Delete text-generation-webui-main

text-generation-webui-main/.github/FUNDING.yml

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

text-generation-webui-main/.github/ISSUE_TEMPLATE/bug_report_template.yml

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-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-main/.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-main/.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-main/.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 30 days. If you believe it is still relevant, please leave a comment below."
-          days-before-issue-stale: 30
-          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-main/.gitignore

@@ -1,27 +0,0 @@
-cache
-characters
-training/datasets
-extensions/silero_tts/outputs
-extensions/elevenlabs_tts/outputs
-extensions/sd_api_pictures/outputs
-logs
-loras
-models
-repositories
-softprompts
-torch-dumps
-*pycache*
-*/*pycache*
-*/*/pycache*
-venv/
-.venv/
-.vscode
-*.bak
-*.ipynb
-*.log
-
-settings.json
-img_bot*
-img_me*
-prompts/[0-9]*
-models/config-user.yaml

text-generation-webui-main/LICENSE

@@ -1,661 +0,0 @@
-                    GNU AFFERO GENERAL PUBLIC LICENSE
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-For more information on this, and how to apply and follow the GNU AGPL, see
-<https://www.gnu.org/licenses/>.

text-generation-webui-main/README.md

@@ -1,310 +0,0 @@
-# Text generation web UI
-
-A gradio web UI for running Large Language Models like LLaMA, llama.cpp, GPT-J, Pythia, OPT, and GALACTICA.
-
-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/qa.png) | ![Image2](https://github.com/oobabooga/screenshots/raw/main/cai3.png) |
-|:---:|:---:|
-|![Image3](https://github.com/oobabooga/screenshots/raw/main/gpt4chan.png) | ![Image4](https://github.com/oobabooga/screenshots/raw/main/galactica.png) |
-
-## Features
-
-* Dropdown menu for switching between models
-* Notebook mode that resembles OpenAI's playground
-* Chat mode for conversation and role playing
-* Instruct mode compatible with Alpaca, Vicuna, Open Assistant, Dolly, Koala, and ChatGLM formats 
-* Nice HTML output for GPT-4chan
-* Markdown output for [GALACTICA](https://github.com/paperswithcode/galai), including LaTeX rendering
-* [Custom chat characters](docs/Custom-chat-characters.md)
-* Advanced chat features (send images, get audio responses with TTS)
-* Very efficient text streaming
-* Parameter presets
-* 8-bit mode
-* Layers splitting across GPU(s), CPU, and disk
-* CPU mode
-* [FlexGen](docs/FlexGen.md)
-* [DeepSpeed ZeRO-3](docs/DeepSpeed.md)
-* API [with](https://github.com/oobabooga/text-generation-webui/blob/main/api-example-stream.py) streaming and [without](https://github.com/oobabooga/text-generation-webui/blob/main/api-example.py) streaming
-* [LLaMA model](docs/LLaMA-model.md)
-* [4-bit GPTQ mode](docs/GPTQ-models-(4-bit-mode).md)
-* [llama.cpp](docs/llama.cpp-models.md)
-* [RWKV model](docs/RWKV-model.md)
-* [LoRA (loading and training)](docs/Using-LoRAs.md)
-* Softprompts
-* [Extensions](docs/Extensions.md) - see the [user extensions list](https://github.com/oobabooga/text-generation-webui-extensions)
-
-## Installation
-
-### One-click installers
-
-| Windows | Linux | macOS |
-|-------|--------|--------|
-| [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) |
-
-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 are here: https://github.com/oobabooga/one-click-installers
-* 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.
-* There is no need to run the installers as admin.
-
-### Manual installation using Conda
-
-Recommended if you have some experience with the command-line.
-
-On Windows, I additionally recommend carrying out the installation on WSL instead of the base system: [WSL installation guide](https://github.com/oobabooga/text-generation-webui/blob/main/docs/WSL-installation-guide.md).
-
-#### 0. Install Conda
-
-https://docs.conda.io/en/latest/miniconda.html
-
-On Linux or WSL, it can be automatically installed with these two commands:
-
-```
-curl -sL "https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh" > "Miniconda3.sh"
-bash Miniconda3.sh
-```
-Source: https://educe-ubc.github.io/conda.html
-
-#### 0.1 (Ubuntu/WSL) Install build tools
-
-```
-sudo apt install build-essential
-```
-
-
-#### 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 | AMD | `pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm5.4.2` |
-| MacOS + MPS (untested) | Any | `pip3 install torch torchvision torchaudio` |
-
-The up to date commands can be found here: https://pytorch.org/get-started/locally/. 
-
-#### 2.1 Special instructions
-
-* MacOS users: https://github.com/oobabooga/text-generation-webui/pull/393
-* AMD users: https://rentry.org/eq3hg
-
-#### 3. Install the web UI
-
-```
-git clone https://github.com/oobabooga/text-generation-webui
-cd text-generation-webui
-pip install -r requirements.txt
-```
-
-### Alternative: manual Windows installation
-
-As an alternative to the recommended WSL method, you can install the web UI natively on Windows using this guide. It will be a lot harder and the performance may be slower: [Windows installation guide](https://github.com/oobabooga/text-generation-webui/blob/main/docs/Windows-installation-guide.md).
-
-### 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 in your system. To see how to install docker compose itself, see the guide in [here](https://github.com/oobabooga/text-generation-webui/blob/main/docs/Docker.md).
-
-Contributed by [@loeken](https://github.com/loeken) in [#633](https://github.com/oobabooga/text-generation-webui/pull/633)
-
-### Updating the requirements
-
-From time to time, the `requirements.txt` changes. To update, use this command:
-
-```
-conda activate textgen
-cd text-generation-webui
-pip install -r requirements.txt --upgrade
-```
-## Downloading models
-
-Models should be placed inside the `models` folder.
-
-[Hugging Face](https://huggingface.co/models?pipeline_tag=text-generation&sort=downloads) is the main place to download models. These are some examples:
-
-* [Pythia](https://huggingface.co/models?sort=downloads&search=eleutherai%2Fpythia+deduped)
-* [OPT](https://huggingface.co/models?search=facebook/opt)
-* [GALACTICA](https://huggingface.co/models?search=facebook/galactica)
-* [GPT-J 6B](https://huggingface.co/EleutherAI/gpt-j-6B/tree/main)
-
-You can automatically download a model from HF using the script `download-model.py`:
-
-    python download-model.py organization/model
-
-For example:
-
-    python download-model.py facebook/opt-1.3b
-
-If you want to download a model manually, note that all you need are the json, txt, and pytorch\*.bin (or model*.safetensors) files. The remaining files are not necessary.
-
-#### GPT-4chan
-
-[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
-```
-
-## 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. |
-| `--notebook`                               | Launch the web UI in notebook mode, where the output is written to the same text box as the input. |
-| `--chat`                                   | Launch the web UI in chat mode. |
-| `--model MODEL`                            | Name of the model to load by default. |
-| `--lora LORA`                              | Name of the LoRA to apply to the model by default. |
-| `--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. |
-| `--no-stream`                              | Don't stream the text output in real time. |
-| `--settings SETTINGS_FILE`                 | Load the default interface settings from this json file. See `settings-template.json` for an example. If you create a file called `settings.json`, 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. |
-
-#### 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 ...]` | Maxmimum 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.|
-| `--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. |
-
-#### llama.cpp
-
-| Flag        | Description |
-|-------------|-------------|
-| `--threads` | Number of threads to use in llama.cpp. |
-| `--n_batch` | Processing batch size for llama.cpp. |
-
-#### GPTQ
-
-| 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`   | The number of layers to allocate to the GPU. Setting this parameter enables CPU offloading for 4-bit models. |
-| `--monkey-patch`          | Apply the monkey patch for using LoRAs with quantized models.
-| `--quant_attn`         | (triton) Enable quant attention.
-| `--warmup_autotune`    | (triton) Enable warmup autotune.
-| `--fused_mlp`          | (triton) Enable fused mlp.
-
-#### FlexGen
-
-| Flag             | Description |
-|------------------|-------------|
-| `--flexgen`                       | Enable the use of FlexGen offloading. |
-| `--percent PERCENT [PERCENT ...]` | FlexGen: allocation percentages. Must be 6 numbers separated by spaces (default: 0, 100, 100, 0, 100, 0). |
-| `--compress-weight`               | FlexGen: Whether to compress weight (default: False).|
-| `--pin-weight [PIN_WEIGHT]`       | FlexGen: whether to pin weights (setting this to False reduces CPU memory by 20%). |
-
-#### 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. |
-
-#### 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-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" |
-
-#### API
-
-| Flag                                  | Description |
-|---------------------------------------|-------------|
-| `--api`                               | Enable the API extension. |
-| `--public-api`                        | Create a public URL for the API using Cloudfare. |
-
-Out of memory errors? [Check the low VRAM guide](docs/Low-VRAM-guide.md).
-
-## Presets
-
-Inference settings presets can be created under `presets/` as text files. These files are detected automatically at startup.
-
-By default, 10 presets by NovelAI and KoboldAI are included. These were selected out of a sample of 43 presets after applying a K-Means clustering algorithm and selecting the elements closest to the average of each cluster.
-
-[Visualization](https://user-images.githubusercontent.com/112222186/228956352-1addbdb9-2456-465a-b51d-089f462cd385.png)
-
-## System requirements
-
-Check the [wiki](docs/System-requirements.md) for some examples of VRAM and RAM usage in both GPU and CPU mode.
-
-## Contributing
-
-Pull requests, suggestions, and issue reports are welcome. 
-
-You are also welcome to review open pull requests.
-
-Before reporting a bug, make sure that you have:
-
-1. Created a conda environment and installed the dependencies exactly as in the *Installation* section above.
-2. [Searched](https://github.com/oobabooga/text-generation-webui/issues) to see if an issue already exists for the issue you encountered.
-
-## Credits
-
-- Gradio dropdown menu refresh button, code for reloading the interface: https://github.com/AUTOMATIC1111/stable-diffusion-webui
-- Verbose preset: Anonymous 4chan user.
-- NovelAI and KoboldAI presets: https://github.com/KoboldAI/KoboldAI-Client/wiki/Settings-Presets
-- Code for early stopping in chat mode, code for some of the sliders: https://github.com/PygmalionAI/gradio-ui/

text-generation-webui-main/api-example-stream.py

@@ -1,66 +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,
-        'do_sample': True,
-        'temperature': 1.3,
-        'top_p': 0.1,
-        'typical_p': 1,
-        'repetition_penalty': 1.18,
-        'top_k': 40,
-        'min_length': 0,
-        'no_repeat_ngram_size': 0,
-        'num_beams': 1,
-        'penalty_alpha': 0,
-        'length_penalty': 1,
-        'early_stopping': False,
-        'seed': -1,
-        'add_bos_token': True,
-        'truncation_length': 2048,
-        'ban_eos_token': False,
-        'skip_special_tokens': True,
-        'stopping_strings': []
-    }
-
-    async with websockets.connect(URI) 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-main/api-example.py

@@ -1,42 +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(context):
-    request = {
-        'prompt': prompt,
-        'max_new_tokens': 250,
-        'do_sample': True,
-        'temperature': 1.3,
-        'top_p': 0.1,
-        'typical_p': 1,
-        'repetition_penalty': 1.18,
-        'top_k': 40,
-        'min_length': 0,
-        'no_repeat_ngram_size': 0,
-        'num_beams': 1,
-        'penalty_alpha': 0,
-        'length_penalty': 1,
-        'early_stopping': False,
-        'seed': -1,
-        'add_bos_token': True,
-        'truncation_length': 2048,
-        'ban_eos_token': False,
-        '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-main/characters/Example.yaml

