mirror of
https://github.com/oobabooga/text-generation-webui.git
synced 2024-11-26 09:40:20 +01:00
oobabooga
GitHub
commit
60bfd0b722
81
README.md
81
README.md
@ -10,28 +10,23 @@ Its goal is to become the [AUTOMATIC1111/stable-diffusion-webui](https://github.
|
||||
|
||||
## Features
|
||||
|
||||
* Dropdown menu for switching between models
|
||||
* Notebook mode that resembles OpenAI's playground
|
||||
* Chat mode for conversation and role-playing
|
||||
* Instruct mode compatible with various formats, 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
|
||||
* 3 interface modes: default, notebook, and chat
|
||||
* Multiple model backends: tranformers, llama.cpp, AutoGPTQ, GPTQ-for-LLaMa, 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](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/multimodal)
|
||||
* 8-bit and 4-bit inference through bitsandbytes
|
||||
* CPU mode for transformers models
|
||||
* [DeepSpeed ZeRO-3 inference](docs/DeepSpeed.md)
|
||||
* [Extensions](docs/Extensions.md)
|
||||
* [Custom chat characters](docs/Chat-mode.md)
|
||||
* Very efficient text streaming
|
||||
* Markdown output with LaTeX rendering, to use for instance with [GALACTICA](https://github.com/paperswithcode/galai)
|
||||
* Nice HTML output for GPT-4chan
|
||||
* [Custom chat characters](docs/Chat-mode.md)
|
||||
* Advanced chat features (send images, get audio responses with TTS)
|
||||
* Very efficient text streaming
|
||||
* Parameter presets
|
||||
* [LLaMA model](docs/LLaMA-model.md)
|
||||
* [4-bit GPTQ mode](docs/GPTQ-models-(4-bit-mode).md)
|
||||
* [LoRA (loading and training)](docs/Using-LoRAs.md)
|
||||
* [llama.cpp](docs/llama.cpp-models.md)
|
||||
* 8-bit and 4-bit through bitsandbytes
|
||||
* Layers splitting across GPU(s), CPU, and disk
|
||||
* CPU mode
|
||||
* [FlexGen](docs/FlexGen.md)
|
||||
* [DeepSpeed ZeRO-3](docs/DeepSpeed.md)
|
||||
* API [with](https://github.com/oobabooga/text-generation-webui/blob/main/api-example-stream.py) streaming and [without](https://github.com/oobabooga/text-generation-webui/blob/main/api-example.py) streaming
|
||||
* [Extensions](docs/Extensions.md) - see the [user extensions list](https://github.com/oobabooga/text-generation-webui-extensions)
|
||||
* API, including endpoints for websocket streaming ([see the examples](https://github.com/oobabooga/text-generation-webui/blob/main/api-examples))
|
||||
|
||||
To learn how to use the various features, check out the Documentation: https://github.com/oobabooga/text-generation-webui/tree/main/docs
|
||||
|
||||
## Installation
|
||||
|
||||
@ -95,14 +90,6 @@ cd text-generation-webui
|
||||
pip install -r requirements.txt
|
||||
```
|
||||
|
||||
#### 4. Install GPTQ
|
||||
|
||||
The base installation covers [transformers](https://github.com/huggingface/transformers) models (`AutoModelForCausalLM` and `AutoModelForSeq2SeqLM` specifically) and [llama.cpp](https://github.com/ggerganov/llama.cpp) (GGML) models.
|
||||
|
||||
To use GPTQ models, the additional installation steps below are necessary:
|
||||
|
||||
[GPTQ models (4 bit mode)](https://github.com/oobabooga/text-generation-webui/blob/main/docs/GPTQ-models-(4-bit-mode).md)
|
||||
|
||||
#### llama.cpp with GPU acceleration
|
||||
|
||||
Requires the additional compilation step described here: [GPU acceleration](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp-models.md#gpu-acceleration).
|
||||
@ -154,9 +141,7 @@ For example:
|
||||
|
||||
python download-model.py facebook/opt-1.3b
|
||||
|
||||
* If you want to download a model manually, note that all you need are the json, txt, and pytorch\*.bin (or model*.safetensors) files. The remaining files are not necessary.
|
||||
|
||||
* Set env vars `HF_USER` and `HF_PASS` to your Hugging Face username and password (or [User Access Token](https://huggingface.co/settings/tokens)) to download a protected model. The model's terms must first be accepted on the HF website.
|
||||
To download a protected model, set env vars `HF_USER` and `HF_PASS` to your Hugging Face username and password (or [User Access Token](https://huggingface.co/settings/tokens)). The model's terms must first be accepted on the HF website.
