Special thanks to:

Warp sponsorship ### [Warp, built for coding with multiple AI agents](https://go.warp.dev/text-generation-webui) [Available for macOS, Linux, & Windows](https://go.warp.dev/text-generation-webui)
# TextGen **A desktop app for local LLMs. Open source, no telemetry.** Text, vision, tool-calling, web search. UI + API. [![GitHub stars](https://img.shields.io/github/stars/oobabooga/textgen?style=for-the-badge&logo=github&logoColor=white&labelColor=black)](https://github.com/oobabooga/textgen) [![Chat mode](https://raw.githubusercontent.com/oobabooga/screenshots/refs/heads/main/CHAT-4.8.png)](https://raw.githubusercontent.com/oobabooga/screenshots/refs/heads/main/CHAT-4.8.png) ## Get started in 1 minute Download, unzip, double-click `textgen`. A window opens. **https://github.com/oobabooga/textgen/releases** Portable builds for Linux, Windows, and macOS with CUDA, Vulkan, ROCm, and CPU-only options. All dependencies included. Compatible with GGUF (llama.cpp) models. For additional backends (ExLlamaV3, Transformers), training, image generation, and extensions, see [Installation](#installation). ## Features ### Chat & generation - `instruct` mode for instruction-following (like ChatGPT), and `chat-instruct`/`chat` modes for talking to custom characters. Prompts are automatically formatted with Jinja2 templates. - **Vision (multimodal)**: Attach images to messages for visual understanding ([tutorial](https://github.com/oobabooga/textgen/wiki/Multimodal-Tutorial)). - **File attachments**: Upload text files, PDF documents, and .docx documents to talk about their contents. - Edit messages, navigate between message versions, and branch conversations at any point. - Notebook tab for free-form text generation outside of chat turns. ### Backends & API - **Multiple backends**: [llama.cpp](https://github.com/ggerganov/llama.cpp), [ik_llama.cpp](https://github.com/ikawrakow/ik_llama.cpp), [Transformers](https://github.com/huggingface/transformers), [ExLlamaV3](https://github.com/turboderp-org/exllamav3), and [TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM). Switch between backends and models without restarting. - **OpenAI/Anthropic-compatible API**: Chat, Completions, and Messages endpoints with tool-calling support. Use as a local drop-in replacement for the OpenAI/Anthropic APIs ([examples](https://github.com/oobabooga/textgen/wiki/12-%E2%80%90-OpenAI-API#examples)). - **Tool-calling**: Models can call custom functions during chat, including web search, page fetching, and math. Each tool is a single `.py` file. MCP servers are also supported ([tutorial](https://github.com/oobabooga/textgen/wiki/Tool-Calling-Tutorial)). ### Training & image generation - **Training**: Fine-tune LoRAs on multi-turn chat or raw text datasets. Supports resuming interrupted runs ([tutorial](https://github.com/oobabooga/textgen/wiki/05-%E2%80%90-Training-Tab)). - **Image generation**: A dedicated tab for `diffusers` models like **Z-Image-Turbo**. Features 4-bit/8-bit quantization and a persistent gallery with image metadata ([tutorial](https://github.com/oobabooga/textgen/wiki/Image-Generation-Tutorial)). ### Privacy & interface - 100% offline and private, with zero telemetry, external resources, or remote update requests. - Dark/light themes, syntax highlighting for code blocks, and LaTeX rendering for mathematical expressions. - Built-in and community [extensions](https://github.com/oobabooga/textgen/wiki/07-%E2%80%90-Extensions) including TTS, voice input, and translation. See the [extensions directory](https://github.com/oobabooga/textgen-extensions) for the full list. ## Downloading models 1. Download a GGUF model file from [Hugging Face](https://huggingface.co/models?pipeline_tag=text-generation&sort=downloads&search=gguf). 2. Place it in the `user_data/models` folder. That's it. The UI will detect it automatically. For recommended GGUF quants, check out [LocalBench](https://localbench.substack.com). To estimate how much memory a model will use, try the [GGUF Memory Calculator](https://huggingface.co/spaces/oobabooga/accurate-gguf-vram-calculator).
Other model types (Transformers, EXL3) Models that consist of multiple files (like 16-bit Transformers models and EXL3 models) should be placed in a subfolder inside `user_data/models`: ``` textgen └── user_data └── models └── Qwen_Qwen3-8B ├── config.json ├── generation_config.json ├── model-00001-of-00004.safetensors ├── ... ├── tokenizer_config.json └── tokenizer.json ``` These formats require the full installation (not the portable build).
