MERGED 1.5.1 macOS Merged Version

This is a development version and I have not added many changes I had planned. Please feel free to use at your own risk as there may be bugs not yet found.

Items Added to this version.

"Stop Server" under the sessions tab. Use with caution if in multi-user, will probably disable this if in multi-user mode, however it offers better shutdown than just killing the process on the server.
Added Python Class for handling diverse GPU/Compute devices like CUDA, CPU or MPS Changed code to use "torch device" once set initially to a device. Will fall back to CPU.

Items working and tested on macOS

More support for Apple Silicon M1/M2 processors.
Working with new llama-cpp-python 0.1.81
Works with LLaMa2 Models
- There GGML models will need conversion to GGUF format if using llama-cpp-python 0.1.81.
- Earlier version llama-coo-python still works
- Have not concluded testing of library dependencies, will have that updated in build instructions for oobagooba-macOS.
- Still mainly supporting GGML, now GGUF, GG-Universal Format files. You will have to convert your GGML files to GGUF format.

Removed from this

Tried to continue what was already started in removing FlexGEN from the repo.
Removed Docker - if someone wants to help maintain for macOS, let me know.
SLowly removing information on CUDA as it is not relevant to macOS.

Updated Installation Instructions for libraries in the oobabooga-macOS Quickstart and the longer Building Apple Silicon Support

GGML support is in this release, and has not been extensively tested. From the look of upstream commits, there are some changes which must be made before this will work with Llama2 models.

If you want the most recent version, from the oobabooga reposiotry, go here: oobabooga/text-generation-webgui

Otherwise, use these instructions I have on putting together the macOS Python environment. These instructions are not only useful for setting up oobabooga, but also for anyone working in data analytics, machine learning, deep learning, scientific computing, and other areas that can benefit from an optimized Python GPU environment on Apple Silicon.

I will be updating this README file with new information specifically regarding macOS and Apple Silicon.

I would like to work closely with the oobabooga team and try to implement similar solutions so the web UI can have a similar look and feel.

Maintaining and improving support for macOS and Apple Silicon in this project has required significant research, debugging, and development effort. If you find my contributions helpful and want to show your appreciation, you can Buy Me a Coffee, sponsor this project, or consider me for job opportunities.

While the focus of this branch is to enhance macOS and Apple Silicon support, I aim to maintain compatibility with Linux and POSIX operating systems. Contributions and feedback related to Linux compatibility are always welcome.

Anyone who would like to assist with supporting Apple Silicon, let me know. There is much to do and I can only do so much by myself.

MERGED 1.5.1 macOS Merged Version

Features

3 interface modes: default, notebook, and chat
Multiple model backends: tranformers, llama.cpp, AutoGPTQ, GPTQ-for-LLaMa, ExLlama, RWKV, FlexGen
Dropdown menu for quickly switching between different models
LoRA: load and unload LoRAs on the fly, load multiple LoRAs at the same time, train a new LoRA
Precise instruction templates for chat mode, including Alpaca, Vicuna, Open Assistant, Dolly, Koala, ChatGLM, MOSS, RWKV-Raven, Galactica, StableLM, WizardLM, Baize, Ziya, Chinese-Vicuna, MPT, INCITE, Wizard Mega, KoAlpaca, Vigogne, Bactrian, h2o, and OpenBuddy
Multimodal pipelines, including LLaVA and MiniGPT-4
8-bit and 4-bit inference through bitsandbytes CPU only mode for macOS, bitsandbytes does not support Apple Silicon GPU
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/unixwzrd/text-generation-webui/tree/main/docs

Installation

Currently only manual installation until an installer can be put together.

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.

NOTE: You may want to create symbolic links in the model directory since sometimes there are multiple models downloaded with different quantizations, or on external storage due to space on th edisk where you insatlled this. If you have external storge, like I do, you can create a link by going to the models directory and creating the link to your model.

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

Instructions

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

Torrent: 16-bit / 32-bit
Direct download: 16-bit / 32-bit

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:

Place the files under models/gpt4chan_model_float16 or models/gpt4chan_model.
Place GPT-J 6B's config.json file in that same folder: config.json.
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.