|
||||
|
||||
#### GGML models
|
||||
|
||||
@ -164,6 +149,11 @@ You can drop these directly into the `models/` folder, making sure that the file
|
||||
|
||||
#### GPT-4chan
|
||||
|
||||
<details>
|
||||
<summary>
|
||||
Instructions
|
||||
</summary>
|
||||
|
||||
[GPT-4chan](https://huggingface.co/ykilcher/gpt-4chan) has been shut down from Hugging Face, so you need to download it elsewhere. You have two options:
|
||||
|
||||
* Torrent: [16-bit](https://archive.org/details/gpt4chan_model_float16) / [32-bit](https://archive.org/details/gpt4chan_model)
|
||||
@ -181,6 +171,9 @@ After downloading the model, follow these steps:
|
||||
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
|
||||
@ -252,10 +245,18 @@ Optionally, you can use the following command-line flags:
|
||||
| `--n_ctx N_CTX` | Size of the prompt context. |
|
||||
| `--llama_cpp_seed SEED` | Seed for llama-cpp models. Default 0 (random). |
|
||||
|
||||
#### GPTQ
|
||||
#### AutoGPTQ
|
||||
|
||||
| Flag | Description |
|
||||
|------------------|-------------|
|
||||
| `--triton` | Use triton. |
|
||||
| `--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. |
|
||||
|
||||
#### GPTQ-for-LLaMa
|
||||
|
||||
| Flag | Description |
|
||||
|---------------------------|-------------|
|
||||
| `--gptq-for-llama` | Use GPTQ-for-LLaMa to load the GPTQ model instead of AutoGPTQ. |
|
||||
| `--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. |
|
||||
@ -266,14 +267,6 @@ Optionally, you can use the following command-line flags:
|
||||
| `--warmup_autotune` | (triton) Enable warmup autotune. |
|
||||
| `--fused_mlp` | (triton) Enable fused mlp. |
|
||||
|
||||
#### AutoGPTQ
|
||||
|
||||
| Flag | Description |
|
||||
|------------------|-------------|
|
||||
| `--autogptq` | Use AutoGPTQ for loading quantized models instead of the internal GPTQ loader. |
|
||||
| `--triton` | Use triton. |
|
||||
|` --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. |
|
||||
|
||||
#### FlexGen
|
||||
|
||||
| Flag | Description |
|
||||
@ -331,15 +324,7 @@ Out of memory errors? [Check the low VRAM guide](docs/Low-VRAM-guide.md).
|
||||
|
||||
Inference settings presets can be created under `presets/` as yaml files. These files are detected automatically at startup.
|
||||
|
||||
By default, 10 presets based on NovelAI and KoboldAI presets are included. These were selected out of a sample of 43 presets after applying a K-Means clustering algorithm and selecting the elements closest to the average of each cluster.
|
||||
|
||||
[Visualization](https://user-images.githubusercontent.com/112222186/228956352-1addbdb9-2456-465a-b51d-089f462cd385.png)
|
||||
|
||||
## Documentation
|
||||
|
||||
Make sure to check out the documentation for an in-depth guide on how to use the web UI.
|
||||
|
||||
https://github.com/oobabooga/text-generation-webui/tree/main/docs
|
||||
By default, 10 presets based on NovelAI and KoboldAI presets are included. These were selected out of a sample of 43 presets after applying a K-Means clustering algorithm and selecting the elements closest to the average of each cluster: [tSNE visualization](https://user-images.githubusercontent.com/112222186/228956352-1addbdb9-2456-465a-b51d-089f462cd385.png).