## Installation For the desktop app, see the [portable builds](https://github.com/oobabooga/textgen/releases). The options below run the web UI in your browser instead. ### Manual portable install with venv Fast setup on any Python 3.9+: ```bash # Clone repository git clone https://github.com/oobabooga/textgen cd textgen # Create virtual environment python -m venv venv # Activate virtual environment # On Windows: venv\Scripts\activate # On macOS/Linux: source venv/bin/activate # Install dependencies (choose appropriate file under requirements/portable for your hardware) pip install -r requirements/portable/requirements.txt --upgrade # Launch server (basic command) python server.py --portable --api --auto-launch # When done working, deactivate deactivate ``` ### Full installation For users who need additional backends (ExLlamaV3, Transformers), training, image generation, or extensions like TTS, voice input, and translation. Requires ~10GB disk space and downloads PyTorch.
Installation details ### One-click installer 1. Clone the repository, or [download its source code](https://github.com/oobabooga/textgen/archive/refs/heads/main.zip) and extract it. 2. Run the startup script for your OS: `start_windows.bat`, `start_linux.sh`, or `start_macos.sh`. 3. When prompted, select your GPU vendor. 4. After installation, open `http://127.0.0.1:7860` in your browser. After installation: * **Restart**: run the same `start_` script. * **Pass command-line flags**: directly (e.g., `./start_linux.sh --help`), or persist them in `user_data/CMD_FLAGS.txt` (e.g., `--api` to enable the API). * **Update**: run the update script for your OS (`update_wizard_windows.bat`, `update_wizard_linux.sh`, or `update_wizard_macos.sh`). * **Reinstall from scratch**: delete the `installer_files` folder and run the `start_` script again. * **Install extension requirements**: use the update wizard's "Install/update extensions requirements" option. It reinstalls the main project requirements at the end to ensure they take precedence over conflicting extension dependencies. Notes: * These scripts (`start_`, `update_wizard_`, `cmd_`) don't need to run as admin/root. * For automated installation, set the `GPU_CHOICE`, `LAUNCH_AFTER_INSTALL`, and `INSTALL_EXTENSIONS` environment variables. Example: `GPU_CHOICE=A LAUNCH_AFTER_INSTALL=FALSE INSTALL_EXTENSIONS=TRUE ./start_linux.sh`. * Under the hood, the script uses Miniforge to set up a Conda environment in `installer_files/`. To run anything manually in this environment, launch an interactive shell using `cmd_linux.sh`, `cmd_windows.bat`, or `cmd_macos.sh`. ### Full installation with Conda #### 0. Install Conda https://github.com/conda-forge/miniforge On Linux or WSL, Miniforge can be automatically installed with these two commands: ``` curl -sL "https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-Linux-x86_64.sh" > "Miniforge3.sh" bash Miniforge3.sh ``` For other platforms, download from: https://github.com/conda-forge/miniforge/releases/latest #### 1. Create a new conda environment ``` conda create -n textgen python=3.13 conda activate textgen ``` #### 2. Install Pytorch | System | GPU | Command | |--------|---------|---------| | Linux/WSL | NVIDIA | `pip3 install torch==2.9.1 --index-url https://download.pytorch.org/whl/cu128` | | Linux/WSL | CPU only | `pip3 install torch==2.9.1 --index-url https://download.pytorch.org/whl/cpu` | | Linux | AMD | `pip3 install https://repo.radeon.com/rocm/manylinux/rocm-rel-7.2/torch-2.9.1%2Brocm7.2.0.lw.git7e1940d4-cp313-cp313-linux_x86_64.whl` | | MacOS + MPS | Any | `pip3 install torch==2.9.1` | | Windows | NVIDIA | `pip3 install torch==2.9.1 --index-url https://download.pytorch.org/whl/cu128` | | Windows | CPU only | `pip3 install torch==2.9.1` | The up-to-date commands can be found here: https://pytorch.org/get-started/locally/. If you need `nvcc` to compile some library manually, you will additionally need to install this: ``` conda install -y -c "nvidia/label/cuda-12.8.1" cuda ``` #### 3. Install the web UI ``` git clone https://github.com/oobabooga/textgen cd textgen