@@ -1,16 +0,0 @@
-name: "Chiharu Yamada"
-context: "Chiharu Yamada's Persona: Chiharu Yamada is a young, computer engineer-nerd with a knack for problem solving and a passion for technology."
-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!
-example_dialogue: |-
-  {{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-main/characters/instruction-following/Alpaca.yaml

@@ -1,3 +0,0 @@
-name: "### Response:"
-your_name: "### Instruction:"
-context: "Below is an instruction that describes a task. Write a response that appropriately completes the request."

text-generation-webui-main/characters/instruction-following/ChatGLM.yaml

@@ -1,3 +0,0 @@
-name: "答:"
-your_name: "[Round <|round|>]\n问:"
-context: ""

text-generation-webui-main/characters/instruction-following/Koala.yaml

@@ -1,3 +0,0 @@
-name: "GPT:"
-your_name: "USER:"
-context: "BEGINNING OF CONVERSATION:"

text-generation-webui-main/characters/instruction-following/LLaVA.yaml

@@ -1,3 +0,0 @@
-name: "### Assistant"
-your_name: "### Human"
-context: "You are LLaVA, a large language and vision assistant trained by UW Madison WAIV Lab. You are able to understand the visual content that the user provides, and assist the user with a variety of tasks using natural language. Follow the instructions carefully and explain your answers in detail.\n### Human: \nHi!\n### Assistant: \nHi there! How can I help you today?\n"

text-generation-webui-main/characters/instruction-following/Open Assistant.yaml

@@ -1,3 +0,0 @@
-name: "<|assistant|>"
-your_name: "<|prompter|>"
-end_of_turn: "<|endoftext|>"

text-generation-webui-main/characters/instruction-following/Vicuna.yaml

@@ -1,3 +0,0 @@
-name: "### Assistant:"
-your_name: "### Human:"
-context: "Below is an instruction that describes a task. Write a response that appropriately completes the request."

text-generation-webui-main/convert-to-flexgen.py

@@ -1,63 +0,0 @@
-'''
-
-Converts a transformers model to a format compatible with flexgen.
-
-'''
-
-import argparse
-import os
-from pathlib import Path
-
-import numpy as np
-import torch
-from tqdm import tqdm
-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.")
-args = parser.parse_args()
-
-
-def disable_torch_init():
-    """
-    Disable the redundant torch default initialization to accelerate model creation.
-    """
-    import torch
-    global torch_linear_init_backup
-    global torch_layer_norm_init_backup
-
-    torch_linear_init_backup = torch.nn.Linear.reset_parameters
-    setattr(torch.nn.Linear, "reset_parameters", lambda self: None)
-
-    torch_layer_norm_init_backup = torch.nn.LayerNorm.reset_parameters
-    setattr(torch.nn.LayerNorm, "reset_parameters", lambda self: None)
-
-
-def restore_torch_init():
-    """Rollback the change made by disable_torch_init."""
-    import torch
-    setattr(torch.nn.Linear, "reset_parameters", torch_linear_init_backup)
-    setattr(torch.nn.LayerNorm, "reset_parameters", torch_layer_norm_init_backup)
-
-
-if __name__ == '__main__':
-    path = Path(args.MODEL)
-    model_name = path.name
-
-    print(f"Loading {model_name}...")
-    # disable_torch_init()
-    model = AutoModelForCausalLM.from_pretrained(path, torch_dtype=torch.float16, low_cpu_mem_usage=True)
-    # restore_torch_init()
-
-    tokenizer = AutoTokenizer.from_pretrained(path)
-
-    out_folder = Path(f"models/{model_name}-np")
-    if not Path(out_folder).exists():
-        os.mkdir(out_folder)
-
-    print(f"Saving the converted model to {out_folder}...")
-    for name, param in tqdm(list(model.model.named_parameters())):
-        name = name.replace("decoder.final_layer_norm", "decoder.layer_norm")
-        param_path = os.path.join(out_folder, name)
-        with open(param_path, "wb") as f:
-            np.save(f, param.cpu().detach().numpy())

text-generation-webui-main/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)

text-generation-webui-main/css/chat.css

@@ -1,43 +0,0 @@
-.h-\[40vh\], .wrap.svelte-byatnx.svelte-byatnx.svelte-byatnx {
-    height: 66.67vh
-}
-
-.gradio-container {
-    margin-left: auto !important;
-    margin-right: auto !important;
-}
-
-.w-screen {
-    width: unset
-}
-
-div.svelte-362y77>*, div.svelte-362y77>.form>* {
-    flex-wrap: nowrap
-}
-
-/* fixes the API documentation in chat mode */
-.api-docs.svelte-1iguv9h.svelte-1iguv9h.svelte-1iguv9h {
-    display: grid;
-}
-
-.pending.svelte-1ed2p3z {
-    opacity: 1;
-}
-
-#extensions {
-  padding: 0;
-  padding: 0;
-}
-
-#gradio-chatbot {
-  height: 66.67vh;
-}
-
-.wrap.svelte-6roggh.svelte-6roggh {
-  max-height: 92.5%;
-}
-
-/* This is for the microphone button in the whisper extension */
-.sm.svelte-1ipelgc {
-  width: 100%;
-}

text-generation-webui-main/css/chat.js

@@ -1,4 +0,0 @@
-document.getElementById("main").childNodes[0].style = "max-width: 800px; margin-left: auto; margin-right: auto";
-document.getElementById("extensions").style.setProperty("max-width", "800px");
-document.getElementById("extensions").style.setProperty("margin-left", "auto");
-document.getElementById("extensions").style.setProperty("margin-right", "auto");

text-generation-webui-main/css/html_4chan_style.css

@@ -1,103 +0,0 @@
-#parent #container {
-    background-color: #eef2ff;
-    padding: 17px;
-}
-#parent #container .reply {
-    background-color: rgb(214, 218, 240);
-    border-bottom-color: rgb(183, 197, 217);
-    border-bottom-style: solid;
-    border-bottom-width: 1px;
-    border-image-outset: 0;
-    border-image-repeat: stretch;
-    border-image-slice: 100%;
-    border-image-source: none;
-    border-image-width: 1;
-    border-left-color: rgb(0, 0, 0);
-    border-left-style: none;
-    border-left-width: 0px;
-    border-right-color: rgb(183, 197, 217);
-    border-right-style: solid;
-    border-right-width: 1px;
-    border-top-color: rgb(0, 0, 0);
-    border-top-style: none;
-    border-top-width: 0px;
-    color: rgb(0, 0, 0);
-    display: table;
-    font-family: arial, helvetica, sans-serif;
-    font-size: 13.3333px;
-    margin-bottom: 4px;
-    margin-left: 0px;
-    margin-right: 0px;
-    margin-top: 4px;
-    overflow-x: hidden;
-    overflow-y: hidden;
-    padding-bottom: 4px;
-    padding-left: 2px;
-    padding-right: 2px;
-    padding-top: 4px;
-}
-
-#parent #container .number {
-    color: rgb(0, 0, 0);
-    font-family: arial, helvetica, sans-serif;
-    font-size: 13.3333px;
-    width: 342.65px;
-    margin-right: 7px;
-}
-
-#parent #container .op {
-    color: rgb(0, 0, 0);
-    font-family: arial, helvetica, sans-serif;
-    font-size: 13.3333px;
-    margin-bottom: 8px;
-    margin-left: 0px;
-    margin-right: 0px;
-    margin-top: 4px;
-    overflow-x: hidden;
-    overflow-y: hidden;
-}
-
-#parent #container .op blockquote {
-    margin-left: 0px !important;
-}
-
-#parent #container .name {
-    color: rgb(17, 119, 67);
-    font-family: arial, helvetica, sans-serif;
-    font-size: 13.3333px;
-    font-weight: 700;
-    margin-left: 7px;
-}
-
-#parent #container .quote {
-    color: rgb(221, 0, 0);
-    font-family: arial, helvetica, sans-serif;
-    font-size: 13.3333px;
-    text-decoration-color: rgb(221, 0, 0);
-    text-decoration-line: underline;
-    text-decoration-style: solid;
-    text-decoration-thickness: auto;
-}
-
-#parent #container .greentext {
-    color: rgb(120, 153, 34);
-    font-family: arial, helvetica, sans-serif;
-    font-size: 13.3333px;
-}
-
-#parent #container blockquote {
-    margin: 0px !important;
-    margin-block-start: 1em;
-    margin-block-end: 1em;
-    margin-inline-start: 40px;
-    margin-inline-end: 40px;
-    margin-top: 13.33px !important;
-    margin-bottom: 13.33px !important;
-    margin-left: 40px !important;
-    margin-right: 40px !important;
-}
-
-#parent #container .message {
-    color: black;
-    border: none;
-}

text-generation-webui-main/css/html_bubble_chat_style.css

@@ -1,86 +0,0 @@
-.chat {
-    margin-left: auto;
-    margin-right: auto;
-    max-width: 800px;
-    height: calc(100vh - 300px);
-    overflow-y: auto;
-    padding-right: 20px;
-    display: flex;
-    flex-direction: column-reverse;
-    word-break: break-word;
-    overflow-wrap: anywhere;
-}
-
-.message {
-    padding-bottom: 25px;
-    font-size: 15px;
-    font-family: Helvetica, Arial, sans-serif;
-    line-height: 1.428571429;
-}
-
-.text-you {
-  background-color: #d9fdd3;
-  border-radius: 15px;
-  padding: 10px;
-  padding-top: 5px;
-  float: right;
-}
-
-.text-bot {
-  background-color: #f2f2f2;
-  border-radius: 15px;
-  padding: 10px;
-  padding-top: 5px;
-}
-
-.dark .text-you {
-  background-color: #005c4b;
-  color: #111b21;
-}
-
-.dark .text-bot {
-  background-color: #1f2937;
-  color: #111b21;
-}
-
-.text-bot p, .text-you p {
-  margin-top: 5px;
-}
-
-.message-body {}
-
-.message-body img {
-    max-width: 300px;
-    max-height: 300px;
-    border-radius: 20px;
-}
-
-.message-body p {
-    margin-bottom: 0 !important;
-    font-size: 15px !important;
-    line-height: 1.428571429 !important;
-}
-
-.message-body li {
-    margin-top: 0.5em !important;
-    margin-bottom: 0.5em !important;
-}
-
-.message-body li > p {
-    display: inline !important;
-}
-
-.message-body code {
-    overflow-x: auto;
-}
-.message-body :not(pre) > code {
-    white-space: normal !important;
-}
-
-.dark .message-body p em {
-    color: rgb(138, 138, 138) !important;
-}
-
-.message-body p em {
-    color: rgb(110, 110, 110) !important;
-}

