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https://github.com/uminosachi/open-llm-webui

This repository contains a web application designed to execute relatively compact, locally-operated Large Language Models (LLMs).
https://github.com/uminosachi/open-llm-webui

chatbot ggml gradio huggingface language-model llama llama2 llama3 llava llava-llama3 llm nlp transformers

Last synced: about 1 month ago
JSON representation

This repository contains a web application designed to execute relatively compact, locally-operated Large Language Models (LLMs).

Host: GitHub
URL: https://github.com/uminosachi/open-llm-webui
Owner: Uminosachi
License: apache-2.0
Created: 2023-05-17T06:02:34.000Z (over 1 year ago)
Default Branch: main
Last Pushed: 2024-08-18T01:52:56.000Z (3 months ago)
Last Synced: 2024-09-23T11:05:25.333Z (about 2 months ago)
Topics: chatbot, ggml, gradio, huggingface, language-model, llama, llama2, llama3, llava, llava-llama3, llm, nlp, transformers
Language: Python
Homepage:
Size: 1.25 MB
Stars: 31
Watchers: 3
Forks: 4
Open Issues: 1
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

README

# Open LLM WebUI

This repository contains a web application designed to execute relatively compact, locally-operated Large Language Models (LLMs).

## Installation

Please follow these steps to install the software:

* Create a new conda environment:

```bash
conda create -n ollm python=3.10
conda activate ollm
```

* Clone the software repository:

```bash
git clone https://github.com/Uminosachi/open-llm-webui.git
cd open-llm-webui
```

### Python Package Installation

#### General Instructions

* Install the necessary Python packages by executing:

```bash
pip install -r requirements.txt
```

#### Installation for Flash Attention (Optional)

* To enable Flash Attention in some models, if CUDA is available, install Flash Attention:

```bash
pip install packaging ninja
pip install flash-attn --no-build-isolation
```

#### Platform-Specific Instructions

* **For Windows (with CUDA support):**
* Install [Visual Studio](https://learn.microsoft.com/en-us/visualstudio/install/install-visual-studio?view=vs-2022):
* ⚠️ Important: Make sure to select `Desktop development with C++` during the installation process.
* Copy MSBuild extensions for CUDA as an administrator (adjust the CUDA version `v12.1` as needed):

```bash
xcopy /e "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.1\extras\visual_studio_integration\MSBuildExtensions" "C:\Program Files\Microsoft Visual Studio\2022\Community\MSBuild\Microsoft\VC\v170\BuildCustomizations"
```

* Configure the required environment variables for the build (adjust the CUDA version as necessary):

```bash
set PATH=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.1\bin;%PATH%
"C:\Program Files\Microsoft Visual Studio\2022\Community\VC\Auxiliary\Build\vcvars64.bat"
set FORCE_CMAKE=1
set CMAKE_ARGS="-DGGML_CUDA=ON -DCMAKE_CXX_FLAGS=/utf-8 -DCMAKE_C_FLAGS=/utf-8"
set CMAKE_BUILD_PARALLEL_LEVEL=16
```

* Install the necessary Python packages (this process may take some time):

```bash
pip install ninja cmake scikit-build-core[pyproject]
pip install --force-reinstall --no-cache-dir llama-cpp-python
pip install -r requirements.txt
```

* **For Linux (with CUDA support):**
* Configure the required environment variables for the build (if not already set):

```bash
export PATH=/usr/local/cuda/bin:${PATH}
export LD_LIBRARY_PATH=/usr/lib/x86_64-linux-gnu:/usr/local/cuda/lib64:${LD_LIBRARY_PATH}
```

* Install the OpenMP libraries used for the build:

```bash
sudo apt-get update
sudo apt-get install libgomp1 libomp-dev
```

* Install the necessary Python packages:

```bash
pip install ninja cmake scikit-build-core[pyproject]
export CMAKE_ARGS="-DGGML_CUDA=ON"
pip install --force-reinstall --no-cache-dir llama-cpp-python
pip install -r requirements.txt
```

* **For Mac OS (without CUDA support):**
* Install the necessary Python packages:

```bash
BUILD_CUDA_EXT=0 pip install -r requirements.txt
```

* Rebuild the `bitsandbytes` package with the CPU option.

```bash
pip uninstall bitsandbytes
git clone https://github.com/TimDettmers/bitsandbytes.git
cd bitsandbytes
cmake -DCOMPUTE_BACKEND=cpu -S .
make
pip install .
```

* Known Issue: Running the LLaVA model on Mac results in an error.