pip install -r requirements/full/ ``` Requirements file to use: | GPU | requirements file to use | |--------|---------| | NVIDIA | `requirements.txt` | | AMD | `requirements_amd.txt` | | CPU only | `requirements_cpu_only.txt` | | Apple Intel | `requirements_apple_intel.txt` | | Apple Silicon | `requirements_apple_silicon.txt` | #### 4. Start the web UI ``` conda activate textgen cd textgen python server.py ``` Then browse to `http://127.0.0.1:7860`. #### Manual compilation The `requirements*.txt` files above contain wheels precompiled through GitHub Actions. To compile manually (e.g., if no wheels are available for your hardware), use `requirements_nowheels.txt` and install your desired loaders manually. #### Updating the requirements From time to time, the `requirements*.txt` files change. To update: ``` conda activate textgen cd textgen pip install -r --upgrade ``` ### Docker ``` For NVIDIA GPU: ln -s docker/{nvidia/Dockerfile,nvidia/docker-compose.yml,.dockerignore} . For AMD GPU: ln -s docker/{amd/Dockerfile,amd/docker-compose.yml,.dockerignore} . For Intel GPU: ln -s docker/{intel/Dockerfile,intel/docker-compose.yml,.dockerignore} . For CPU only ln -s docker/{cpu/Dockerfile,cpu/docker-compose.yml,.dockerignore} . cp docker/.env.example .env #Create logs/cache dir : mkdir -p user_data/logs user_data/cache # Edit .env and set: # TORCH_CUDA_ARCH_LIST based on your GPU model # APP_RUNTIME_GID your host user's group id (run `id -g` in a terminal) # BUILD_EXTENIONS optionally add comma separated list of extensions to build # Edit user_data/CMD_FLAGS.txt and add in it the options you want to execute (like --listen --cpu) # docker compose up --build ``` * You need to have Docker Compose v2.17 or higher installed. See [this guide](https://github.com/oobabooga/textgen/wiki/09-%E2%80%90-Docker) for instructions. * For additional docker files, check out [this repository](https://github.com/Atinoda/text-generation-webui-docker).
## Command-line flags
Show full list ```txt usage: server.py [-h] [--user-data-dir USER_DATA_DIR] [--multi-user] [--model MODEL] [--lora LORA [LORA ...]] [--model-dir MODEL_DIR] [--lora-dir LORA_DIR] [--model-menu] [--settings SETTINGS] [--extensions EXTENSIONS [EXTENSIONS ...]] [--verbose] [--idle-timeout IDLE_TIMEOUT] [--image-model IMAGE_MODEL] [--image-model-dir IMAGE_MODEL_DIR] [--image-dtype {bfloat16,float16}] [--image-attn-backend {flash_attention_2,sdpa}] [--image-cpu-offload] [--image-compile] [--image-quant {none,bnb-8bit,bnb-4bit,torchao-int8wo,torchao-fp4,torchao-float8wo}] [--loader LOADER] [--ctx-size N] [--cache-type N] [--model-draft MODEL_DRAFT] [--draft-max DRAFT_MAX] [--gpu-layers-draft GPU_LAYERS_DRAFT] [--device-draft DEVICE_DRAFT] [--ctx-size-draft CTX_SIZE_DRAFT] [--spec-type {none,ngram-mod,ngram-simple,ngram-map-k,ngram-map-k4v,ngram-cache}] [--spec-ngram-size-n SPEC_NGRAM_SIZE_N] [--spec-ngram-size-m SPEC_NGRAM_SIZE_M] [--spec-ngram-min-hits SPEC_NGRAM_MIN_HITS] [--gpu-layers N] [--cpu-moe] [--mmproj MMPROJ] [--streaming-llm] [--tensor-split TENSOR_SPLIT] [--split-mode {layer,row,tensor,none}] [--no-mmap] [--mlock] [--no-kv-offload] [--batch-size BATCH_SIZE] [--ubatch-size UBATCH_SIZE] [--threads THREADS] [--threads-batch THREADS_BATCH] [--numa] [--parallel PARALLEL] [--fit-target FIT_TARGET] [--extra-flags EXTRA_FLAGS] [--ik] [--cpu] [--cpu-memory CPU_MEMORY] [--disk] [--disk-cache-dir DISK_CACHE_DIR] [--load-in-8bit] [--bf16] [--no-cache] [--trust-remote-code] [--force-safetensors] [--no_use_fast] [--attn-implementation IMPLEMENTATION] [--load-in-4bit] [--use_double_quant] [--compute_dtype COMPUTE_DTYPE] [--quant_type QUANT_TYPE] [--gpu-split GPU_SPLIT] [--enable-tp] [--tp-backend TP_BACKEND] [--cfg-cache] [--listen] [--listen-port LISTEN_PORT] [--listen-host LISTEN_HOST] [--share] [--auto-launch] [--gradio-auth GRADIO_AUTH] [--gradio-auth-path GRADIO_AUTH_PATH] [--ssl-keyfile SSL_KEYFILE] [--ssl-certfile SSL_CERTFILE] [--subpath SUBPATH] [--old-colors] [--portable] [--api] [--public-api] [--public-api-id PUBLIC_API_ID] [--api-port API_PORT] [--api-key API_KEY] [--admin-key