text-generation-webui-main/css/html_cai_style.css

@@ -1,91 +0,0 @@
-.chat {
-    margin-left: auto;
-    margin-right: auto;
-    max-width: 800px;
-    height: calc(100vh - 300px);
-    overflow-y: auto;
-    padding-right: 20px;
-    display: flex;
-    flex-direction: column-reverse;
-    word-break: break-word;
-    overflow-wrap: anywhere;
-}
-
-.message {
-    display: grid;
-    grid-template-columns: 60px minmax(0, 1fr);
-    padding-bottom: 25px;
-    font-size: 15px;
-    font-family: Helvetica, Arial, sans-serif;
-    line-height: 1.428571429;
-}
-
-.circle-you {
-    width: 50px;
-    height: 50px;
-    background-color: rgb(238, 78, 59);
-    border-radius: 50%;
-}
-
-.circle-bot {
-    width: 50px;
-    height: 50px;
-    background-color: rgb(59, 78, 244);
-    border-radius: 50%;
-}
-
-.circle-bot img,
-.circle-you img {
-    border-radius: 50%;
-    width: 100%;
-    height: 100%;
-    object-fit: cover;
-}
-
-.text {}
-
-.text p {
-    margin-top: 5px;
-}
-
-.username {
-    font-weight: bold;
-}
-
-.message-body {}
-
-.message-body img {
-    max-width: 300px;
-    max-height: 300px;
-    border-radius: 20px;
-}
-
-.message-body p {
-    margin-bottom: 0 !important;
-    font-size: 15px !important;
-    line-height: 1.428571429 !important;
-}
-
-.message-body li {
-    margin-top: 0.5em !important;
-    margin-bottom: 0.5em !important;
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-
-.message-body li > p {
-    display: inline !important;
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-
-.message-body code {
-    overflow-x: auto;
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-.message-body :not(pre) > code {
-    white-space: normal !important;
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-
-.dark .message-body p em {
-    color: rgb(138, 138, 138) !important;
-}
-
-.message-body p em {
-    color: rgb(110, 110, 110) !important;
-}

text-generation-webui-main/css/html_instruct_style.css

@@ -1,73 +0,0 @@
-.chat {
-    margin-left: auto;
-    margin-right: auto;
-    max-width: 800px;
-    height: calc(100vh - 300px);
-    overflow-y: auto;
-    padding-right: 20px;
-    display: flex;
-    flex-direction: column-reverse;
-    word-break: break-word;
-    overflow-wrap: anywhere;
-}
-
-.message {
-    display: grid;
-    grid-template-columns: 60px 1fr;
-    padding-bottom: 25px;
-    font-size: 15px;
-    font-family: Helvetica, Arial, sans-serif;
-    line-height: 1.428571429;
-}
-
-.username {
-    display: none;
-}
-
-.message-body {}
-
-.message-body p {
-    font-size: 15px !important;
-}
-
-.message-body li {
-    margin-top: 0.5em !important;
-    margin-bottom: 0.5em !important;
-}
-
-.message-body li > p {
-    display: inline !important;
-}
-
-.message-body code {
-    overflow-x: auto;
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-.message-body :not(pre) > code {
-    white-space: normal !important;
-}
-
-.dark .message-body p em {
-    color: rgb(138, 138, 138) !important;
-}
-
-.message-body p em {
-    color: rgb(110, 110, 110) !important;
-}
-
-.gradio-container .chat .assistant-message {
-  padding: 15px;
-  border-radius: 20px;
-  background-color: #0000000f;
-  margin-top: 9px !important;
-  margin-bottom: 18px !important;
-}
-
-.gradio-container .chat .user-message {
-  padding: 15px;
-  border-radius: 20px;
-  margin-bottom: 9px !important;
-}
-
-.dark .chat .assistant-message {
-  background-color: #374151;
-}

text-generation-webui-main/css/html_readable_style.css

@@ -1,29 +0,0 @@
-.container {
-    max-width: 600px;
-    margin-left: auto;
-    margin-right: auto;
-    background-color: rgb(31, 41, 55);
-    padding:3em;
-    word-break: break-word;
-    overflow-wrap: anywhere;
-    color: #efefef !important;
-}
-
-.container p, .container li {
-    font-size: 16px !important;
-    color: #efefef !important;
-    margin-bottom: 22px;
-    line-height: 1.4 !important;
-}
-
-.container li > p {
-    display: inline !important;
-}
-
-.container code {
-    overflow-x: auto;
-}
-
-.container :not(pre) > code {
-    white-space: normal !important;
-}

text-generation-webui-main/css/main.css

@@ -1,139 +0,0 @@
-.tabs.svelte-710i53 {
-    margin-top: 0
-}
-
-.py-6 {
-    padding-top: 2.5rem
-}
-
-.dark #refresh-button {
-    background-color: #ffffff1f;
-}
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-#refresh-button {
-  flex: none;
-  margin: 0;
-  padding: 0;
-  min-width: 50px;
-  border: none;
-  box-shadow: none;
-  border-radius: 10px;
-  background-color: #0000000d;
-}
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-#download-label, #upload-label {
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-#accordion {
-}
-
-.dark svg {
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-}
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-.dark a {
-  color: white !important;
-  text-decoration: none !important;
-}
-
-ol li p, ul li p {
-    display: inline-block;
-}
-
-#main, #parameters, #chat-settings, #interface-mode, #lora, #training-tab, #model-tab {
-  border: 0;
-}
-
-.gradio-container-3-18-0 .prose * h1, h2, h3, h4 {
-  color: white;
-}
-
-.gradio-container {
-  max-width: 100% !important;
-  padding-top: 0 !important;
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-#extensions {
-  padding: 15px;
-  margin-bottom: 35px;
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-  font-size: 27px;
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-.header_bar {
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-  border: none !important;
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-.dark textarea {
-  color: #efefef !important;
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-
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-  font-size: 14px !important;
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-.small-button {
-  max-width: 171px;
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-
-/* Align the elements for SD_api_picture extension */
-.SDAP #sampler_box {
-  padding-top: var(--spacing-sm);
-  padding-bottom: var(--spacing-sm);
-}
-
-.SDAP #seed_box,
-.SDAP #cfg_box {
-  padding-top: var(--spacing-md);
-}
-
-.SDAP #sampler_box span,
-.SDAP #seed_box span,
-.SDAP #cfg_box span{
-  margin-bottom: var(--spacing-sm);
-}
-
-.SDAP svg.dropdown-arrow {
-  flex-shrink: 0 !important;
-  margin: 0px !important;
-}
-
-.SDAP .hires_opts input[type="number"] {
-  width: 6em !important;
-}

text-generation-webui-main/css/main.js

@@ -1,18 +0,0 @@
-document.getElementById("main").parentNode.childNodes[0].classList.add("header_bar");
-document.getElementById("main").parentNode.style = "padding: 0; margin: 0";
-document.getElementById("main").parentNode.parentNode.parentNode.style = "padding: 0";
-
-// Get references to the elements
-let main = document.getElementById('main');
-let main_parent = main.parentNode;
-let extensions = document.getElementById('extensions');
-
-// Add an event listener to the main element
-main_parent.addEventListener('click', function(e) {
-    // Check if the main element is visible
-    if (main.offsetHeight > 0 && main.offsetWidth > 0) {
-        extensions.style.display = 'flex';
-    } else {
-        extensions.style.display = 'none';
-    }
-});

text-generation-webui-main/docker/.dockerignore

@@ -1,9 +0,0 @@
-.env
-Dockerfile
-/characters
-/loras
-/models
-/presets
-/prompts
-/softprompts
-/training

text-generation-webui-main/docker/.env.example

@@ -1,25 +0,0 @@
-# by default the Dockerfile specifies these versions: 3.5;5.0;6.0;6.1;7.0;7.5;8.0;8.6+PTX
-# however for me to work i had to specify the exact version for my card ( 2060 ) it was 7.5
-# https://developer.nvidia.com/cuda-gpus you can find the version for your card here
-TORCH_CUDA_ARCH_LIST=7.5
-
-# these commands worked for me with roughly 4.5GB of vram
-CLI_ARGS=--model llama-7b-4bit --wbits 4 --listen --auto-devices
-
-# the following examples have been tested with the files linked in docs/README_docker.md:
-# example running 13b with 4bit/128 groupsize        : CLI_ARGS=--model llama-13b-4bit-128g --wbits 4 --listen --groupsize 128 --pre_layer 25
-# example with loading api extension and public share: CLI_ARGS=--model llama-7b-4bit --wbits 4 --listen --auto-devices --no-stream --extensions api --share
-# example running 7b with 8bit groupsize             : CLI_ARGS=--model llama-7b --load-in-8bit --listen --auto-devices
-
-# the port the webui binds to on the host
-HOST_PORT=7860
-# the port the webui binds to inside the container
-CONTAINER_PORT=7860
-
-# the port the api binds to on the host
-HOST_API_PORT=5000
-# the port the api binds to inside the container
-CONTAINER_API_PORT=5000
-
-# the version used to install text-generation-webui from
-WEBUI_VERSION=HEAD

text-generation-webui-main/docker/Dockerfile

@@ -1,68 +0,0 @@
-FROM nvidia/cuda:11.8.0-devel-ubuntu22.04 as builder
-
-RUN apt-get update && \
-    apt-get install --no-install-recommends -y git vim build-essential python3-dev python3-venv && \
-    rm -rf /var/lib/apt/lists/*
-
-RUN git clone https://github.com/oobabooga/GPTQ-for-LLaMa /build
-
-WORKDIR /build
-
-RUN python3 -m venv /build/venv
-RUN . /build/venv/bin/activate && \
-    pip3 install --upgrade pip setuptools && \
-    pip3 install torch torchvision torchaudio && \
-    pip3 install -r requirements.txt
-
-# https://developer.nvidia.com/cuda-gpus
-# for a rtx 2060: ARG TORCH_CUDA_ARCH_LIST="7.5"
-ARG TORCH_CUDA_ARCH_LIST="3.5;5.0;6.0;6.1;7.0;7.5;8.0;8.6+PTX"
-RUN . /build/venv/bin/activate && \
-    python3 setup_cuda.py bdist_wheel -d .
-
-FROM nvidia/cuda:11.8.0-runtime-ubuntu22.04
-
-LABEL maintainer="Your Name <your.email@example.com>"
-LABEL description="Docker image for GPTQ-for-LLaMa and Text Generation WebUI"
-
-RUN apt-get update && \
-    apt-get install --no-install-recommends -y libportaudio2 libasound-dev git python3 python3-pip make g++ && \
-    rm -rf /var/lib/apt/lists/*
-
-RUN --mount=type=cache,target=/root/.cache/pip pip3 install virtualenv
-RUN mkdir /app
-
-WORKDIR /app
-
-ARG WEBUI_VERSION
-RUN test -n "${WEBUI_VERSION}" && git reset --hard ${WEBUI_VERSION} || echo "Using provided webui source"
-
-RUN virtualenv /app/venv
-RUN . /app/venv/bin/activate && \
-    pip3 install --upgrade pip setuptools && \
-    pip3 install torch torchvision torchaudio
-
-COPY --from=builder /build /app/repositories/GPTQ-for-LLaMa
-RUN . /app/venv/bin/activate && \
-    pip3 install /app/repositories/GPTQ-for-LLaMa/*.whl
-
-COPY extensions/api/requirements.txt /app/extensions/api/requirements.txt
-COPY extensions/elevenlabs_tts/requirements.txt /app/extensions/elevenlabs_tts/requirements.txt
-COPY extensions/google_translate/requirements.txt /app/extensions/google_translate/requirements.txt
-COPY extensions/silero_tts/requirements.txt /app/extensions/silero_tts/requirements.txt
-COPY extensions/whisper_stt/requirements.txt /app/extensions/whisper_stt/requirements.txt
-RUN --mount=type=cache,target=/root/.cache/pip . /app/venv/bin/activate && cd extensions/api && pip3 install -r requirements.txt
-RUN --mount=type=cache,target=/root/.cache/pip . /app/venv/bin/activate && cd extensions/elevenlabs_tts && pip3 install -r requirements.txt
-RUN --mount=type=cache,target=/root/.cache/pip . /app/venv/bin/activate && cd extensions/google_translate && pip3 install -r requirements.txt
-RUN --mount=type=cache,target=/root/.cache/pip . /app/venv/bin/activate && cd extensions/silero_tts && pip3 install -r requirements.txt
-RUN --mount=type=cache,target=/root/.cache/pip . /app/venv/bin/activate && cd extensions/whisper_stt && pip3 install -r requirements.txt
-
-COPY requirements.txt /app/requirements.txt
-RUN . /app/venv/bin/activate && \
-    pip3 install -r requirements.txt
-
-RUN cp /app/venv/lib/python3.10/site-packages/bitsandbytes/libbitsandbytes_cuda118.so /app/venv/lib/python3.10/site-packages/bitsandbytes/libbitsandbytes_cpu.so
-
-COPY . /app/
-ENV CLI_ARGS=""
-CMD . /app/venv/bin/activate && python3 server.py ${CLI_ARGS}

