CoolFace
Apppublic

Applesam4/NETO-AI

sourceHugging Faceapache-2.0updated 3y agoView on Hugging Face
0likes
App README

Text generation web UI

A gradio web UI for running Large Language Models like LLaMA, llama.cpp, GPT-J, OPT, and GALACTICA.

Its goal is to become the 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)
Image3Image4

Features

  • —3 interface modes: default, notebook, and chat
  • —Multiple model backends: transformers, llama.cpp, ExLlama, AutoGPTQ, GPTQ-for-LLaMa
  • —Dropdown menu for quickly switching between different models
  • —LoRA: load and unload LoRAs on the fly, train a new LoRA
  • —Precise instruction templates for chat mode, including Llama 2, Alpaca, Vicuna, WizardLM, StableLM, and many others
  • —Multimodal pipelines, including LLaVA and MiniGPT-4
  • —8-bit and 4-bit inference through bitsandbytes
  • —CPU mode for transformers models
  • —DeepSpeed ZeRO-3 inference
  • —Extensions
  • —Custom chat characters
  • —Very efficient text streaming
  • —Markdown output with LaTeX rendering, to use for instance with GALACTICA
  • —Nice HTML output for GPT-4chan
  • —API, including endpoints for websocket streaming (see the examples)

To learn how to use the various features, check out the Documentation: https://github.com/oobabooga/text-generation-webui/tree/main/docs

Installation

One-click installers

WindowsLinuxmacOSWSL
oobabooga-windows.zipoobabooga-linux.zipoobabooga-macos.zipoobabooga-wsl.zip

Just download the zip above, extract it, and double-click on "start". The web UI and all its dependencies will be installed in the same folder.

  • —The source codes are here: https://github.com/oobabooga/one-click-installers
  • —There is no need to run the installers as admin.
  • —AMD doesn't work on Windows.
  • —Huge thanks to @jllllll, @ClayShoaf, and @xNul for their contributions to these installers.

Manual installation using Conda

Recommended if you have some experience with the command line.

0. Install Conda

https://docs.conda.io/en/latest/miniconda.html

On Linux or WSL, it can be automatically installed with these two commands:

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

1. Create a new conda environment
conda create -n textgen python=3.10.9
conda activate textgen
2. Install Pytorch
SystemGPUCommand
Linux/WSLNVIDIApip3 install torch torchvision torchaudio
Linux/WSLCPU onlypip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
LinuxAMDpip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm5.4.2
MacOS + MPSAnypip3 install torch torchvision torchaudio
WindowsNVIDIApip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu117
WindowsCPU onlypip3 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
bitsandbytes

bitsandbytes >= 0.39 may not work on older NVIDIA GPUs. In that case, to use --load-in-8bit, you may have to downgrade like this:

  • —Linux: pip install bitsandbytes==0.38.1
  • —Windows: pip install https://github.com/jllllll/bitsandbytes-windows-webui/raw/main/bitsandbytes-0.38.1-py3-none-any.whl

Alternative: Docker

ln -s docker/{Dockerfile,docker-compose.yml,.dockerignore} .
cp docker/.env.example .env
# Edit .env and set TORCH_CUDA_ARCH_LIST based on your GPU model
docker compose up --build
  • —You need to have docker compose v2.17 or higher installed. See this guide for instructions.
  • —For additional docker files, check out this repository.

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 is the main place to download models. These are some examples:

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

To download a protected model, set env vars HF_USER and HF_PASS to your Hugging Face username and password (or User Access Token). The model's terms must first be accepted on the HF website.

GGML models

You can drop these directly into the models/ folder, making sure that the file name contains ggml somewhere and ends in .bin.