ADMIN_KEY] [--api-enable-ipv6] [--api-disable-ipv4] [--nowebui] [--temperature N] [--dynatemp-low N] [--dynatemp-high N] [--dynatemp-exponent N] [--smoothing-factor N] [--smoothing-curve N] [--top-p N] [--top-k N] [--min-p N] [--top-n-sigma N] [--typical-p N] [--xtc-threshold N] [--xtc-probability N] [--epsilon-cutoff N] [--eta-cutoff N] [--tfs N] [--top-a N] [--adaptive-target N] [--adaptive-decay N] [--dry-multiplier N] [--dry-allowed-length N] [--dry-base N] [--repetition-penalty N] [--frequency-penalty N] [--presence-penalty N] [--encoder-repetition-penalty N] [--no-repeat-ngram-size N] [--repetition-penalty-range N] [--penalty-alpha N] [--guidance-scale N] [--mirostat-mode N] [--mirostat-tau N] [--mirostat-eta N] [--do-sample | --no-do-sample] [--dynamic-temperature | --no-dynamic-temperature] [--temperature-last | --no-temperature-last] [--sampler-priority N] [--dry-sequence-breakers N] [--enable-thinking | --no-enable-thinking] [--reasoning-effort N] [--preserve-thinking | --no-preserve-thinking] [--chat-template-file CHAT_TEMPLATE_FILE] [--no-electron] TextGen options: -h, --help show this help message and exit Basic settings: --user-data-dir USER_DATA_DIR Path to the user data directory. Default: auto-detected. --multi-user Multi-user mode. Chat histories are not saved or automatically loaded. Best suited for small trusted teams. --model MODEL Name of the model to load by default. --lora LORA [LORA ...] The list of LoRAs to load. If you want to load more than one LoRA, write the names separated by spaces. --model-dir MODEL_DIR Path to directory with all the models. --lora-dir LORA_DIR Path to directory with all the loras. --model-menu Show a model menu in the terminal when the web UI is first launched. --settings SETTINGS Load the default interface settings from this yaml file. See user_data/settings-template.yaml for an example. If you create a file called user_data/settings.yaml, this file will be loaded by default without the need to use the --settings flag. --extensions EXTENSIONS [EXTENSIONS ...] The list of extensions to load. If you want to load more than one extension, write the names separated by spaces. --verbose Print the prompts to the terminal. --idle-timeout IDLE_TIMEOUT Unload model after this many minutes of inactivity. It will be automatically reloaded when you try to use it again. Image model: --image-model IMAGE_MODEL Name of the image model to select on startup (overrides saved setting). --image-model-dir IMAGE_MODEL_DIR Path to directory with all the image models. --image-dtype {bfloat16,float16} Data type for image model. --image-attn-backend {flash_attention_2,sdpa} Attention backend for image model. --image-cpu-offload Enable CPU offloading for image model. --image-compile Compile the image model for faster inference. --image-quant {none,bnb-8bit,bnb-4bit,torchao-int8wo,torchao-fp4,torchao-float8wo} Quantization method for image model. Model loader: --loader LOADER Choose the model loader manually, otherwise, it will get autodetected. Valid options: Transformers, llama.cpp, ExLlamav3_HF, ExLlamav3, TensorRT- LLM. Context and cache: --ctx-size, --n_ctx, --max_seq_len N Context size in tokens. 0 = auto for llama.cpp (requires gpu-layers=-1), 8192 for other loaders. --cache-type, --cache_type N KV cache type; valid options: llama.cpp - fp16, q8_0, q4_0; ExLlamaV3 - fp16, q2 to q8 (can specify k_bits and v_bits separately, e.g. q4_q8). Speculative decoding: --model-draft MODEL_DRAFT Path to the draft model for speculative decoding. --draft-max DRAFT_MAX Number of tokens to draft for speculative decoding. --gpu-layers-draft GPU_LAYERS_DRAFT Number of layers to offload to the GPU for the draft model. --device-draft DEVICE_DRAFT Comma-separated list of devices to use for offloading the draft model. Example: CUDA0,CUDA1 --ctx-size-draft CTX_SIZE_DRAFT Size of the prompt context for the draft model. If 0, uses the same as the main model. --spec-type {none,ngram-mod,ngram-simple,ngram-map-k,ngram-map-k4v,ngram-cache} Draftless speculative decoding type. Recommended: ngram-mod. --spec-ngram-size-n SPEC_NGRAM_SIZE_N