text-generation-webui-main/docker/docker-compose.yml

@@ -1,31 +0,0 @@
-version: "3.3"
-services:
-  text-generation-webui:
-    build:
-      context: .
-      args:
-        # specify which cuda version your card supports: https://developer.nvidia.com/cuda-gpus
-        TORCH_CUDA_ARCH_LIST: ${TORCH_CUDA_ARCH_LIST}
-        WEBUI_VERSION: ${WEBUI_VERSION}
-    env_file: .env
-    ports:
-      - "${HOST_PORT}:${CONTAINER_PORT}"
-      - "${HOST_API_PORT}:${CONTAINER_API_PORT}"
-    stdin_open: true
-    tty: true
-    volumes:
-      - ./characters:/app/characters
-      - ./extensions:/app/extensions
-      - ./loras:/app/loras
-      - ./models:/app/models
-      - ./presets:/app/presets
-      - ./prompts:/app/prompts
-      - ./softprompts:/app/softprompts
-      - ./training:/app/training
-    deploy:
-      resources:
-        reservations:
-          devices:
-            - driver: nvidia
-              device_ids: ['0']
-              capabilities: [gpu]

text-generation-webui-main/docs/Custom-chat-characters.md

@@ -1,30 +0,0 @@
-Custom chat mode characters are defined by `.yaml` files inside the `characters` folder. An example is included: [Example.yaml](https://github.com/oobabooga/text-generation-webui/blob/main/characters/Example.yaml)
-
-The following fields may be defined:
-
-| Field | Description |
-|-------|-------------|
-| `name` | The character's name. |
-| `context` | A string that appears at the top of the prompt. It usually contains a description of the character's personality. |
-| `greeting` (optional) | The character's opening message when a new conversation is started. |
-| `example_dialogue` (optional) | A few example messages to guide the model. |
-| `your_name` (optional) | Your name. This overwrites what you had previously written in the `Your name` field in the interface. |
-
-#### Special tokens
-
-* `{{char}}` or `<BOT>`: are replaced with the character's name
-* `{{user}}` or `<USER>`: are replaced with your name
-
-These replacements happen when the character is loaded, and they apply to the `context`, `greeting`, and `example_dialogue` fields.
-
-#### How do I add a profile picture for my character?
-
-Put an image with the same name as your character's yaml file into the `characters` folder. For example, if your bot is `Character.yaml`, add `Character.jpg` or `Character.png` to the folder.
-
-#### Is the chat history truncated in the prompt?
-
-Once your prompt reaches the 2048 token limit, old messages will be removed one at a time. The context string will always stay at the top of the prompt and will never get truncated.
-
-#### Pygmalion format characters
-
-These are also supported out of the box. Simply put the JSON file in the `characters` folder, or upload it directly from the web UI by clicking on the "Upload character" tab at the bottom.

text-generation-webui-main/docs/DeepSpeed.md

@@ -1,23 +0,0 @@
-An alternative way of reducing the GPU memory usage of models is to use the `DeepSpeed ZeRO-3` optimization.
-
-With this, I have been able to load a 6b model (GPT-J 6B) with less than 6GB of VRAM. The speed of text generation is very decent and much better than what would be accomplished with `--auto-devices --gpu-memory 6`.
-
-As far as I know, DeepSpeed is only available for Linux at the moment.
-
-### How to use it
-
-1. Install DeepSpeed: 
-
-```
-pip install deepspeed
-```
-
-2. Start the web UI replacing `python` with `deepspeed --num_gpus=1` and adding the `--deepspeed` flag. Example:
-
-```
-deepspeed --num_gpus=1 server.py --deepspeed --chat --model gpt-j-6B
-```
-
-### Learn more
-
-For more information, check out [this comment](https://github.com/oobabooga/text-generation-webui/issues/40#issuecomment-1412038622) by 81300, who came up with the DeepSpeed support in this web UI.

text-generation-webui-main/docs/Docker.md

@@ -1,181 +0,0 @@
-Docker Compose is a way of installing and launching the web UI in an isolated Ubuntu image using only a few commands.
-
-In order to create the image as described in the main README, you must have docker compose 2.17 or higher:
-
-```
-~$ docker compose version
-Docker Compose version v2.17.2
-```
-
-# Intructions by [@loeken](https://github.com/loeken)
-
-- [Ubuntu 22.04](#ubuntu-2204)
-  - [0. youtube video](#0-youtube-video)
-  - [1. update the drivers](#1-update-the-drivers)
-  - [2. reboot](#2-reboot)
-  - [3. install docker](#3-install-docker)
-  - [4. docker \& container toolkit](#4-docker--container-toolkit)
-  - [5. clone the repo](#5-clone-the-repo)
-  - [6. prepare models](#6-prepare-models)
-  - [7. prepare .env file](#7-prepare-env-file)
-  - [8. startup docker container](#8-startup-docker-container)
-- [Manjaro](#manjaro)
-  - [update the drivers](#update-the-drivers)
-  - [reboot](#reboot)
-  - [docker \& container toolkit](#docker--container-toolkit)
-  - [continue with ubuntu task](#continue-with-ubuntu-task)
-- [Windows](#windows)
-  - [0. youtube video](#0-youtube-video-1)
-  - [1. choco package manager](#1-choco-package-manager)
-  - [2. install drivers/dependencies](#2-install-driversdependencies)
-  - [3. install wsl](#3-install-wsl)
-  - [4. reboot](#4-reboot)
-  - [5. git clone \&\& startup](#5-git-clone--startup)
-  - [6. prepare models](#6-prepare-models-1)
-  - [7. startup](#7-startup)
-- [notes](#notes)
-
-# Ubuntu 22.04
-
-## 0. youtube video
-A video walking you through the setup can be found here:
-
-[![oobabooga text-generation-webui setup in docker on ubuntu 22.04](https://img.youtube.com/vi/ELkKWYh8qOk/0.jpg)](https://www.youtube.com/watch?v=ELkKWYh8qOk)
-
-
-## 1. update the drivers
-in the the “software updater” update drivers to the last version of the prop driver.
-
-## 2. reboot
-to switch using to new driver
-
-## 3. install docker
-```bash
-sudo apt update
-sudo apt-get install curl
-sudo mkdir -m 0755 -p /etc/apt/keyrings
-curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo gpg --dearmor -o /etc/apt/keyrings/docker.gpg
-echo \
-  "deb [arch="$(dpkg --print-architecture)" signed-by=/etc/apt/keyrings/docker.gpg] https://download.docker.com/linux/ubuntu \
-  "$(. /etc/os-release && echo "$VERSION_CODENAME")" stable" | \
-  sudo tee /etc/apt/sources.list.d/docker.list > /dev/null
-sudo apt update
-sudo apt-get install docker-ce docker-ce-cli containerd.io docker-buildx-plugin docker-compose-plugin docker-compose -y
-sudo usermod -aG docker $USER
-newgrp docker
-```
-
-## 4. docker & container toolkit
-```bash
-curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg
-echo "deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://nvidia.github.io/libnvidia-container/stable/ubuntu22.04/amd64 /" | \
-sudo tee /etc/apt/sources.list.d/nvidia.list > /dev/null 
-sudo apt update
-sudo apt install nvidia-docker2 nvidia-container-runtime -y
-sudo systemctl restart docker
-```
-
-## 5. clone the repo
-```
-git clone https://github.com/oobabooga/text-generation-webui
-cd text-generation-webui
-```
-
-## 6. prepare models
-download and place the models inside the models folder. tested with:
-
-4bit
-https://github.com/oobabooga/text-generation-webui/pull/530#issuecomment-1483891617
-https://github.com/oobabooga/text-generation-webui/pull/530#issuecomment-1483941105
-
-8bit:
-https://github.com/oobabooga/text-generation-webui/pull/530#issuecomment-1484235789
-
-## 7. prepare .env file
-edit .env values to your needs.
-```bash
-cp .env.example .env
-nano .env
-```
-
-## 8. startup docker container
-```bash
-docker-compose up --build
-```
-
-# Manjaro
-manjaro/arch is similar to ubuntu just the dependency installation is more convenient
-
-## update the drivers
-```bash
-sudo mhwd -a pci nonfree 0300
-```
-## reboot
-```bash
-reboot
-```
-## docker & container toolkit
-```bash
-yay -S docker docker-compose buildkit gcc nvidia-docker
-sudo usermod -aG docker $USER
-newgrp docker
-sudo systemctl restart docker # required by nvidia-container-runtime
-```
-
-## continue with ubuntu task
-continue at [5. clone the repo](#5-clone-the-repo)
-
-# Windows
-## 0. youtube video
-A video walking you through the setup can be found here:
-[![oobabooga text-generation-webui setup in docker on windows 11](https://img.youtube.com/vi/ejH4w5b5kFQ/0.jpg)](https://www.youtube.com/watch?v=ejH4w5b5kFQ)
-
-## 1. choco package manager
-install package manager  (https://chocolatey.org/ )
-```
-Set-ExecutionPolicy Bypass -Scope Process -Force; [System.Net.ServicePointManager]::SecurityProtocol = [System.Net.ServicePointManager]::SecurityProtocol -bor 3072; iex ((New-Object System.Net.WebClient).DownloadString('https://community.chocolatey.org/install.ps1'))
-```
-
-## 2. install drivers/dependencies
-```
-choco install nvidia-display-driver cuda git docker-desktop
-```
-
-## 3. install wsl
-wsl --install
-
-## 4. reboot
-after reboot enter username/password in wsl
-
-## 5. git clone && startup
-clone the repo and edit .env values to your needs.
-```
-cd Desktop
-git clone https://github.com/oobabooga/text-generation-webui
-cd text-generation-webui
-COPY .env.example .env
-notepad .env
-```
-
-## 6. prepare models
-download and place the models inside the models folder. tested with:
-
-4bit https://github.com/oobabooga/text-generation-webui/pull/530#issuecomment-1483891617 https://github.com/oobabooga/text-generation-webui/pull/530#issuecomment-1483941105
-
-8bit: https://github.com/oobabooga/text-generation-webui/pull/530#issuecomment-1484235789
-
-## 7. startup
-```
-docker-compose up
-```
-
-# notes
-
-on older ubuntus you can manually install the docker compose plugin like this:
-```
-DOCKER_CONFIG=${DOCKER_CONFIG:-$HOME/.docker}
-mkdir -p $DOCKER_CONFIG/cli-plugins
-curl -SL https://github.com/docker/compose/releases/download/v2.17.2/docker-compose-linux-x86_64 -o $DOCKER_CONFIG/cli-plugins/docker-compose
-chmod +x $DOCKER_CONFIG/cli-plugins/docker-compose
-export PATH="$HOME/.docker/cli-plugins:$PATH"
-```