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.
`--multi-user`	Multi-user mode. Chat histories are not saved or automatically loaded. WARNING: this is highly experimental.
`--character CHARACTER`	The name of the character to load in chat mode by default.
`--model MODEL`	Name of the model to load by default.
`--lora LORA [LORA ...]`	The list of LoRAs to load. If you want to load more than one LoRA, write the names separated by spaces.
`--model-dir MODEL_DIR`	Path to directory with all the models.
`--lora-dir LORA_DIR`	Path to directory with all the loras.
`--model-menu`	Show a model menu in the terminal when the web UI is first launched.
`--no-stream`	Don't stream the text output in real time.
`--settings SETTINGS_FILE`	Load the default interface settings from this yaml file. See `settings-template.yaml` for an example. If you create a file called `settings.yaml`, this file will be loaded by default without the need to use the `--settings` flag.
`--extensions EXTENSIONS [EXTENSIONS ...]`	The list of extensions to load. If you want to load more than one extension, write the names separated by spaces.
`--verbose`	Print the prompts to the terminal.

Model loader

Flag	Description
`--loader LOADER`	Choose the model loader manually, otherwise, it will get autodetected. Valid options: transformers, autogptq, gptq-for-llama, exllama, exllama_hf, llamacpp, rwkv

Accelerate/transformers

Flag	Description
`--cpu`	Use the CPU to generate text. Warning: Training on CPU is extremely slow.
`--auto-devices`	Automatically split the model across the available GPU(s) and CPU.
`--gpu-memory GPU_MEMORY [GPU_MEMORY ...]`	Maximum GPU memory in GiB to be allocated per GPU. Example: `--gpu-memory 10` for a single GPU, `--gpu-memory 10 5` for two GPUs. You can also set values in MiB like `--gpu-memory 3500MiB`.
`--cpu-memory CPU_MEMORY`	Maximum CPU memory in GiB to allocate for offloaded weights. Same as above.
`--disk`	If the model is too large for your GPU(s) and CPU combined, send the remaining layers to the disk.
`--disk-cache-dir DISK_CACHE_DIR`	Directory to save the disk cache to. Defaults to `cache/`.
`--load-in-8bit`	Load the model with 8-bit precision (using bitsandbytes).
`--bf16`	Load the model with bfloat16 precision. Requires NVIDIA Ampere GPU.
`--no-cache`	Set `use_cache` to False while generating text. This reduces the VRAM usage a bit with a performance cost.
`--xformers`	Use xformer's memory efficient attention. This should increase your tokens/s.
`--sdp-attention`	Use torch 2.0's sdp attention.
`--trust-remote-code`	Set trust_remote_code=True while loading a model. Necessary for ChatGLM and Falcon.

Accelerate 4-bit

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

Flag	Description
`--load-in-4bit`	Load the model with 4-bit precision (using bitsandbytes).
`--compute_dtype COMPUTE_DTYPE`	compute dtype for 4-bit. Valid options: bfloat16, float16, float32.
`--quant_type QUANT_TYPE`	quant_type for 4-bit. Valid options: nf4, fp4.
`--use_double_quant`	use_double_quant for 4-bit.

llama.cpp

Flag	Description
`--threads`	Number of threads to use.
`--n_batch`	Maximum number of prompt tokens to batch together when calling llama_eval.
`--no-mmap`	Prevent mmap from being used.
`--mlock`	Force the system to keep the model in RAM.
`--cache-capacity CACHE_CAPACITY`	Maximum cache capacity. Examples: 2000MiB, 2GiB. When provided without units, bytes will be assumed. Does not apply for Apple Silicon GPU since it uses unified memory.
`--n-gpu-layers N_GPU_LAYERS`	Number of layers to offload to the GPU. Only works if llama-cpp-python was compiled with Apple Silicon GPU Support for BLAS and llama-cpp using Metal. Load the model and look for **llama_model_load_internal: n_layer in ths STDERR and this will show you the number of layers in the model. Set this value to that number or possibly n + 2, This si very sensitive now an will overrun your data area or tensor cache causing a segmentation fault.
`--n_ctx N_CTX`	Size of the prompt context.
`--llama_cpp_seed SEED`	Seed for llama-cpp models. Default 0 (random).
`--n_gqa N_GQA`	grouped-query attention. Must be 8 for llama2 70b.
`--rms_norm_eps RMS_NORM_EPS`	Must be 1e-5 for llama2 70b.