N-gram lookup size for ngram speculative decoding. --spec-ngram-size-m SPEC_NGRAM_SIZE_M Draft n-gram size for ngram speculative decoding. --spec-ngram-min-hits SPEC_NGRAM_MIN_HITS Minimum n-gram hits for ngram-map speculative decoding. llama.cpp: --gpu-layers, --n-gpu-layers N Number of layers to offload to the GPU. -1 = auto. --cpu-moe Move the experts to the CPU (for MoE models). --mmproj MMPROJ Path to the mmproj file for vision models. --streaming-llm Activate StreamingLLM to avoid re-evaluating the entire prompt when old messages are removed. --tensor-split TENSOR_SPLIT Split the model across multiple GPUs. Comma-separated list of proportions. Example: 60,40. --split-mode {layer,row,tensor,none} How to split the model across multiple GPUs. "tensor" can make multi-GPU significantly faster. --no-mmap Prevent mmap from being used. --mlock Force the system to keep the model in RAM. --no-kv-offload Do not offload the K, Q, V to the GPU. This saves VRAM but reduces performance. --batch-size BATCH_SIZE Maximum number of prompt tokens to batch together when calling llama-server. This is the application level batch size. --ubatch-size UBATCH_SIZE Maximum number of prompt tokens to batch together when calling llama-server. This is the max physical batch size for computation (device level). --threads THREADS Number of threads to use. --threads-batch THREADS_BATCH Number of threads to use for batches/prompt processing. --numa Activate NUMA task allocation for llama.cpp. --parallel PARALLEL Number of parallel request slots. The context size is divided equally among slots. For example, to have 4 slots with 8192 context each, set ctx_size to 32768. --fit-target FIT_TARGET Target VRAM margin per device for auto GPU layers, comma-separated list of values in MiB. A single value is broadcast across all devices. --extra-flags EXTRA_FLAGS Extra flags to pass to llama-server. Example: "--jinja --rpc 192.168.1.100:50052" --ik Use ik_llama.cpp instead of upstream llama.cpp. Requires the ik_llama_cpp_binaries package to be installed. Transformers/Accelerate: --cpu Use the CPU to generate text. Warning: Training on CPU is extremely slow. --cpu-memory CPU_MEMORY Maximum CPU memory in GiB. Use this for CPU offloading. --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. --load-in-8bit Load the model with 8-bit precision (using bitsandbytes). --bf16 Load the model with bfloat16 precision. Requires NVIDIA Ampere GPU. --no-cache Set use_cache to False while generating text. This reduces VRAM usage slightly, but it comes at a performance cost. --trust-remote-code Set trust_remote_code=True while loading the model. Necessary for some models. --force-safetensors Set use_safetensors=True while loading the model. This prevents arbitrary code execution. --no_use_fast Set use_fast=False while loading the tokenizer (it's True by default). Use this if you have any problems related to use_fast. --attn-implementation IMPLEMENTATION Attention implementation. Valid options: sdpa, eager, flash_attention_2. bitsandbytes 4-bit: --load-in-4bit Load the model with 4-bit precision (using bitsandbytes). --use_double_quant use_double_quant for 4-bit. --compute_dtype COMPUTE_DTYPE compute dtype for 4-bit. Valid options: bfloat16, float16, float32. --quant_type QUANT_TYPE quant_type for 4-bit. Valid options: nf4, fp4. ExLlamaV3: --gpu-split GPU_SPLIT Comma-separated list of VRAM (in GB) to use per GPU device for model layers. Example: 20,7,7. --enable-tp, --enable_tp Enable Tensor Parallelism (TP) to split the model across GPUs. --tp-backend TP_BACKEND The backend for tensor parallelism. Valid options: native, nccl. Default: native. --cfg-cache Create an additional cache for CFG negative prompts. Necessary to use CFG with that loader. Gradio: --listen Make the web UI reachable from your local network. --listen-port LISTEN_PORT The listening port that the server will use. --listen-host LISTEN_HOST The hostname 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 GRADIO_AUTH Set Gradio authentication password in the format "username:password". Multiple credentials