text-generation-webui-main/docs/Extensions.md

@@ -1,165 +0,0 @@
-This web UI supports extensions. They are simply files under 
-
-```
-extensions/your_extension_name/script.py
-```
-
-which can be invoked with the 
-
-```
---extension your_extension_name
-```
-
-command-line flag.
-
-## [text-generation-webui-extensions](https://github.com/oobabooga/text-generation-webui-extensions)
-
-The link above contains a directory of user extensions for text-generation-webui.
-
-If you create an extension, you are welcome to host it in a GitHub repository and submit it to the list above.
-
-## Built-in extensions
-
-Most of these have been created by the extremely talented contributors that you can find here: [contributors](https://github.com/oobabooga/text-generation-webui/graphs/contributors?from=2022-12-18&to=&type=a).
-
-|Extension|Description|
-|---------|-----------|
-|[api](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/api)| Creates an API with two endpoints, one for streaming at `/api/v1/stream` port 5005 and another for blocking at `/api/v1/generate` por 5000. This is the main API for this web UI. |
-|[google_translate](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/google_translate)| Automatically translates inputs and outputs using Google Translate.|
-|[character_bias](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/character_bias)| Just a very simple example that biases the bot's responses in chat mode.|
-|[gallery](https://github.com/oobabooga/text-generation-webui/blob/main/extensions/gallery/)| Creates a gallery with the chat characters and their pictures. |
-|[silero_tts](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/silero_tts)| Text-to-speech extension using [Silero](https://github.com/snakers4/silero-models). When used in chat mode, it replaces the responses with an audio widget. |
-|[elevenlabs_tts](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/elevenlabs_tts)| Text-to-speech extension using the [ElevenLabs](https://beta.elevenlabs.io/) API. You need an API key to use it. |
-|[send_pictures](https://github.com/oobabooga/text-generation-webui/blob/main/extensions/send_pictures/)| Creates an image upload field that can be used to send images to the bot in chat mode. Captions are automatically generated using BLIP. |
-|[whisper_stt](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/whisper_stt)| Allows you to enter your inputs in chat mode using your microphone. |
-|[sd_api_pictures](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/sd_api_pictures)| Allows you to request pictures from the bot in chat mode, which will be generated using the AUTOMATIC1111 Stable Diffusion API. See examples [here](https://github.com/oobabooga/text-generation-webui/pull/309). |
-|[llava](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/llava) | Adds LLaVA multimodal model support. For detailed description see [README.md](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/llava/README.md) in the extension directory. |
-
-## How to write an extension
-
-`script.py` has access to all variables in the UI through the `modules.shared` module, and it may define the following functions:
-
-| Function        | Description |
-|-------------|-------------|
-| `def ui()` | Creates custom gradio elements when the UI is launched. | 
-| `def input_modifier(string)`  | Modifies the input string before it enters the model. In chat mode, it is applied to the user message. Otherwise, it is applied to the entire prompt. |
-| `def output_modifier(string)`  | Modifies the output string before it is presented in the UI. In chat mode, it is applied to the bot's reply. Otherwise, it is applied to the entire output. |
-| `def bot_prefix_modifier(string)`  | Applied in chat mode to the prefix for the bot's reply (more on that below). |
-| `def custom_generate_chat_prompt(...)` | Overrides the prompt generator in chat mode. |
-| `def tokenizer_modifier(state, prompt, input_ids, input_embeds)` | Modifies the `input_ids`/`input_embeds` fed to the model. Should return `prompt`, `input_ids`, `input_embeds`. See `llava` extension for an example |
-
-Additionally, the script may define two special global variables:
-
-#### `params` dictionary
-
-```python
-params = {
-    "language string": "ja",
-}
-```
-
-This dicionary can be used to make the extension parameters customizable by adding entries to a `settings.json` file like this:
-
-```python
-"google_translate-language string": "fr",
-``` 
-
-#### `input_hijack` dictionary
-
-```python
-input_hijack = {
-    'state': False,
-    'value': ["", ""]
-}
-```
-This is only relevant in chat mode. If your extension sets `input_hijack['state']` to `True` at any moment, the next call to `modules.chat.chatbot_wrapper` will use the values inside `input_hijack['value']` as the user input for text generation. See the `send_pictures` extension above for an example. 
-
-Additionally, your extension can set the value to be a callback, in the form of `def cb(text: str, visible_text: str) -> [str, str]`. See the `llava` extension above for an example.
-
-## The `bot_prefix_modifier`
-
-In chat mode, this function modifies the prefix for a new bot message. For instance, if your bot is named `Marie Antoinette`, the default prefix for a new message will be
-
-```
-Marie Antoinette:
-```
-
-Using `bot_prefix_modifier`, you can change it to:
-
-```
-Marie Antoinette: *I am very enthusiastic*
-```
- 
-Marie Antoinette will become very enthusiastic in all her messages.
-
-## Using multiple extensions at the same time
-
-In order to use your extension, you must start the web UI with the `--extensions` flag followed by the name of your extension (the folder under `text-generation-webui/extension` where `script.py` resides).
-
-You can activate more than one extension at a time by providing their names separated by spaces. The input, output and bot prefix modifiers will be applied in the specified order. For `custom_generate_chat_prompt`, only the first declaration encountered will be used and the rest will be ignored.
-
-```
-python server.py --extensions enthusiasm translate # First apply enthusiasm, then translate
-python server.py --extensions translate enthusiasm # First apply translate, then enthusiasm
-```
-
-## `custom_generate_chat_prompt` example
-
-Below is an extension that just reproduces the default prompt generator in `modules/chat.py`. You can modify it freely to come up with your own prompts in chat mode.
-
-```python
-def custom_generate_chat_prompt(user_input, state, **kwargs):
-    impersonate = kwargs['impersonate'] if 'impersonate' in kwargs else False
-    _continue = kwargs['_continue'] if '_continue' in kwargs else False
-    also_return_rows = kwargs['also_return_rows'] if 'also_return_rows' in kwargs else False
-    is_instruct = state['mode'] == 'instruct'
-    rows = [f"{state['context'].strip()}\n"]
-
-    # Finding the maximum prompt size
-    chat_prompt_size = state['chat_prompt_size']
-    if shared.soft_prompt:
-        chat_prompt_size -= shared.soft_prompt_tensor.shape[1]
-    max_length = min(get_max_prompt_length(state), chat_prompt_size)
-
-    if is_instruct:
-        prefix1 = f"{state['name1']}\n"
-        prefix2 = f"{state['name2']}\n"
-    else:
-        prefix1 = f"{state['name1']}: "
-        prefix2 = f"{state['name2']}: "
-
-    i = len(shared.history['internal']) - 1
-    while i >= 0 and len(encode(''.join(rows))[0]) < max_length:
-        if _continue and i == len(shared.history['internal']) - 1:
-            rows.insert(1, f"{prefix2}{shared.history['internal'][i][1]}")
-        else:
-            rows.insert(1, f"{prefix2}{shared.history['internal'][i][1].strip()}{state['end_of_turn']}\n")
-        string = shared.history['internal'][i][0]
-        if string not in ['', '<|BEGIN-VISIBLE-CHAT|>']:
-            rows.insert(1, f"{prefix1}{string.strip()}{state['end_of_turn']}\n")
-        i -= 1
-
-    if impersonate:
-        rows.append(f"{prefix1.strip() if not is_instruct else prefix1}")
-        limit = 2
-    elif _continue:
-        limit = 3
-    else:
-        # Adding the user message
-        user_input = fix_newlines(user_input)
-        if len(user_input) > 0:
-            rows.append(f"{prefix1}{user_input}{state['end_of_turn']}\n")
-
-        # Adding the Character prefix
-        rows.append(apply_extensions(f"{prefix2.strip() if not is_instruct else prefix2}", "bot_prefix"))
-        limit = 3
-
-    while len(rows) > limit and len(encode(''.join(rows))[0]) >= max_length:
-        rows.pop(1)
-    prompt = ''.join(rows)
-
-    if also_return_rows:
-        return prompt, rows
-    else:
-        return prompt
-```

text-generation-webui-main/docs/FlexGen.md

@@ -1,64 +0,0 @@
->FlexGen is a high-throughput generation engine for running large language models with limited GPU memory (e.g., a 16GB T4 GPU or a 24GB RTX3090 gaming card!).
-
-https://github.com/FMInference/FlexGen
-
-## Installation
-
-No additional installation steps are necessary. FlexGen is in the `requirements.txt` file for this project.
-
-## Converting a model
-
-FlexGen only works with the OPT model, and it needs to be converted to numpy format before starting the web UI:
-
-```
-python convert-to-flexgen.py models/opt-1.3b/
-```
-
-The output will be saved to `models/opt-1.3b-np/`.
-
-## Usage
-
-The basic command is the following:
-
-```
-python server.py --model opt-1.3b  --flexgen
-```
-
-For large models, the RAM usage may be too high and your computer may freeze. If that happens, you can try this:
-
-```
-python server.py --model opt-1.3b  --flexgen --compress-weight
-```
-
-With this second command, I was able to run both OPT-6.7b and OPT-13B with **2GB VRAM**, and the speed was good in both cases.
-
-You can also manually set the offload strategy with
-
-```
-python server.py --model opt-1.3b  --flexgen --percent 0 100 100 0 100 0
-```
-
-where the six numbers after `--percent` are:
-
-```
-the percentage of weight on GPU
-the percentage of weight on CPU
-the percentage of attention cache on GPU
-the percentage of attention cache on CPU
-the percentage of activations on GPU
-the percentage of activations on CPU
-```
-
-You should typically only change the first two numbers. If their sum is less than 100, the remaining layers will be offloaded to the disk, by default into the `text-generation-webui/cache` folder.
-
-## Performance
-
-In my experiments with OPT-30B using a RTX 3090 on Linux, I have obtained these results:
-
-* `--flexgen --compress-weight --percent 0 100 100 0 100 0`: 0.99 seconds per token.
-* `--flexgen --compress-weight --percent 100 0 100 0 100 0`: 0.765 seconds per token.
-
-## Limitations
-
-* Only works with the OPT models.
-* Only two generation parameters are available: `temperature` and `do_sample`.