## Running the application

```bash
python ollm_app.py
```

* Open in your browser.

## Downloading the Model

To download the model:

* Launch this application.
* Click on the "Download model" button next to the LLM model ID.
* Wait for the download to complete.

### 📜 Model List (transformers)

| Provider | Model Names |
|---------------|--------------------------------------------------------------------------------------------|
| Microsoft | Phi-3-mini-4k-instruct |
| Google | gemma-2-9b-it, gemma-1.1-2b-it, gemma-1.1-7b-it |
| NVIDIA | Llama3-ChatQA-1.5-8B |
| Qwen | Qwen2-7B-Instruct |
| Mistral AI | Mistral-7B-Instruct-v0.3 |
| Rakuten | RakutenAI-7B-chat, RakutenAI-7B-instruct |
| rinna | youri-7b-chat |
| TheBloke | Llama-2-7b-Chat-GPTQ, Kunoichi-7B-GPTQ |

* 📋 Note: By adding the repository paths of models to `model_manager/add_tfs_models.txt`, they will be included in the list of Model IDs and displayed in the UI.
* 🔍 Note: The downloaded model file will be stored in the `.cache/huggingface/hub` directory of your home directory.

#### Access and Download Gemma and Llama Models

* Before downloading any models, ensure that you have obtained the necessary access rights through Hugging Face. Please visit the following pages to request access:
* [Llama 3 model by Meta](https://huggingface.co/meta-llama/Meta-Llama-3-8B)
* [Llama 2 model by Meta](https://huggingface.co/meta-llama/Llama-2-7b-hf)
* [Gemma 1.1 model by Google](https://huggingface.co/google/gemma-1.1-2b-it)

#### Login to Hugging Face

* Before downloading any models, please log in via the command line using:

```bash
huggingface-cli login
```

### 🦙 Model List (llama.cpp)

| Provider | Model Names |
|---------------|--------------------------------------------------------------------------------------------|
| Microsoft | Phi-3-mini-4k-instruct-q4.gguf, Phi-3-mini-4k-instruct-fp16.gguf |
| TheBloke | llama-2-7b-chat.Q4_K_M.gguf |
| QuantFactory | Meta-Llama-3-8B-Instruct.Q4_K_M.gguf |

#### Using any GGUF file

* 🔍 File Placement: Place files with the `.gguf` extension in the `models` directory within the `open-llm-webui` folder. These files will then appear in the model list on the `llama.cpp` tab of the web UI and can be used accordingly.
* 📝 Metadata Usage: If the metadata of a GGUF model includes `tokenizer.chat_template`, this template will be used to create the prompts.

### 🖼️ Model List (Multimodal LLaVA)

| Provider | Model Names |
|---------------|--------------------------------------------------------------------------------------------|
| Microsoft | Phi-3.5-vision-instruct, Phi-3-vision-128k-instruct |
| Meta | Llama-3.2-11B-Vision (limited support as a trial) |
| llava-hf | llava-v1.6-mistral-7b-hf, llava-v1.6-vicuna-7b-hf, llava-1.5-7b-hf |
| tinyllava | TinyLLaVA-Phi-2-SigLIP-3.1B |
| openbmb | MiniCPM-V-2_6-int4, MiniCPM-V-2_6, MiniCPM-Llama3-V-2_5-int4, MiniCPM-Llama3-V-2_5 |
| SakanaAI | EvoVLM-JP-v1-7B |