can also be supplied with "u1:p1,u2:p2,u3:p3". --gradio-auth-path GRADIO_AUTH_PATH Set the Gradio authentication file path. The file should contain one or more user:password pairs in the same format as above. --ssl-keyfile SSL_KEYFILE The path to the SSL certificate key file. --ssl-certfile SSL_CERTFILE The path to the SSL certificate cert file. --subpath SUBPATH Customize the subpath for gradio, use with reverse proxy --old-colors Use the legacy Gradio colors, before the December/2024 update. --portable Hide features not available in portable mode like training. API: --api Enable the API server. --public-api Create a public URL for the API using Cloudflare. --public-api-id PUBLIC_API_ID Tunnel ID for named Cloudflare Tunnel. Use together with public-api option. --api-port API_PORT The listening port for the API. --api-key API_KEY API authentication key. --admin-key ADMIN_KEY API authentication key for admin tasks like loading and unloading models. If not set, will be the same as --api-key. --api-enable-ipv6 Enable IPv6 for the API --api-disable-ipv4 Disable IPv4 for the API --nowebui Do not launch the Gradio UI. Useful for launching the API in standalone mode. API generation defaults: --temperature N Temperature --dynatemp-low N Dynamic temperature low --dynatemp-high N Dynamic temperature high --dynatemp-exponent N Dynamic temperature exponent --smoothing-factor N Smoothing factor --smoothing-curve N Smoothing curve --top-p N Top P --top-k N Top K --min-p N Min P --top-n-sigma N Top N Sigma --typical-p N Typical P --xtc-threshold N XTC threshold --xtc-probability N XTC probability --epsilon-cutoff N Epsilon cutoff --eta-cutoff N Eta cutoff --tfs N TFS --top-a N Top A --adaptive-target N Adaptive target --adaptive-decay N Adaptive decay --dry-multiplier N DRY multiplier --dry-allowed-length N DRY allowed length --dry-base N DRY base --repetition-penalty N Repetition penalty --frequency-penalty N Frequency penalty --presence-penalty N Presence penalty --encoder-repetition-penalty N Encoder repetition penalty --no-repeat-ngram-size N No repeat ngram size --repetition-penalty-range N Repetition penalty range --penalty-alpha N Penalty alpha --guidance-scale N Guidance scale --mirostat-mode N Mirostat mode --mirostat-tau N Mirostat tau --mirostat-eta N Mirostat eta --do-sample, --no-do-sample Do sample --dynamic-temperature, --no-dynamic-temperature Dynamic temperature --temperature-last, --no-temperature-last Temperature last --sampler-priority N Sampler priority --dry-sequence-breakers N DRY sequence breakers --enable-thinking, --no-enable-thinking Enable thinking --reasoning-effort N Reasoning effort --preserve-thinking, --no-preserve-thinking Preserve thinking blocks from prior turns in the chat template --chat-template-file CHAT_TEMPLATE_FILE Path to a chat template file (.jinja, .jinja2, or .yaml) to use as the default instruction template for API requests. Overrides the model's built-in template. Electron: --no-electron In portable builds, skip the Electron desktop window. Useful if you prefer to use the web UI in the browser. ```
## Loading a model automatically To skip the Model tab on every launch, add this to `user_data/CMD_FLAGS.txt`: ``` --model my-model.gguf ``` Replace `my-model.gguf` with the name of a file in `user_data/models`. The model will load on startup. To pass extra flags, put each on its own line: ``` --model my-model.gguf --cache-type q8_0 ``` ## Documentation https://github.com/oobabooga/textgen/wiki ## Community [![Reddit](https://img.shields.io/reddit/subreddit-subscribers/Oobabooga?style=for-the-badge&logo=reddit&logoColor=white&label=r%2FOobabooga&labelColor=black&color=FF4500)](https://www.reddit.com/r/Oobabooga/) ## Acknowledgments - In August 2023, [Andreessen Horowitz](https://a16z.com/) (a16z) provided a generous grant to encourage and support my independent work on this project. I am **extremely** grateful for their trust and recognition. - This project was inspired by [AUTOMATIC1111/stable-diffusion-webui](https://github.com/AUTOMATIC1111/stable-diffusion-webui) and wouldn't exist without it.