text-generation-webui-main/docs/GPTQ-models-(4-bit-mode).md

@@ -1,140 +0,0 @@
-In 4-bit mode, models are loaded with just 25% of their regular VRAM usage. So LLaMA-7B fits into a 6GB GPU, and LLaMA-30B fits into a 24GB GPU.
-
-This is possible thanks to [@qwopqwop200](https://github.com/qwopqwop200/GPTQ-for-LLaMa)'s adaptation of the GPTQ algorithm for LLaMA: https://github.com/qwopqwop200/GPTQ-for-LLaMa
-
-GPTQ is a clever quantization algorithm that lightly reoptimizes the weights during quantization so that the accuracy loss is compensated relative to a round-to-nearest quantization. See the paper for more details: https://arxiv.org/abs/2210.17323
-
-## GPTQ-for-LLaMa branches
-
-Different branches of GPTQ-for-LLaMa are available:
-
-| Branch | Comment |
-|----|----|
-| [Old CUDA branch (recommended)](https://github.com/oobabooga/GPTQ-for-LLaMa/) | The fastest branch, works on Windows and Linux. |
-| [Up-to-date triton branch](https://github.com/qwopqwop200/GPTQ-for-LLaMa) | Slightly more precise than the old CUDA branch from 13b upwards, significantly more precise for 7b. 2x slower for small context size and only works on Linux. |
-| [Up-to-date CUDA branch](https://github.com/qwopqwop200/GPTQ-for-LLaMa/tree/cuda) | As precise as the up-to-date triton branch, 10x slower than the old cuda branch for small context size. |
-
-Overall, I recommend using the old CUDA branch. It is included by default in the one-click-installer for this web UI.
-
-## Installation
-
-### Step 0: install nvcc
-
-```
-conda activate textgen
-conda install -c conda-forge cudatoolkit-dev
-```
-
-The command above takes some 10 minutes to run and shows no progress bar or updates along the way.
-
-See this issue for more details: https://github.com/oobabooga/text-generation-webui/issues/416#issuecomment-1475078571
-
-### Step 1: install GPTQ-for-LLaMa
-
-Clone the GPTQ-for-LLaMa repository into the `text-generation-webui/repositories` subfolder and install it:
-
-```
-mkdir repositories
-cd repositories
-git clone https://github.com/oobabooga/GPTQ-for-LLaMa.git -b cuda
-cd GPTQ-for-LLaMa
-python setup_cuda.py install
-```
-
-You are going to need to have a C++ compiler installed into your system for the last command. On Linux, `sudo apt install build-essential` or equivalent is enough.
-
-If you want to you to use the up-to-date CUDA or triton branches instead of the old CUDA branch, use these commands:
-
-```
-cd repositories
-rm -r GPTQ-for-LLaMa
-pip uninstall -y quant-cuda
-git clone https://github.com/qwopqwop200/GPTQ-for-LLaMa.git -b cuda
-...
-```
-
-```
-cd repositories
-rm -r GPTQ-for-LLaMa
-pip uninstall -y quant-cuda
-git clone https://github.com/qwopqwop200/GPTQ-for-LLaMa.git -b triton
-...
-```
-
-
-https://github.com/qwopqwop200/GPTQ-for-LLaMa
-
-### Step 2: get the pre-converted weights
-
-* Converted without `group-size` (better for the 7b model): https://github.com/oobabooga/text-generation-webui/pull/530#issuecomment-1483891617
-* Converted with `group-size` (better from 13b upwards): https://github.com/oobabooga/text-generation-webui/pull/530#issuecomment-1483941105 
-
-⚠️ The tokenizer files in the sources above may be outdated. Make sure to obtain the universal LLaMA tokenizer as described [here](https://github.com/oobabooga/text-generation-webui/blob/main/docs/LLaMA-model.md#option-1-pre-converted-weights).
-
-### Step 3: Start the web UI:
-
-For the models converted without `group-size`:
-
-```
-python server.py --model llama-7b-4bit 
-```
-
-For the models converted with `group-size`:
-
-```
-python server.py --model llama-13b-4bit-128g 
-```
-
-The command-line flags `--wbits` and `--groupsize` are automatically detected based on the folder names, but you can also specify them manually like 
-
-```
-python server.py --model llama-13b-4bit-128g --wbits 4 --groupsize 128
-```
-
-## CPU offloading
-
-It is possible to offload part of the layers of the 4-bit model to the CPU with the `--pre_layer` flag. The higher the number after `--pre_layer`, the more layers will be allocated to the GPU.
-
-With this command, I can run llama-7b with 4GB VRAM:
-
-```
-python server.py --model llama-7b-4bit --pre_layer 20
-```
-
-This is the performance:
-
-```
-Output generated in 123.79 seconds (1.61 tokens/s, 199 tokens)
-```
-
-## Using LoRAs in 4-bit mode
-
-At the moment, this feature is not officially supported by the relevant libraries, but a patch exists and is supported by this web UI: https://github.com/johnsmith0031/alpaca_lora_4bit
-
-In order to use it:
-
-1. Make sure that your requirements are up to date:
-
-```
-cd text-generation-webui
-pip install -r requirements.txt --upgrade
-```
-
-2. Clone `johnsmith0031/alpaca_lora_4bit` into the repositories folder:
-
-```
-cd text-generation-webui/repositories
-git clone https://github.com/johnsmith0031/alpaca_lora_4bit
-```
-
-3. Install https://github.com/sterlind/GPTQ-for-LLaMa with this command:
-
-```
-pip install git+https://github.com/sterlind/GPTQ-for-LLaMa.git@lora_4bit
-```
-
-4. Start the UI with the `--monkey-patch` flag:
-
-```
-python server.py --model llama-7b-4bit-128g --listen --lora tloen_alpaca-lora-7b --monkey-patch
-```

text-generation-webui-main/docs/LLaMA-model.md

@@ -1,45 +0,0 @@
-LLaMA is a Large Language Model developed by Meta AI. 
-
-It was trained on more tokens than previous models. The result is that the smallest version with 7 billion parameters has similar performance to GPT-3 with 175 billion parameters.
-
-This guide will cover usage through the official `transformers` implementation. For 4-bit mode, head over to [GPTQ models (4 bit mode)
-](GPTQ-models-(4-bit-mode).md).
-
-## Getting the weights
-
-### Option 1: pre-converted weights
-
-* Torrent: https://github.com/oobabooga/text-generation-webui/pull/530#issuecomment-1484235789
-* Direct download: https://huggingface.co/Neko-Institute-of-Science
-
-⚠️ The tokenizers for the sources above and also for many LLaMA fine-tunes available on Hugging Face may be outdated, so I recommend downloading the following universal LLaMA tokenizer: 
-
-```
-python download-model.py oobabooga/llama-tokenizer
-```
-
-Once downloaded, it will be automatically applied to **every** `LlamaForCausalLM` model that you try to load.
-
-### Option 2: convert the weights yourself
-
-1. Install the `protobuf` library:
-
-```
-pip install protobuf
-```
-
-2. Use the script below to convert the model in `.pth` format that you, a fellow academic, downloaded using Meta's official link:
-
-### [convert_llama_weights_to_hf.py](https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/convert_llama_weights_to_hf.py)
-
-```
-python convert_llama_weights_to_hf.py --input_dir /path/to/LLaMA --model_size 7B --output_dir /tmp/outputs/llama-7b
-```
-
-3. Move the `llama-7b` folder inside your `text-generation-webui/models` folder.
-
-## Starting the web UI
-
-```python
-python server.py --model llama-7b
-```

text-generation-webui-main/docs/Low-VRAM-guide.md

@@ -1,51 +0,0 @@
-If you GPU is not large enough to fit a model, try these in the following order:
-
-### Load the model in 8-bit mode
-
-```
-python server.py --load-in-8bit
-```
-
-This reduces the memory usage by half with no noticeable loss in quality. Only newer GPUs support 8-bit mode.
-
-### Split the model across your GPU and CPU
-
-```
-python server.py --auto-devices
-```
-
-If you can load the model with this command but it runs out of memory when you try to generate text, try increasingly limiting the amount of memory allocated to the GPU until the error stops happening:
-
-```
-python server.py --auto-devices --gpu-memory 10
-python server.py --auto-devices --gpu-memory 9
-python server.py --auto-devices --gpu-memory 8
-...
-```
-
-where the number is in GiB.
-
-For finer control, you can also specify the unit in MiB explicitly:
-
-```
-python server.py --auto-devices --gpu-memory 8722MiB
-python server.py --auto-devices --gpu-memory 4725MiB
-python server.py --auto-devices --gpu-memory 3500MiB
-...
-```
-
-Additionally, you can also set the `--no-cache` value to reduce the GPU usage while generating text at a performance cost. This may allow you to set a higher value for `--gpu-memory`, resulting in a net performance gain.
-
-### Send layers to a disk cache
-
-As a desperate last measure, you can split the model across your GPU, CPU, and disk:
-
-```
-python server.py --auto-devices --disk
-```
-
-With this, I am able to load a 30b model into my RTX 3090, but it takes 10 seconds to generate 1 word.
-
-### DeepSpeed (experimental)
-
-An experimental alternative to all of the above is to use DeepSpeed: [guide](DeepSpeed.md).

text-generation-webui-main/docs/README.md

@@ -1,19 +0,0 @@
-# text-generation-webui manual
-
-## Table of contents
-
-* [Custom-chat-characters](Custom-chat-characters.md)
-* [Docker Compose](Docker.md)
-* [DeepSpeed](DeepSpeed.md)
-* [Extensions](Extensions.md)
-* [FlexGen](FlexGen.md)
-* [GPTQ-models-(4-bit-mode)](GPTQ-models-(4-bit-mode).md)
-* [llama.cpp-models](llama.cpp-models.md)
-* [LLaMA-model](LLaMA-model.md)
-* [Low-VRAM-guide](Low-VRAM-guide.md)
-* [RWKV-model](RWKV-model.md)
-* [Spell-book](Spell-book.md)
-* [System-requirements](System-requirements.md)
-* [Using-LoRAs](Using-LoRAs.md)
-* [Windows-installation-guide](Windows-installation-guide.md)
-* [WSL-installation-guide](WSL-installation-guide.md)

text-generation-webui-main/docs/RWKV-model.md

@@ -1,54 +0,0 @@
-> RWKV: RNN with Transformer-level LLM Performance
->
-> It combines the best of RNN and transformer - great performance, fast inference, saves VRAM, fast training, "infinite" ctx_len, and free sentence embedding (using the final hidden state).
-
-https://github.com/BlinkDL/RWKV-LM
-
-https://github.com/BlinkDL/ChatRWKV
-
-## Using RWKV in the web UI
-
-#### 1. Download the model
-
-It is available in different sizes:
-
-* https://huggingface.co/BlinkDL/rwkv-4-pile-3b/
-* https://huggingface.co/BlinkDL/rwkv-4-pile-7b/
-* https://huggingface.co/BlinkDL/rwkv-4-pile-14b/
-
-There are also older releases with smaller sizes like:
-
-* https://huggingface.co/BlinkDL/rwkv-4-pile-169m/resolve/main/RWKV-4-Pile-169M-20220807-8023.pth
-
-Download the chosen `.pth` and put it directly in the `models` folder. 
-
-#### 2. Download the tokenizer
-
-[20B_tokenizer.json](https://raw.githubusercontent.com/BlinkDL/ChatRWKV/main/v2/20B_tokenizer.json)
-
-Also put it directly in the `models` folder. Make sure to not rename it. It should be called `20B_tokenizer.json`.
-
-#### 3. Launch the web UI
-
-No additional steps are required. Just launch it as you would with any other model.
-
-```
-python server.py --listen  --no-stream --model RWKV-4-Pile-169M-20220807-8023.pth
-```
-
-## Setting a custom strategy
-
-It is possible to have very fine control over the offloading and precision for the model with the `--rwkv-strategy` flag. Possible values include:
-
-```
-"cpu fp32" # CPU mode
-"cuda fp16" # GPU mode with float16 precision
-"cuda fp16 *30 -> cpu fp32" # GPU+CPU offloading. The higher the number after *, the higher the GPU allocation.
-"cuda fp16i8" # GPU mode with 8-bit precision
-```
-
-See the README for the PyPl package for more details: https://pypi.org/project/rwkv/
-
-## Compiling the CUDA kernel
-
-You can compile the CUDA kernel for the model with `--rwkv-cuda-on`. This should improve the performance a lot but I haven't been able to get it to work yet.