AutoGPTQ

Flag	Description
`--triton`	Use triton.
`--no_inject_fused_attention`	Disable the use of fused attention, which will use less VRAM at the cost of slower inference.
`--no_inject_fused_mlp`	Triton mode only: disable the use of fused MLP, which will use less VRAM at the cost of slower inference.
`--no_use_cuda_fp16`	This can make models faster on some systems.
`--desc_act`	For models that don't have a quantize_config.json, this parameter is used to define whether to set desc_act or not in BaseQuantizeConfig.

ExLlama

Flag	Description
`--gpu-split`	Comma-separated list of VRAM (in GB) to use per GPU device for model layers, e.g. `20,7,7`
`--max_seq_len MAX_SEQ_LEN`	Maximum sequence length.

GPTQ-for-LLaMa

Flag	Description
`--wbits WBITS`	Load a pre-quantized model with specified precision in bits. 2, 3, 4 and 8 are supported.
`--model_type MODEL_TYPE`	Model type of pre-quantized model. Currently LLaMA, OPT, and GPT-J are supported.
`--groupsize GROUPSIZE`	Group size.
`--pre_layer PRE_LAYER [PRE_LAYER ...]`	The number of layers to allocate to the GPU. Setting this parameter enables CPU offloading for 4-bit models. For multi-gpu, write the numbers separated by spaces, eg `--pre_layer 30 60`.
`--checkpoint CHECKPOINT`	The path to the quantized checkpoint file. If not specified, it will be automatically detected.
`--monkey-patch`	Apply the monkey patch for using LoRAs with quantized models.
`--quant_attn`	(triton) Enable quant attention.
`--warmup_autotune`	(triton) Enable warmup autotune.
`--fused_mlp`	(triton) Enable fused mlp.

DeepSpeed

Flag	Description
`--deepspeed`	Enable the use of DeepSpeed ZeRO-3 for inference via the Transformers integration.
`--nvme-offload-dir NVME_OFFLOAD_DIR`	DeepSpeed: Directory to use for ZeRO-3 NVME offloading.
`--local_rank LOCAL_RANK`	DeepSpeed: Optional argument for distributed setups.

RWKV

Flag	Description
`--rwkv-strategy RWKV_STRATEGY`	RWKV: The strategy to use while loading the model. Examples: "cpu fp32", "cuda fp16", "cuda fp16i8".
`--rwkv-cuda-on`	RWKV: Compile the CUDA kernel for better performance.

RoPE (for llama.cpp and ExLlama only)

Flag	Description
`--compress_pos_emb COMPRESS_POS_EMB`	Positional embeddings compression factor. Should typically be set to max_seq_len / 2048.
`--alpha_value ALPHA_VALUE`	Positional embeddings alpha factor for NTK RoPE scaling. Scaling is not identical to embedding compression. Use either this or compress_pos_emb, not both.

Gradio

Flag	Description
`--listen`	Make the web UI reachable from your local network.
`--listen-host LISTEN_HOST`	The hostname that the server will use.
`--listen-port LISTEN_PORT`	The listening port that the server will use.
`--share`	Create a public URL. This is useful for running the web UI on Google Colab or similar.
`--auto-launch`	Open the web UI in the default browser upon launch.
`--gradio-auth USER:PWD`	set gradio authentication like "username:password"; or comma-delimit multiple like "u1:p1,u2:p2,u3:p3"
`--gradio-auth-path GRADIO_AUTH_PATH`	Set the gradio authentication file path. The file should contain one or more user:password pairs in this format: "u1:p1,u2:p2,u3:p3"

API

Flag	Description
`--api`	Enable the API extension.
`--public-api`	Create a public URL for the API using Cloudfare.
`--api-blocking-port BLOCKING_PORT`	The listening port for the blocking API.
`--api-streaming-port STREAMING_PORT`	The listening port for the streaming API.

Multimodal

Flag	Description
`--multimodal-pipeline PIPELINE`	The multimodal pipeline to use. Examples: `llava-7b`, `llava-13b`.

Presets

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

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

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

I will be checking in on the oobabooga Discord server at the #mac-setup channel.

Discord: https://discord.gg/jwZCF2dPQN

Credits

The devopers and maintainers of the original oobabooga repository: oobabooga/text-generation-webgui
Gradio dropdown menu refresh button, code for reloading the interface: https://github.com/AUTOMATIC1111/stable-diffusion-webui
Godlike preset: https://github.com/KoboldAI/KoboldAI-Client/wiki/Settings-Presets
Code for some of the sliders: https://github.com/PygmalionAI/gradio-ui/

unixwzrd / text-generation-webui-macos