GPT-4chan

<details> <summary> Instructions </summary>

GPT-4chan has been shut down from Hugging Face, so you need to download it elsewhere. You have two options:

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. 1.Place the files under models/gpt4chan_model_float16 or models/gpt4chan_model.
  2. 2.Place GPT-J 6B's config.json file in that same folder: config.json.
  3. 3.Download GPT-J 6B's tokenizer files (they will be automatically detected when you attempt to load GPT-4chan):
python download-model.py EleutherAI/gpt-j-6B --text-only

When you load this model in default or notebook modes, the "HTML" tab will show the generated text in 4chan format. </details>

Starting the web UI

conda activate textgen cd text-generation-webui python server.py

Then browse to

http://localhost:7860/?__theme=dark

Optionally, you can use the following command-line flags:

Basic settings
FlagDescription
-h, --helpShow this help message and exit.
--notebookLaunch the web UI in notebook mode, where the output is written to the same text box as the input.
--chatLaunch the web UI in chat mode.
--multi-userMulti-user mode. Chat histories are not saved or automatically loaded. WARNING: this is highly experimental.
--character CHARACTERThe name of the character to load in chat mode by default.
--model MODELName 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_DIRPath to directory with all the models.
--lora-dir LORA_DIRPath to directory with all the loras.
--model-menuShow a model menu in the terminal when the web UI is first launched.
--no-streamDon't stream the text output in real time.
--settings SETTINGS_FILELoad the default interface settings from this yaml file. See settings-template.yaml for an example. If you create a file called settings.yaml, this file will be loaded by default without the need to use the --settings flag.
--extensions EXTENSIONS [EXTENSIONS ...]The list of extensions to load. If you want to load more than one extension, write the names separated by spaces.
--verbosePrint the prompts to the terminal.
Model loader
FlagDescription
--loader LOADERChoose the model loader manually, otherwise, it will get autodetected. Valid options: transformers, autogptq, gptq-for-llama, exllama, exllama_hf, llamacpp, rwkv
Accelerate/transformers
FlagDescription
--cpuUse the CPU to generate text. Warning: Training on CPU is extremely slow.
--auto-devicesAutomatically split the model across the available GPU(s) and CPU.
--gpu-memory GPU_MEMORY [GPU_MEMORY ...]Maximum GPU memory in GiB to be allocated per GPU. Example: --gpu-memory 10 for a single GPU, --gpu-memory 10 5 for two GPUs. You can also set values in MiB like --gpu-memory 3500MiB.
--cpu-memory CPU_MEMORYMaximum CPU memory in GiB to allocate for offloaded weights. Same as above.
--diskIf the model is too large for your GPU(s) and CPU combined, send the remaining layers to the disk.
--disk-cache-dir DISK_CACHE_DIRDirectory to save the disk cache to. Defaults to cache/.
--load-in-8bitLoad the model with 8-bit precision (using bitsandbytes).
--bf16Load the model with bfloat16 precision. Requires NVIDIA Ampere GPU.
--no-cacheSet use_cache to False while generating text. This reduces the VRAM usage a bit with a performance cost.
--xformersUse xformer's memory efficient attention. This should increase your tokens/s.
--sdp-attentionUse torch 2.0's sdp attention.
--trust-remote-codeSet trustremotecode=True while loading a model. Necessary for ChatGLM and Falcon.
Accelerate 4-bit

⚠️ Requires minimum compute of 7.0 on Windows at the moment.