|
||||
|
||||
## Contributing
|
||||
|
||||
|
||||
@ -6,14 +6,6 @@ GPTQ is a clever quantization algorithm that lightly reoptimizes the weights dur
|
||||
|
||||
There are two ways of loading GPTQ models in the web UI at the moment:
|
||||
|
||||
* Using GPTQ-for-LLaMa directly:
|
||||
* faster CPU offloading
|
||||
* faster multi-GPU inference
|
||||
* supports loading LoRAs using a monkey patch
|
||||
* included by default in the one-click installers
|
||||
* requires you to manually figure out the wbits/groupsize/model_type parameters for the model to be able to load it
|
||||
* supports either only cuda or only triton depending on the branch
|
||||
|
||||
* Using AutoGPTQ:
|
||||
* supports more models
|
||||
* standardized (no need to guess any parameter)
|
||||
@ -21,8 +13,59 @@ There are two ways of loading GPTQ models in the web UI at the moment:
|
||||
* ~no wheels are presently available so it requires manual compilation~
|
||||
* supports loading both triton and cuda models
|
||||
|
||||
* Using GPTQ-for-LLaMa directly:
|
||||
* faster CPU offloading
|
||||
* faster multi-GPU inference
|
||||
* supports loading LoRAs using a monkey patch
|
||||
* requires you to manually figure out the wbits/groupsize/model_type parameters for the model to be able to load it
|
||||
* supports either only cuda or only triton depending on the branch
|
||||
|
||||
For creating new quantizations, I recommend using AutoGPTQ: https://github.com/PanQiWei/AutoGPTQ
|
||||
|
||||
## AutoGPTQ
|
||||
|
||||
### Installation
|
||||
|
||||
No additional steps are necessary as AutoGPTQ is already in the `requirements.txt` for the webui. If you still want or need to install it manually for whatever reason, these are the commands:
|
||||
|
||||
```
|
||||
conda activate textgen
|
||||
git clone https://github.com/PanQiWei/AutoGPTQ.git && cd AutoGPTQ
|
||||
pip install .
|
||||
```
|
||||
|
||||
The last command requires `nvcc` to be installed (see the [instructions above](https://github.com/oobabooga/text-generation-webui/blob/main/docs/GPTQ-models-(4-bit-mode).md#step-1-install-nvcc)).
|
||||
|
||||
### Usage
|
||||
|
||||
When you quantize a model using AutoGPTQ, a folder containing a filed called `quantize_config.json` will be generated. Place that folder inside your `models/` folder and load it with the `--autogptq` flag:
|
||||
|
||||
```
|
||||
python server.py --autogptq --model model_name
|
||||
```
|
||||
|
||||
Alternatively, check the `autogptq` box in the "Model" tab of the UI before loading the model.
|
||||
|
||||
### Offloading
|
||||
|
||||
In order to do CPU offloading or multi-gpu inference with AutoGPTQ, use the `--gpu-memory` flag. It is currently somewhat slower than offloading with the `--pre_layer` option in GPTQ-for-LLaMA.
|
||||
|
||||
For CPU offloading:
|
||||
|
||||
```
|
||||
python server.py --autogptq --gpu-memory 3000MiB --model model_name
|
||||
```
|
||||
|
||||
For multi-GPU inference:
|
||||
|
||||
```
|
||||
python server.py --autogptq --gpu-memory 3000MiB 6000MiB --model model_name
|
||||
```
|
||||
|
||||
### Using LoRAs with AutoGPTQ
|
||||
|
||||
Not supported yet.
|
||||
|
||||
## GPTQ-for-LLaMa
|
||||
|
||||
GPTQ-for-LLaMa is the original adaptation of GPTQ for the LLaMA model. It was made possible by [@qwopqwop200](https://github.com/qwopqwop200/GPTQ-for-LLaMa): https://github.com/qwopqwop200/GPTQ-for-LLaMa
|
||||
@ -108,23 +151,21 @@ These are models that you can simply download and place in your `models` folder.
|
||||
|
||||
### Starting the web UI:
|
||||
|
||||
Use the `--gptq-for-llama` flag.
|
||||
|
||||
For the models converted without `group-size`:
|
||||
|
||||
```
|
||||
python server.py --model llama-7b-4bit
|
||||
python server.py --model llama-7b-4bit --gptq-for-llama
|
||||
```
|
||||
|
||||
For the models converted with `group-size`:
|
||||
|
||||
```
|
||||
python server.py --model llama-13b-4bit-128g
|
||||
python server.py --model llama-13b-4bit-128g --gptq-for-llama --wbits 4 --groupsize 128
|
||||
```
|
||||
|
||||
The command-line flags `--wbits` and `--groupsize` are automatically detected based on the folder names, but you can also specify them manually like
|
||||
|
||||
```
|
||||
python server.py --model llama-13b-4bit-128g --wbits 4 --groupsize 128
|
||||
```
|
||||
The command-line flags `--wbits` and `--groupsize` are automatically detected based on the folder names in many cases.
|
||||
|
||||
### CPU offloading
|
||||
|
||||
@ -171,46 +212,4 @@ pip install git+https://github.com/sterlind/GPTQ-for-LLaMa.git@lora_4bit
|
||||
python server.py --model llama-7b-4bit-128g --listen --lora tloen_alpaca-lora-7b --monkey-patch
|
||||
```
|
||||
|
||||
## AutoGPTQ
|
||||
|
||||
### Installation
|
||||
|
||||
No additional steps are necessary as AutoGPTQ is already in the `requirements.txt` for the webui. If you still want or need to install it manually for whatever reason, these are the commands:
|
||||
|
||||
```
|
||||
conda activate textgen
|
||||
git clone https://github.com/PanQiWei/AutoGPTQ.git && cd AutoGPTQ
|
||||
pip install .