#### Access and Download Llama 3 Models

* Before downloading any models, ensure that you have obtained the necessary access rights through Hugging Face. Please visit the following pages to request access:
* [Llama 3.2 Vision model by Meta](https://huggingface.co/meta-llama/Llama-3.2-11B-Vision)
* [Llama 3 model by Meta](https://huggingface.co/meta-llama/Meta-Llama-3-8B)

## Usage

* Enter your message into the "Input text" box. Adjust the slider for "Max new tokens" as needed.
* Under "Advanced options" adjust the settings for "Temperature", "Top k", "Top p", and "Repetition Penalty" as needed.
* If replacing the system message of the prompt, under "Advanced options" enable the checkbox and enter text.
* Press "Enter" on your keyboard or click the "Generate" button.
* ⚠️ Note: If the cloud-based model has been updated, it may be downloaded upon execution.
* If you click the "Clear chat" button, the chat history will be cleared.

### transformers tab

* By enabling the `CPU execution` checkbox, the model will use the argument `device_map="cpu"`.
* Some of the transformers models are loaded with the following 4-bit or 8-bit settings using the `bitsandbytes` package.

### llama.cpp tab

* Use the radio buttons in the `Default chat template` to select the template that will be used if the GGUF model lacks a `chat_template`.

### LLaVA tab

* You can upload an image to the LLaVA Image area of this tab and input a prompt related to the image.
* Some of the LLaVA models are loaded with the following 4-bit or 8-bit settings using the `bitsandbytes` package.

### options

* When you enable the `Translate (ja->en/en->ja)` checkbox:
* Any input in Japanese will be automatically translated to English, and responses in English will be automatically translated back into Japanese.
* ⚠️ Note: Downloading the translation model for the first time may take some time.

![UI image](images/open-ollm-webui_ui_image_1.png)

## Model Credit

| Developer | Model | License |
|---------------------|------------------------------|----------------------------------------------------------------|
| Meta | Llama-3.2 | [Llama 3.2 Community License](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/LICENSE) |
| Meta | Llama-3.1 | [Llama 3.1 Community License](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/LICENSE) |
| Meta | Llama-3 | [Llama 3 Community License](https://github.com/meta-llama/llama3/blob/main/LICENSE) |
| Meta | Llama-2 | [Llama 2 Community License](https://github.com/facebookresearch/llama/blob/main/LICENSE) |
| Microsoft | Phi-3.5, Phi-3 | [The MIT License](https://opensource.org/licenses/MIT) |
| Google | Gemma | [Gemma Terms of Use](https://ai.google.dev/gemma/terms) |
| NVIDIA | Llama3-ChatQA | [Llama 3 Community License](https://github.com/meta-llama/llama3/blob/main/LICENSE) |
| Alibaba Group | Qwen2-7B-Instruct | [Apache License 2.0](https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md) |
| Mistral AI | Mistral-7B-Instruct | [Apache License 2.0](https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md) |
| Rakuten | RakutenAI | [Apache License 2.0](https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md) |
| rinna | Youri | [Llama 2 Community License](https://ai.meta.com/llama/license/) |
| Sanji Watsuki | Kunoichi-7B | [CC-BY-NC-4.0](https://spdx.org/licenses/CC-BY-NC-4.0) |
| Hugging Face | llava-v1.6-mistral-7b-hf | [Apache License 2.0](https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md) |
| Hugging Face | llava-v1.6-vicuna-7b-hf, llava-1.5-7b-hf | [Llama 2 Community License](https://ai.meta.com/llama/license/) |
| TinyLLaVA | TinyLLaVA-Phi-2-SigLIP-3.1B | [Apache License 2.0](https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md) |
| OpenBMB | MiniCPM | [MiniCPM Model License](https://github.com/OpenBMB/MiniCPM/blob/main/MiniCPM%20Model%20License.md) |
| Sakana AI | EvoVLM-JP-v1-7B | [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0) |