text-generation-webui-main/docs/Spell-book.md

@@ -1,107 +0,0 @@
-You have now entered a hidden corner of the internet.
-
-A confusing yet intriguing realm of paradoxes and contradictions.
-
-A place where you will find out that what you thought you knew, you in fact didn't know, and what you didn't know was in front of you all along.
-
-![](https://i.pinimg.com/originals/6e/e2/7b/6ee27bad351d3aca470d80f1033ba9c6.jpg)
-
-*In other words, here I will document little-known facts about this web UI that I could not find another place for in the wiki.*
-
-#### You can train LoRAs in CPU mode
-
-Load the web UI with
-
-```
-python server.py --cpu
-```
-
-and start training the LoRA from the training tab as usual.
-
-#### 8-bit mode works with CPU offloading
-
-```
-python server.py --load-in-8bit --gpu-memory 4000MiB
-```
-
-#### `--pre_layer`, and not `--gpu-memory`, is the right way to do CPU offloading with 4-bit models
-
-```
-python server.py --wbits 4 --groupsize 128 --pre_layer 20
-```
-
-#### Models can be loaded in 32-bit, 16-bit, 8-bit, and 4-bit modes
-
-```
-python server.py --cpu
-python server.py
-python server.py --load-in-8bit
-python server.py --wbits 4
-```
-
-#### The web UI works with any version of GPTQ-for-LLaMa
-
-Including the up to date triton and cuda branches. But you have to delete the `repositories/GPTQ-for-LLaMa` folder and reinstall the new one every time:
-
-```
-cd text-generation-webui/repositories
-rm -r GPTQ-for-LLaMa
-pip uninstall quant-cuda
-git clone https://github.com/oobabooga/GPTQ-for-LLaMa -b cuda # or any other repository and branch
-cd GPTQ-for-LLaMa
-python setup_cuda.py install
-```
-
-#### Instruction-following templates are represented as chat characters
-
-https://github.com/oobabooga/text-generation-webui/tree/main/characters/instruction-following
-
-#### The right way to run Alpaca, Open Assistant, Vicuna, etc is Instruct mode, not normal chat mode
-
-Otherwise the prompt will not be formatted correctly.
-
-1. Start the web UI with
-
-```
-python server.py --chat
-```
-
-2. Click on the "instruct" option under "Chat modes"
-
-3. Select the correct template in the hidden dropdown menu that will become visible. 
-
-#### Notebook mode is best mode
-
-Ascended individuals have realized that notebook mode is the superset of chat mode and can do chats with ultimate flexibility, including group chats, editing replies, starting a new bot reply in a given way, and impersonating.
-
-#### RWKV is a RNN 
-
-Most models are transformers, but not RWKV, which is a RNN. It's a great model.
-
-#### `--gpu-memory` is not a hard limit on the GPU memory
-
-It is simply a parameter that is passed to the `accelerate` library while loading the model. More memory will be allocated during generation. That's why this parameter has to be set to less than your total GPU memory.
-
-#### Contrastive search perhaps the best preset
-
-But it uses a ton of VRAM.
-
-#### You can check the sha256sum of downloaded models with the download script
-
-```
-python download-model.py facebook/galactica-125m --check
-```
-
-#### The download script continues interrupted downloads by default
-
-It doesn't start over.
-
-#### You can download models with multiple threads
-
-```
-python download-model.py facebook/galactica-125m --threads 8
-```
-
-#### LoRAs work in 4-bit mode
-
-You need to follow [these instructions](GPTQ-models-(4-bit-mode).md#using-loras-in-4-bit-mode) and then start the web UI with the `--monkey-patch` flag.

text-generation-webui-main/docs/System-requirements.md

@@ -1,42 +0,0 @@
-These are the VRAM and RAM requirements (in MiB) to run some examples of models **in 16-bit (default) precision**:
-
-| model                  |   VRAM (GPU) |     RAM |
-|:-----------------------|-------------:|--------:|
-| arxiv_ai_gpt2          |      1512.37 | 5824.2  |
-| blenderbot-1B-distill  |      2441.75 | 4425.91 |
-| opt-1.3b               |      2509.61 | 4427.79 |
-| gpt-neo-1.3b           |      2605.27 | 5851.58 |
-| opt-2.7b               |      5058.05 | 4863.95 |
-| gpt4chan_model_float16 |     11653.7  | 4437.71 |
-| gpt-j-6B               |     11653.7  | 5633.79 |
-| galactica-6.7b         |     12697.9  | 4429.89 |
-| opt-6.7b               |     12700    | 4368.66 |
-| bloomz-7b1-p3          |     13483.1  | 4470.34 |
-
-#### GPU mode with 8-bit precision
-
-Allows you to load models that would not normally fit into your GPU. Enabled by default for 13b and 20b models in this web UI.
-
-| model          |   VRAM (GPU) |     RAM |
-|:---------------|-------------:|--------:|
-| opt-13b        |      12528.1 | 1152.39 |
-| gpt-neox-20b   |      20384   | 2291.7  |
-
-#### CPU mode (32-bit precision)
-
-A lot slower, but does not require a GPU. 
-
-On my i5-12400F, 6B models take around 10-20 seconds to respond in chat mode, and around 5 minutes to generate a 200 tokens completion. 
-
-| model                  |      RAM |
-|:-----------------------|---------:|
-| arxiv_ai_gpt2          |  4430.82 |
-| gpt-neo-1.3b           |  6089.31 |
-| opt-1.3b               |  8411.12 |
-| blenderbot-1B-distill  |  8508.16 |
-| opt-2.7b               | 14969.3  |
-| bloomz-7b1-p3          | 21371.2  |
-| gpt-j-6B               | 24200.3  |
-| gpt4chan_model         | 24246.3  |
-| galactica-6.7b         | 26561.4  |
-| opt-6.7b               | 29596.6  |

text-generation-webui-main/docs/Training-LoRAs.md

@@ -1,167 +0,0 @@
-## Training Your Own LoRAs
-
-The WebUI seeks to make training your own LoRAs as easy as possible. It comes down to just a few simple steps:
-
-### **Step 1**: Make a plan.
-- What base model do you want to use? The LoRA you make has to be matched up to a single architecture (eg LLaMA-13B) and cannot be transferred to others (eg LLaMA-7B, StableLM, etc. would all be different). Derivatives of the same model (eg Alpaca finetune of LLaMA-13B) might be transferrable, but even then it's best to train exactly on what you plan to use.
-- What model format do you want? At time of writing, 8-bit models are most stable, and 4-bit are supported but experimental. In the near future it is likely that 4-bit will be the best option for most users.
-- What are you training it on? Do you want it to learn real information, a simple format, ...?
-
-### **Step 2**: Gather a dataset.
-- If you use a dataset similar to the [Alpaca](https://github.com/gururise/AlpacaDataCleaned/blob/main/alpaca_data_cleaned.json) format, that is natively supported by the `Formatted Dataset` input in the WebUI, with premade formatter options.
-- If you use a dataset that isn't matched to Alpaca's format, but uses the same basic JSON structure, you can make your own format file by copying `training/formats/alpaca-format.json` to a new file and [editing its content](#format-files).
-- If you can get the dataset into a simple text file, that works too! You can train using the `Raw text file` input option.
-    - This means you can for example just copy/paste a chatlog/documentation page/whatever you want, shove it in a plain text file, and train on it.
-- If you use a structured dataset not in this format, you may have to find an external way to convert it - or open an issue to request native support.
-
-### **Step 3**: Do the training.
-- **3.1**: Load the WebUI, and your model.
-    - Make sure you don't have any LoRAs already loaded (unless you want to train for multi-LoRA usage).
-- **3.2**: Open the `Training` tab at the top, `Train LoRA` sub-tab.
-- **3.3**: Fill in the name lof the LoRA, select your dataset in the dataset options.
-- **3.4**: Select other parameters to your preference. See [parameters below](#parameters).
-- **3.5**: click `Start LoRA Training`, and wait.
-    - It can take a few hours for a large dataset, or just a few minute if doing a small run.
-    - You may want to monitor your [loss value](#loss) while it goes.
-
-### **Step 4**: Evaluate your results.
-- Load the LoRA under the Models Tab.
-- You can go test-drive it on the `Text generation` tab, or you can use the `Perplexity evaluation` sub-tab of the `Training` tab.
-- If you used the `Save every n steps` option, you can grab prior copies of the model from sub-folders within the LoRA model's folder and try them instead.
-
-### **Step 5**: Re-run if you're unhappy.
-- Make sure to unload the LoRA before training it.
-- You can simply resume a prior run - use `Copy parameters from` to select your LoRA, and edit parameters. Note that you cannot change the `Rank` of an already created LoRA.
-    - If you want to resume from a checkpoint saved along the way, simply copy the contents of the checkpoint folder into the LoRA's folder.
-    - (Note: `adapter_model.bin` is the important file that holds the actual LoRA content).
-    - This will start Learning Rate and Steps back to the start. If you want to resume as if you were midway through, you can adjust your Learning Rate to the last reported LR in logs and reduce your epochs.
-- Or, you can start over entirely if you prefer.
-- If your model is producing corrupted outputs, you probably need to start over and use a lower Learning Rate.
-- If your model isn't learning detailed information but you want it to, you might need to just run more epochs, or you might need a higher Rank.
-- If your model is enforcing a format you didn't want, you may need to tweak your dataset, or start over and not train as far.
-
-## Format Files
-
-If using JSON formatted datasets, they are presumed to be in the following approximate format:
-
-```json
-[
-    {
-        "somekey": "somevalue",
-        "key2": "value2"
-    },
-    {
-        // etc
-    }
-]
-```
-
-Where the keys (eg `somekey`, `key2` above) are standardized, and relatively consistent across the dataset, and the values (eg `somevalue`, `value2`) contain the content actually intended to be trained.
-
-For Alpaca, the keys are `instruction`, `input`, and `output`, wherein `input` is sometimes blank.
-
-A simple format file for Alpaca to be used as a chat bot is:
-
-```json
-{
-    "instruction,output": "User: %instruction%\nAssistant: %output%",
-    "instruction,input,output": "User: %instruction%: %input%\nAssistant: %output%"
-}
-```
-
-Note that the keys (eg `instruction,output`) are a comma-separated list of dataset keys, and the values are a simple string that use those keys with `%%`.
-
-So for example if a dataset has `"instruction": "answer my question"`, then the format file's `User: %instruction%\n` will be automatically filled in as `User: answer my question\n`.
-
-If you have different sets of key inputs, you can make your own format file to match it. This format-file is designed to be as simple as possible to enable easy editing to match your needs.
-
-## Parameters
-
-The basic purpose and function of each parameter is documented on-page in the WebUI, so read through them in the UI to understand your options.
-
-That said, here's a guide to the most important parameter choices you should consider:
-
-### VRAM
-
-- First, you must consider your VRAM availability.
-    - Generally, under default settings, VRAM usage for training with default parameters is very close to when generating text (with 1000+ tokens of context) (ie, if you can generate text, you can train LoRAs).
-        - Note: worse by default in the 4-bit monkeypatch currently. Reduce `Micro Batch Size` to `1` to restore this to expectations.
-    - If you have VRAM to spare, setting higher batch sizes will use more VRAM and get you better quality training in exchange.
-    - If you have large data, setting a higher cutoff length may be beneficial, but will cost significant VRAM. If you can spare some, set your batch size to `1` and see how high you can push your cutoff length.
-    - If you're low on VRAM, reducing batch size or cutoff length will of course improve that.
-    - Don't be afraid to just try it and see what happens. If it's too much, it will just error out, and you can lower settings and try again.
-
-### Rank
-
-- Second, you want to consider the amount of learning you want.
-    - For example, you may wish to just learn a dialogue format (as in the case of Alpaca) in which case setting a low `Rank` value (32 or lower) works great.
-    - Or, you might be training on project documentation you want the bot to understand and be able to understand questions about, in which case the higher the rank, the better.
-    - Generally, higher Rank = more precise learning = more total content learned = more VRAM usage while training.
-
-### Learning Rate and Epochs
-
-- Third, how carefully you want it to be learned.
-    - In other words, how okay or not you are with the model losing unrelated understandings.
-    - You can control this with 3 key settings: the Learning Rate, its scheduler, and your total epochs.
-    - The learning rate controls how much change is made to the model by each token it sees.
-        - It's in scientific notation normally, so for example `3e-4` means `3 * 10^-4` which is `0.0003`. The number after `e-` controls how many `0`s are in the number.
-        - Higher values let training run faster, but also are more likely to corrupt prior data in the model.
-    - You essentially have two variables to balance: the LR, and Epochs.
-        - If you make LR higher, you can set Epochs equally lower to match. High LR + low epochs = very fast, low quality training.
-        - If you make LR low, set epochs high. Low LR + high epochs = slow but high-quality training.
-    - The scheduler controls change-over-time as you train - it starts high, and then goes low. This helps balance getting data in, and having decent quality, at the same time.
-        - You can see graphs of the different scheduler options [in the HuggingFace docs here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_1/en/main_classes/optimizer_schedules#transformers.SchedulerType)
-
-## Loss
-
-When you're running training, the WebUI's console window will log reports that include, among other things, a numeric value named `Loss`. It will start as a high number, and gradually get lower and lower as it goes.
-
-"Loss" in the world of AI training theoretically means "how close is the model to perfect", with `0` meaning "absolutely perfect". This is calculated by measuring the difference between the model outputting exactly the text you're training it to output, and what it actually outputs.
-
-In practice, a good LLM should have a very complex variable range of ideas running in its artificial head, so a loss of `0` would indicate that the model has broken and forgotten to how think about anything other than what you trained it.
-
-So, in effect, Loss is a balancing game: you want to get it low enough that it understands your data, but high enough that it isn't forgetting everything else. Generally, if it goes below `1.0`, it's going to start forgetting its prior memories, and you should stop training. In some cases you may prefer to take it as low as `0.5` (if you want it to be very very predictable). Different goals have different needs, so don't be afraid to experiment and see what works best for you.
-
-Note: if you see Loss start at or suddenly jump to exactly `0`, it is likely something has gone wrong in your training process (eg model corruption).
-
-## Note: 4-Bit Monkeypatch
-
-The [4-bit LoRA monkeypatch](GPTQ-models-(4-bit-mode).md#using-loras-in-4-bit-mode) works for training, but has side effects:
-- VRAM usage is higher currently. You can reduce the `Micro Batch Size` to `1` to compensate.
-- Models do funky things. LoRAs apply themselves, or refuse to apply, or spontaneously error out, or etc. It can be helpful to reload base model or restart the WebUI between training/usage to minimize chances of anything going haywire.
-- Loading or working with multiple LoRAs at the same time doesn't currently work.
-- Generally, recognize and treat the monkeypatch as the dirty temporary hack it is - it works, but isn't very stable. It will get better in time when everything is merged upstream for full official support.
-
-## Legacy notes
-
-LoRA training was contributed by [mcmonkey4eva](https://github.com/mcmonkey4eva) in PR [#570](https://github.com/oobabooga/text-generation-webui/pull/570).
-
-### Using the original alpaca-lora code
-
-Kept here for reference. The Training tab has much more features than this method.
-
-```
-conda activate textgen
-git clone https://github.com/tloen/alpaca-lora
-```
-
-Edit those two lines in `alpaca-lora/finetune.py` to use your existing model folder instead of downloading everything from decapoda:
-
-```
-model = LlamaForCausalLM.from_pretrained(
-    "models/llama-7b",
-    load_in_8bit=True,
-    device_map="auto",
-)
-tokenizer = LlamaTokenizer.from_pretrained(
-    "models/llama-7b", add_eos_token=True
-)
-```
-
-Run the script with:
-
-```
-python finetune.py
-```
-
-It just works. It runs at 22.32s/it, with 1170 iterations in total, so about 7 hours and a half for training a LoRA. RTX 3090, 18153MiB VRAM used, drawing maximum power (350W, room heater mode).