FlagDescription
--load-in-4bitLoad the model with 4-bit precision (using bitsandbytes).
--compute_dtype COMPUTE_DTYPEcompute dtype for 4-bit. Valid options: bfloat16, float16, float32.
--quant_type QUANT_TYPEquant_type for 4-bit. Valid options: nf4, fp4.
--use_double_quantusedoublequant for 4-bit.
llama.cpp
FlagDescription
--threadsNumber of threads to use.
--n_batchMaximum number of prompt tokens to batch together when calling llama_eval.
--no-mmapPrevent mmap from being used.
--mlockForce the system to keep the model in RAM.
--cache-capacity CACHE_CAPACITYMaximum cache capacity. Examples: 2000MiB, 2GiB. When provided without units, bytes will be assumed.
--n-gpu-layers N_GPU_LAYERSNumber of layers to offload to the GPU. Only works if llama-cpp-python was compiled with BLAS. Set this to 1000000000 to offload all layers to the GPU.
--n_ctx N_CTXSize of the prompt context.
--llama_cpp_seed SEEDSeed for llama-cpp models. Default 0 (random).
--n_gqa N_GQAgrouped-query attention. Must be 8 for llama2 70b.
--rms_norm_eps RMS_NORM_EPSMust be 1e-5 for llama2 70b.
AutoGPTQ
FlagDescription
--tritonUse triton.
--no_inject_fused_attentionDisable the use of fused attention, which will use less VRAM at the cost of slower inference.
--no_inject_fused_mlpTriton mode only: disable the use of fused MLP, which will use less VRAM at the cost of slower inference.
--no_use_cuda_fp16This can make models faster on some systems.
--desc_actFor models that don't have a quantizeconfig.json, this parameter is used to define whether to set descact or not in BaseQuantizeConfig.
ExLlama
FlagDescription
--gpu-splitComma-separated list of VRAM (in GB) to use per GPU device for model layers, e.g. 20,7,7
--max_seq_len MAX_SEQ_LENMaximum sequence length.
GPTQ-for-LLaMa
FlagDescription
--wbits WBITSLoad a pre-quantized model with specified precision in bits. 2, 3, 4 and 8 are supported.
--model_type MODEL_TYPEModel type of pre-quantized model. Currently LLaMA, OPT, and GPT-J are supported.
--groupsize GROUPSIZEGroup size.
--pre_layer PRE_LAYER [PRE_LAYER ...]The number of layers to allocate to the GPU. Setting this parameter enables CPU offloading for 4-bit models. For multi-gpu, write the numbers separated by spaces, eg --pre_layer 30 60.
--checkpoint CHECKPOINTThe path to the quantized checkpoint file. If not specified, it will be automatically detected.
--monkey-patchApply 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.
DeepSpeed
FlagDescription
--deepspeedEnable the use of DeepSpeed ZeRO-3 for inference via the Transformers integration.
--nvme-offload-dir NVME_OFFLOAD_DIRDeepSpeed: Directory to use for ZeRO-3 NVME offloading.
--local_rank LOCAL_RANKDeepSpeed: Optional argument for distributed setups.
RWKV
FlagDescription
--rwkv-strategy RWKV_STRATEGYRWKV: The strategy to use while loading the model. Examples: "cpu fp32", "cuda fp16", "cuda fp16i8".
--rwkv-cuda-onRWKV: Compile the CUDA kernel for better performance.
RoPE (for llama.cpp and ExLlama only)
FlagDescription
--compress_pos_emb COMPRESS_POS_EMBPositional embeddings compression factor. Should typically be set to maxseqlen / 2048.
--alpha_value ALPHA_VALUEPositional embeddings alpha factor for NTK RoPE scaling. Scaling is not identical to embedding compression. Use either this or compressposemb, not both.
Gradio
FlagDescription
--listenMake the web UI reachable from your local network.
--listen-host LISTEN_HOSTThe hostname that the server will use.
--listen-port LISTEN_PORTThe listening port that the server will use.
--shareCreate a public URL. This is useful for running the web UI on Google Colab or similar.
--auto-launchOpen the web UI in the default browser upon launch.
--gradio-auth USER:PWDset gradio authentication like "username:password"; or comma-delimit multiple like "u1:p1,u2:p2,u3:p3"
--gradio-auth-path GRADIO_AUTH_PATHSet 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
FlagDescription
--apiEnable the API extension.
--public-apiCreate a public URL for the API using Cloudfare.
--api-blocking-port BLOCKING_PORTThe listening port for the blocking API.
--api-streaming-port STREAMING_PORTThe listening port for the streaming API.
Multimodal
FlagDescription
--multimodal-pipeline PIPELINEThe multimodal pipeline to use. Examples: llava-7b, llava-13b.

Presets

Inference settings presets can be created under presets/ as yaml files. These files are detected automatically at startup.

The presets that are included by default are the result of a contest that received 7215 votes. More details can be found here.

Contributing

  • —Pull requests, suggestions, and issue reports are welcome.
  • —Make sure to carefully search existing issues before starting a new one.
  • —If you have some experience with git, testing an open pull request and leaving a comment on whether it works as expected or not is immensely helpful.
  • —A simple way to contribute, even if you are not a programmer, is to leave a 👍 on an issue or pull request that you find relevant.

Community

  • —Subreddit: https://www.reddit.com/r/oobaboogazz/
  • —Discord: https://discord.gg/jwZCF2dPQN