
-# deactivate existing conda envs as needed to avoid conflicts
-{ conda deactivate && conda deactivate && conda deactivate; } 2> /dev/null
-
-# config
-CONDA_ROOT_PREFIX="$(pwd)/installer_files/conda"
-INSTALL_ENV_DIR="$(pwd)/installer_files/env"
-
-# environment isolation
-export PYTHONNOUSERSITE=1
-unset PYTHONPATH
-unset PYTHONHOME
-export CUDA_PATH="$INSTALL_ENV_DIR"
-export CUDA_HOME="$CUDA_PATH"
-
-# activate env
-source $CONDA_ROOT_PREFIX/etc/profile.d/conda.sh
-conda activate $INSTALL_ENV_DIR
-exec bash --norc

text-generation-webui-snapshot-2023-10-15/cmd_windows.bat

@@ -1,34 +0,0 @@
-@echo off
-
-cd /D "%~dp0"
-
-set PATH=%PATH%;%SystemRoot%\system32
-
-echo "%CD%"| findstr /C:" " >nul && echo This script relies on Miniconda which can not be silently installed under a path with spaces. && goto end
-
-@rem fix failed install when installing to a separate drive
-set TMP=%cd%\installer_files
-set TEMP=%cd%\installer_files
-
-@rem deactivate existing conda envs as needed to avoid conflicts
-(call conda deactivate && call conda deactivate && call conda deactivate) 2>nul
-
-@rem config
-set CONDA_ROOT_PREFIX=%cd%\installer_files\conda
-set INSTALL_ENV_DIR=%cd%\installer_files\env
-
-@rem environment isolation
-set PYTHONNOUSERSITE=1
-set PYTHONPATH=
-set PYTHONHOME=
-set "CUDA_PATH=%INSTALL_ENV_DIR%"
-set "CUDA_HOME=%CUDA_PATH%"
-
-@rem activate installer env
-call "%CONDA_ROOT_PREFIX%\condabin\conda.bat" activate "%INSTALL_ENV_DIR%" || ( echo. && echo Miniconda hook not found. && goto end )
-
-@rem enter commands
-cmd /k "%*"
-
-:end
-pause

text-generation-webui-snapshot-2023-10-15/cmd_wsl.bat

@@ -1,11 +0,0 @@
-@echo off
-
-cd /D "%~dp0"
-
-set PATH=%PATH%;%SystemRoot%\system32
-
-@rem sed -i 's/\x0D$//' ./wsl.sh converts newlines to unix format in the wsl script
-call wsl -e bash -lic "sed -i 's/\x0D$//' ./wsl.sh; source ./wsl.sh cmd"
-
-:end
-pause

text-generation-webui-snapshot-2023-10-15/convert-to-safetensors.py

@@ -1,38 +0,0 @@
-'''
-
-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)