text-generation-webui-main/docs/Using-LoRAs.md

@@ -1,55 +0,0 @@
-Based on https://github.com/tloen/alpaca-lora
-
-## Instructions
-
-1. Download a LoRA, for instance:
-
-```
-python download-model.py tloen/alpaca-lora-7b
-```
-
-2. Load the LoRA. 16-bit, 8-bit, and CPU modes work:
-
-```
-python server.py --model llama-7b-hf --lora alpaca-lora-7b
-python server.py --model llama-7b-hf --lora alpaca-lora-7b --load-in-8bit
-python server.py --model llama-7b-hf --lora alpaca-lora-7b --cpu
-```
-
-* For using LoRAs in 4-bit mode, follow [these special instructions](GPTQ-models-(4-bit-mode).md#using-loras-in-4-bit-mode).
-
-* Instead of using the `--lora` command-line flag, you can also select the LoRA in the "Parameters" tab of the interface.
-
-## Prompt
-For the Alpaca LoRA in particular, the prompt must be formatted like this:
-
-```
-Below is an instruction that describes a task. Write a response that appropriately completes the request.
-### Instruction:
-Write a Python script that generates text using the transformers library.
-### Response:
-```
-
-Sample output:
-
-```
-Below is an instruction that describes a task. Write a response that appropriately completes the request.
-### Instruction:
-Write a Python script that generates text using the transformers library.
-### Response:
-
-import transformers
-from transformers import AutoTokenizer, AutoModelForCausalLM
-tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
-model = AutoModelForCausalLM.from_pretrained("bert-base-uncased")
-texts = ["Hello world", "How are you"]
-for sentence in texts:
-sentence = tokenizer(sentence)
-print(f"Generated {len(sentence)} tokens from '{sentence}'")
-output = model(sentences=sentence).predict()
-print(f"Predicted {len(output)} tokens for '{sentence}':\n{output}")
-```
-
-## Training a LoRA
-
-You can train your own LoRAs from the `Training` tab. See [Training LoRAs](Training-LoRAs.md) for details.

text-generation-webui-main/docs/WSL-installation-guide.md

@@ -1,73 +0,0 @@
-Guide created by [@jfryton](https://github.com/jfryton). Thank you jfryton.
-
------
-
-Here's an easy-to-follow, step-by-step guide for installing Windows Subsystem for Linux (WSL) with Ubuntu on Windows 10/11:
-
-## Step 1: Enable WSL
-
-1. Press the Windows key + X and click on "Windows PowerShell (Admin)" or "Windows Terminal (Admin)" to open PowerShell or Terminal with administrator privileges.
-2. In the PowerShell window, type the following command and press Enter:
-
-```
-wsl --install
-```
-
-If this command doesn't work, you can enable WSL with the following command for Windows 10:
-
-```
-wsl --set-default-version 1
-```
-
-For Windows 11, you can use:
-
-```
-wsl --set-default-version 2
-```
-
-You may be prompted to restart your computer. If so, save your work and restart.
-
-## Step 2: Install Ubuntu
-
-1. Open the Microsoft Store.
-2. Search for "Ubuntu" in the search bar.
-3. Choose the desired Ubuntu version (e.g., Ubuntu 20.04 LTS) and click "Get" or "Install" to download and install the Ubuntu app.
-4. Once the installation is complete, click "Launch" or search for "Ubuntu" in the Start menu and open the app.
-
-## Step 3: Set up Ubuntu
-
-1. When you first launch the Ubuntu app, it will take a few minutes to set up. Be patient as it installs the necessary files and sets up your environment.
-2. Once the setup is complete, you will be prompted to create a new UNIX username and password. Choose a username and password, and make sure to remember them, as you will need them for future administrative tasks within the Ubuntu environment.
-
-## Step 4: Update and upgrade packages
-
-1. After setting up your username and password, it's a good idea to update and upgrade your Ubuntu system. Run the following commands in the Ubuntu terminal:
-
-```
-sudo apt update
-sudo apt upgrade
-```
-
-2. Enter your password when prompted. This will update the package list and upgrade any outdated packages.
-
-Congratulations! You have now installed WSL with Ubuntu on your Windows 10/11 system. You can use the Ubuntu terminal for various tasks, like running Linux commands, installing packages, or managing files.
-
-You can launch your WSL Ubuntu installation by selecting the Ubuntu app (like any other program installed on your computer) or typing 'ubuntu' into Powershell or Terminal.
-
-## Step 5: Proceed with Linux instructions
-
-1. You can now follow the Linux setup instructions. If you receive any error messages about a missing tool or package, just install them using apt:
-
-```
-sudo apt install [missing package]
-```
-
-If you face any issues or need to troubleshoot, you can always refer to the official Microsoft documentation for WSL: https://docs.microsoft.com/en-us/windows/wsl/
-
-## Bonus: Port Forwarding
-
-By default, you won't be able to access the webui from another device on your local network. You will need to setup the appropriate port forwarding using the following command (using PowerShell or Terminal with administrator privileges). 
-
-```
-netsh interface portproxy add v4tov4 listenaddress=0.0.0.0 listenport=7860 connectaddress=localhost connectport=7860
-```

text-generation-webui-main/docs/Windows-installation-guide.md

@@ -1,9 +0,0 @@
-If you are having trouble following the installation instructions in the README, Reddit user [Technical_Leather949](https://www.reddit.com/user/Technical_Leather949/) has created a more detailed, step-by-step guide covering:
-
-* Windows installation
-* 8-bit mode on Windows
-* LLaMA
-* LLaMA 4-bit
-
-The guide can be found here: https://www.reddit.com/r/LocalLLaMA/comments/11o6o3f/how_to_install_llama_8bit_and_4bit/
-

text-generation-webui-main/docs/llama.cpp-models.md

@@ -1,17 +0,0 @@
-## Using llama.cpp in the web UI
-
-#### Pre-converted models
-
-Simply place the model in the `models` folder, making sure that its name contains `ggml` somewhere and ends in `.bin`.
-
-#### Convert LLaMA yourself
-
-Follow the instructions in the llama.cpp README to generate the `ggml-model-q4_0.bin` file: https://github.com/ggerganov/llama.cpp#usage
-
-## Performance
-
-This was the performance of llama-7b int4 on my i5-12400F:
-
-> Output generated in 33.07 seconds (6.05 tokens/s, 200 tokens, context 17)
-
-You can change the number of threads with `--threads N`.