llama2

Arya8831/llama2

llama2 is a machine learning model from Arya8831. Use it for the machine learning task on the model card, and read the license before you ship it in a product.

A Gradio web UI for Large Language Models.

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Updated Oct 8, 2023

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Access: Public
Created: Oct 8, 2023
Updated: Oct 8, 2023
SHA: c999ef24

Model card

From the Hugging Face model README

Text generation web UI

A Gradio web UI for Large Language Models.

Its goal is to become the AUTOMATIC1111/stable-diffusion-webui of text generation.

Features

3 interface modes: default (two columns), notebook, and chat
Multiple model backends: transformers, llama.cpp, ExLlama, AutoGPTQ, GPTQ-for-LLaMa, ctransformers
Dropdown menu for quickly switching between different models
LoRA: load and unload LoRAs on the fly, train a new LoRA using QLoRA
Precise instruction templates for chat mode, including Llama-2-chat, Alpaca, Vicuna, WizardLM, StableLM, and many others
4-bit, 8-bit, and CPU inference through the transformers library
Use llama.cpp models with transformers samplers (llamacpp_HF loader)
Multimodal pipelines, including LLaVA and MiniGPT-4
Extensions framework
Custom chat characters
Very efficient text streaming
Markdown output with LaTeX rendering, to use for instance with GALACTICA
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

Windows	Linux	macOS	WSL
oobabooga-windows.zip	oobabooga-linux.zip	oobabooga-macos.zip	oobabooga-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 and more information can be found here: https://github.com/oobabooga/one-click-installers
There is no need to run the installers as admin.
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 (source):

curl -sL "https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh" > "Miniconda3.sh"
bash Miniconda3.sh

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/WSL	CPU only	`pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu`
Linux	AMD	`pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm5.4.2`
MacOS + MPS	Any	`pip3 install torch torchvision torchaudio`
Windows	NVIDIA	`pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu117`
Windows	CPU only	`pip3 install torch torchvision torchaudio`

The up-to-date commands can be found here: https://pytorch.org/get-started/locally/.

3. Install the web UI

git clone https://github.com/oobabooga/text-generation-webui
cd text-generation-webui
pip install -r requirements.txt

AMD, Metal, Intel Arc, and CPUs without AVCX2

Replace the last command above with

pip install -r requirements_nocuda.txt

Manually install llama-cpp-python using the appropriate command for your hardware: Installation from PyPI.
AMD: Manually install AutoGPTQ: Installation.
AMD: Manually install ExLlama by simply cloning it into the repositories folder (it will be automatically compiled at runtime after that):

cd text-generation-webui
mkdir repositories
cd repositories
git clone https://github.com/turboderp/exllama

bitsandbytes on older NVIDIA GPUs

bitsandbytes >= 0.39 may not work. 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 these commands:

conda activate textgen
cd text-generation-webui
pip install -r requirements.txt --upgrade

Downloading models

Models should be placed in the text-generation-webui/models folder. They are usually downloaded from Hugging Face.

Transformers or GPTQ models are made of several files and must be placed in a subfolder. Example:

text-generation-webui
├── models
│   ├── lmsys_vicuna-33b-v1.3
│   │   ├── config.json
│   │   ├── generation_config.json
│   │   ├── pytorch_model-00001-of-00007.bin
│   │   ├── pytorch_model-00002-of-00007.bin
│   │   ├── pytorch_model-00003-of-00007.bin
│   │   ├── pytorch_model-00004-of-00007.bin
│   │   ├── pytorch_model-00005-of-00007.bin
│   │   ├── pytorch_model-00006-of-00007.bin
│   │   ├── pytorch_model-00007-of-00007.bin
│   │   ├── pytorch_model.bin.index.json
│   │   ├── special_tokens_map.json
│   │   ├── tokenizer_config.json
│   │   └── tokenizer.model

GGML/GGUF models are a single file and should be placed directly into models. Example:

text-generation-webui
├── models
│   ├── llama-13b.ggmlv3.q4_K_M.bin

In both cases, you can use the "Model" tab of the UI to download the model from Hugging Face automatically. It is also possible to download via the command-line with python download-model.py organization/model (use --help to see all the options).

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:

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:

</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

Flag	Description
`-h`, `--help`	Show this help message and exit.
`--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.
`--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, ctransformers

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.

GGML/GGUF (for llama.cpp and ctransformers)

Flag	Description
`--threads`	Number of threads to use.
`--n_batch`	Maximum number of prompt tokens to batch together when calling llama_eval.
`--n-gpu-layers N_GPU_LAYERS`	Number 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_CTX`	Size of the prompt context.

llama.cpp

Flag	Description
`--no-mmap`	Prevent mmap from being used.
`--mlock`	Force the system to keep the model in RAM.
`--mul_mat_q`	Activate new mulmat kernels.
`--cache-capacity CACHE_CAPACITY`	Maximum cache capacity. Examples: 2000MiB, 2GiB. When provided without units, bytes will be assumed.
`--tensor_split TENSOR_SPLIT`	Split the model across multiple GPUs, comma-separated list of proportions, e.g. 18,17
`--llama_cpp_seed SEED`	Seed for llama-cpp models. Default 0 (random).
`--n_gqa N_GQA`	GGML only (not used by GGUF): Grouped-Query Attention. Must be 8 for llama-2 70b.
`--rms_norm_eps RMS_NORM_EPS`	GGML only (not used by GGUF): 5e-6 is a good value for llama-2 models.
`--cpu`	Use the CPU version of llama-cpp-python instead of the GPU-accelerated version.
`--cfg-cache`	llamacpp_HF: Create an additional cache for CFG negative prompts.

ctransformers

Flag	Description
`--model_type MODEL_TYPE`	Model type of pre-quantized model. Currently gpt2, gptj, gptneox, falcon, llama, mpt, starcoder (gptbigcode), dollyv2, and replit are supported.

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.
`--disable_exllama`	Disable ExLlama kernel, which can improve inference speed on some systems.

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.
`--cfg-cache`	ExLlama_HF: Create an additional cache for CFG negative prompts. Necessary to use CFG with that loader, but not necessary for CFG with base ExLlama.

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.

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, ExLlama, and transformers)

Flag	Description
`--alpha_value ALPHA_VALUE`	Positional embeddings alpha factor for NTK RoPE scaling. Use either this or compress_pos_emb, not both.
`--rope_freq_base ROPE_FREQ_BASE`	If greater than 0, will be used instead of alpha_value. Those two are related by rope_freq_base = 10000 * alpha_value ^ (64 / 63).
`--compress_pos_emb COMPRESS_POS_EMB`	Positional embeddings compression factor. Should be set to (context length) / (model's original context length). Equal to 1/rope_freq_scale.

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"
`--ssl-keyfile SSL_KEYFILE`	The path to the SSL certificate key file.
`--ssl-certfile SSL_CERTFILE`	The path to the SSL certificate cert file.

API

Flag	Description
`--api`	Enable the API extension.
`--public-api`	Create a public URL for the API using Cloudfare.
`--public-api-id PUBLIC_API_ID`	Tunnel ID for named Cloudflare Tunnel. Use together with public-api option.
`--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

If you would like to contribute to the project, check out the Contributing guidelines.

Community

Subreddit: https://www.reddit.com/r/oobabooga/
Discord: https://discord.gg/jwZCF2dPQN

Acknowledgment

In August 2023, Andreessen Horowitz (a16z) provided a generous grant to encourage and support my independent work on this project. I am extremely grateful for their trust and recognition, which will allow me to dedicate more time towards realizing the full potential of text-generation-webui.

Created: Oct 8, 2023
Updated: Oct 8, 2023