|
||||
```
|
||||
|
||||
The last command requires `nvcc` to be installed (see the [instructions above](https://github.com/oobabooga/text-generation-webui/blob/main/docs/GPTQ-models-(4-bit-mode).md#step-1-install-nvcc)).
|
||||
|
||||
### Usage
|
||||
|
||||
When you quantize a model using AutoGPTQ, a folder containing a filed called `quantize_config.json` will be generated. Place that folder inside your `models/` folder and load it with the `--autogptq` flag:
|
||||
|
||||
```
|
||||
python server.py --autogptq --model model_name
|
||||
```
|
||||
|
||||
Alternatively, check the `autogptq` box in the "Model" tab of the UI before loading the model.
|
||||
|
||||
### Offloading
|
||||
|
||||
In order to do CPU offloading or multi-gpu inference with AutoGPTQ, use the `--gpu-memory` flag. It is currently somewhat slower than offloading with the `--pre_layer` option in GPTQ-for-LLaMA.
|
||||
|
||||
For CPU offloading:
|
||||
|
||||
```
|
||||
python server.py --autogptq --gpu-memory 3000MiB --model model_name
|
||||
```
|
||||
|
||||
For multi-GPU inference:
|
||||
|
||||
```
|
||||
python server.py --autogptq --gpu-memory 3000MiB 6000MiB --model model_name
|
||||
```
|
||||
|
||||
### Using LoRAs with AutoGPTQ
|
||||
|
||||
Not supported yet.
|
||||
|
||||
@ -180,3 +180,11 @@ llama-65b-gptq-3bit:
|
||||
.*bluemoonrp-(30|13)b:
|
||||
mode: 'instruct'
|
||||
instruction_template: 'Bluemoon'
|
||||
truncation_length: 4096
|
||||
.*Nous-Hermes-13b:
|
||||
mode: 'instruct'
|
||||
instruction_template: 'Alpaca'
|
||||
.*airoboros-13b-gpt4:
|
||||
mode: 'instruct'
|
||||
instruction_template: 'Vicuna-v1.1'
|
||||
truncation_length: 4096
|
||||
|
||||
@ -325,6 +325,10 @@ def generate_chat_reply(text, history, state, regenerate=False, _continue=False,
|
||||
# Same as above but returns HTML for the UI
|
||||
def generate_chat_reply_wrapper(text, start_with, state, regenerate=False, _continue=False):
|
||||
if start_with != '' and _continue == False:
|
||||
if regenerate == True:
|
||||
text = remove_last_message()
|
||||
regenerate = False
|
||||
|
||||
_continue = True
|
||||
send_dummy_message(text)
|
||||
send_dummy_reply(start_with)
|
||||
|
||||
@ -81,10 +81,10 @@ def load_model(model_name):
|
||||
logger.error('The path to the model does not exist. Exiting.')
|
||||
return None, None
|
||||
|
||||
if shared.args.autogptq:
|
||||
load_func = AutoGPTQ_loader
|
||||
elif shared.args.wbits > 0:
|
||||
if shared.args.gptq_for_llama:
|
||||
load_func = GPTQ_loader
|
||||
elif Path(f'{shared.args.model_dir}/{model_name}/quantize_config.json').exists() or shared.args.wbits > 0:
|
||||
load_func = AutoGPTQ_loader
|
||||
elif shared.model_type == 'llamacpp':
|
||||
load_func = llamacpp_loader
|
||||
elif shared.model_type == 'rwkv':
|
||||
|
||||
@ -141,7 +141,8 @@ parser.add_argument('--warmup_autotune', action='store_true', help='(triton) Ena
|
||||
parser.add_argument('--fused_mlp', action='store_true', help='(triton) Enable fused mlp.')
|
||||
|
||||
# AutoGPTQ
|
||||
parser.add_argument('--autogptq', action='store_true', help='Use AutoGPTQ for loading quantized models instead of the internal GPTQ loader.')
|
||||
parser.add_argument('--gptq-for-llama', action='store_true', help='Use GPTQ-for-LLaMa to load the GPTQ model instead of AutoGPTQ.')
|
||||
parser.add_argument('--autogptq', action='store_true', help='DEPRECATED')
|
||||
parser.add_argument('--triton', action='store_true', help='Use triton.')
|
||||
parser.add_argument('--desc_act', action='store_true', help='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.')
|
||||
|
||||
@ -181,12 +182,9 @@ parser.add_argument('--multimodal-pipeline', type=str, default=None, help='The m
|
||||
args = parser.parse_args()
|
||||
args_defaults = parser.parse_args([])
|
||||
|
||||
# Deprecation warnings for parameters that have been renamed
|
||||
deprecated_dict = {}
|
||||
for k in deprecated_dict:
|
||||
if getattr(args, k) != deprecated_dict[k][1]:
|
||||
logger.warning(f"--{k} is deprecated and will be removed. Use --{deprecated_dict[k][0]} instead.")
|
||||
setattr(args, deprecated_dict[k][0], getattr(args, k))
|
||||
# Deprecation warnings
|
||||
if args.autogptq:
|
||||
logger.warning('--autogptq has been deprecated and will be removed soon. AutoGPTQ is now used by default for GPTQ models.')
|
||||
|
||||
# Security warnings
|
||||
if args.trust_remote_code:
|
||||
|
||||
@ -30,7 +30,7 @@ theme = gr.themes.Default(
|
||||
|
||||
|
||||
def list_model_elements():
|
||||
elements = ['cpu_memory', 'auto_devices', 'disk', 'cpu', 'bf16', 'load_in_8bit', 'trust_remote_code', 'load_in_4bit', 'compute_dtype', 'quant_type', 'use_double_quant', 'wbits', 'groupsize', 'model_type', 'pre_layer', 'autogptq', 'triton', 'desc_act', 'threads', 'n_batch', 'no_mmap', 'mlock', 'n_gpu_layers', 'n_ctx', 'llama_cpp_seed']
|
||||
elements = ['cpu_memory', 'auto_devices', 'disk', 'cpu', 'bf16', 'load_in_8bit', 'trust_remote_code', 'load_in_4bit', 'compute_dtype', 'quant_type', 'use_double_quant', 'gptq_for_llama', 'wbits', 'groupsize', 'model_type', 'pre_layer', 'triton', 'desc_act', 'threads', 'n_batch', 'no_mmap', 'mlock', 'n_gpu_layers', 'n_ctx', 'llama_cpp_seed']
|
||||
for i in range(torch.cuda.device_count()):
|
||||
elements.append(f'gpu_memory_{i}')
|
||||
|
||||
|
||||
@ -393,12 +393,12 @@ def create_model_menus():
|
||||
with gr.Row():
|
||||
with gr.Column():
|
||||
gr.Markdown('AutoGPTQ')
|
||||
shared.gradio['autogptq'] = gr.Checkbox(label="autogptq", value=shared.args.autogptq, info='Activate AutoGPTQ loader. gpu-memory should be used for CPU offloading instead of pre_layer.')
|
||||
shared.gradio['triton'] = gr.Checkbox(label="triton", value=shared.args.triton)
|
||||
shared.gradio['desc_act'] = gr.Checkbox(label="desc_act", value=shared.args.desc_act, info='\'desc_act\', \'wbits\', and \'groupsize\' are used for old models without a quantize_config.json.')
|
||||
|
||||
with gr.Column():
|
||||
gr.Markdown('GPTQ-for-LLaMa')
|
||||
shared.gradio['gptq_for_llama'] = gr.Checkbox(label="gptq-for-llama", value=shared.args.gptq_for_llama, info='Use GPTQ-for-LLaMa to load the GPTQ model instead of AutoGPTQ. pre_layer should be used for CPU offloading instead of gpu-memory.')
|
||||
with gr.Row():
|
||||
shared.gradio['wbits'] = gr.Dropdown(label="wbits", choices=["None", 1, 2, 3, 4, 8], value=shared.args.wbits if shared.args.wbits > 0 else "None")
|
||||
shared.gradio['groupsize'] = gr.Dropdown(label="groupsize", choices=["None", 32, 64, 128, 1024], value=shared.args.groupsize if shared.args.groupsize > 0 else "None")
|
||||
@ -1049,6 +1049,7 @@ if __name__ == "__main__":
|
||||
'mode': shared.settings['mode'],
|
||||
'skip_special_tokens': shared.settings['skip_special_tokens'],
|
||||
'custom_stopping_strings': shared.settings['custom_stopping_strings'],
|
||||
'truncation_length': shared.settings['truncation_length'],
|
||||
}
|
||||
|
||||
shared.model_config.move_to_end('.*', last=False) # Move to the beginning
|
||||
|
||||
Loading…
Reference in New Issue
Block a user