mirror of
https://github.com/oobabooga/text-generation-webui.git
synced 2024-10-01 01:26:03 -04:00
Merge branch 'main' into add-train-lora-tab
This commit is contained in:
Alex "mcmonkey" Goodwin
commit
e439228ed8
29
.gitignore
vendored
29
.gitignore
vendored
@ -1,26 +1,21 @@
|
||||
cache/*
|
||||
characters/*
|
||||
extensions/silero_tts/outputs/*
|
||||
extensions/elevenlabs_tts/outputs/*
|
||||
extensions/sd_api_pictures/outputs/*
|
||||
logs/*
|
||||
loras/*
|
||||
models/*
|
||||
softprompts/*
|
||||
torch-dumps/*
|
||||
cache
|
||||
characters
|
||||
training/datasets
|
||||
extensions/silero_tts/outputs
|
||||
extensions/elevenlabs_tts/outputs
|
||||
extensions/sd_api_pictures/outputs
|
||||
logs
|
||||
loras
|
||||
models
|
||||
softprompts
|
||||
torch-dumps
|
||||
*pycache*
|
||||
*/*pycache*
|
||||
*/*/pycache*
|
||||
venv/
|
||||
.venv/
|
||||
repositories
|
||||
|
||||
settings.json
|
||||
img_bot*
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img_me*
|
||||
|
||||
!characters/Example.json
|
||||
!characters/Example.png
|
||||
!loras/place-your-loras-here.txt
|
||||
!models/place-your-models-here.txt
|
||||
!softprompts/place-your-softprompts-here.txt
|
||||
!torch-dumps/place-your-pt-models-here.txt
|
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|
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@ -176,10 +176,10 @@ Optionally, you can use the following command-line flags:
|
||||
| `--cai-chat` | Launch the web UI in chat mode with a style similar to Character.AI's. If the file `img_bot.png` or `img_bot.jpg` exists in the same folder as server.py, this image will be used as the bot's profile picture. Similarly, `img_me.png` or `img_me.jpg` will be used as your profile picture. |
|
||||
| `--cpu` | Use the CPU to generate text.|
|
||||
| `--load-in-8bit` | Load the model with 8-bit precision.|
|
||||
| `--load-in-4bit` | DEPRECATED: use `--gptq-bits 4` instead. |
|
||||
| `--gptq-bits GPTQ_BITS` | GPTQ: Load a pre-quantized model with specified precision. 2, 3, 4 and 8 (bit) are supported. Currently only works with LLaMA and OPT. |
|
||||
| `--gptq-model-type MODEL_TYPE` | GPTQ: Model type of pre-quantized model. Currently only LLaMa and OPT are supported. |
|
||||
| `--gptq-pre-layer GPTQ_PRE_LAYER` | GPTQ: The number of layers to preload. |
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||||
| `--wbits WBITS` | GPTQ: Load a pre-quantized model with specified precision in bits. 2, 3, 4 and 8 are supported. |
|
||||
| `--model_type MODEL_TYPE` | GPTQ: Model type of pre-quantized model. Currently only LLaMA and OPT are supported. |
|
||||
| `--groupsize GROUPSIZE` | GPTQ: Group size. |
|
||||
| `--pre_layer PRE_LAYER` | GPTQ: The number of layers to preload. |
|
||||
| `--bf16` | Load the model with bfloat16 precision. Requires NVIDIA Ampere GPU. |
|
||||
| `--auto-devices` | Automatically split the model across the available GPU(s) and CPU.|
|
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| `--disk` | If the model is too large for your GPU(s) and CPU combined, send the remaining layers to the disk. |
|
||||
|
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@ -23,3 +23,9 @@ div.svelte-362y77>*, div.svelte-362y77>.form>* {
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.pending.svelte-1ed2p3z {
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opacity: 1;
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}
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#extensions {
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padding: 0;
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padding: 0;
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}
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|
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10
css/main.css
10
css/main.css
@ -54,3 +54,13 @@ ol li p, ul li p {
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.gradio-container-3-18-0 .prose * h1, h2, h3, h4 {
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color: white;
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||||
}
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|
||||
.gradio-container {
|
||||
max-width: 100% !important;
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padding-top: 0 !important;
|
||||
}
|
||||
|
||||
#extensions {
|
||||
padding: 15px;
|
||||
padding: 15px;
|
||||
}
|
||||
|
||||
@ -11,7 +11,7 @@ let extensions = document.getElementById('extensions');
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main_parent.addEventListener('click', function(e) {
|
||||
// Check if the main element is visible
|
||||
if (main.offsetHeight > 0 && main.offsetWidth > 0) {
|
||||
extensions.style.display = 'block';
|
||||
extensions.style.display = 'flex';
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||||
} else {
|
||||
extensions.style.display = 'none';
|
||||
}
|
||||
|
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@ -118,7 +118,7 @@ def get_download_links_from_huggingface(model, branch):
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is_safetensors = re.match("model.*\.safetensors", fname)
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is_pt = re.match(".*\.pt", fname)
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is_tokenizer = re.match("tokenizer.*\.model", fname)
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is_text = re.match(".*\.(txt|json|py)", fname) or is_tokenizer
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is_text = re.match(".*\.(txt|json|py|md)", fname) or is_tokenizer
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|
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if any((is_pytorch, is_safetensors, is_pt, is_tokenizer, is_text)):
|
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if is_text:
|
||||
|
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@ -26,6 +26,7 @@ current_params = params.copy()
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voices_by_gender = ['en_99', 'en_45', 'en_18', 'en_117', 'en_49', 'en_51', 'en_68', 'en_0', 'en_26', 'en_56', 'en_74', 'en_5', 'en_38', 'en_53', 'en_21', 'en_37', 'en_107', 'en_10', 'en_82', 'en_16', 'en_41', 'en_12', 'en_67', 'en_61', 'en_14', 'en_11', 'en_39', 'en_52', 'en_24', 'en_97', 'en_28', 'en_72', 'en_94', 'en_36', 'en_4', 'en_43', 'en_88', 'en_25', 'en_65', 'en_6', 'en_44', 'en_75', 'en_91', 'en_60', 'en_109', 'en_85', 'en_101', 'en_108', 'en_50', 'en_96', 'en_64', 'en_92', 'en_76', 'en_33', 'en_116', 'en_48', 'en_98', 'en_86', 'en_62', 'en_54', 'en_95', 'en_55', 'en_111', 'en_3', 'en_83', 'en_8', 'en_47', 'en_59', 'en_1', 'en_2', 'en_7', 'en_9', 'en_13', 'en_15', 'en_17', 'en_19', 'en_20', 'en_22', 'en_23', 'en_27', 'en_29', 'en_30', 'en_31', 'en_32', 'en_34', 'en_35', 'en_40', 'en_42', 'en_46', 'en_57', 'en_58', 'en_63', 'en_66', 'en_69', 'en_70', 'en_71', 'en_73', 'en_77', 'en_78', 'en_79', 'en_80', 'en_81', 'en_84', 'en_87', 'en_89', 'en_90', 'en_93', 'en_100', 'en_102', 'en_103', 'en_104', 'en_105', 'en_106', 'en_110', 'en_112', 'en_113', 'en_114', 'en_115']
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voice_pitches = ['x-low', 'low', 'medium', 'high', 'x-high']
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voice_speeds = ['x-slow', 'slow', 'medium', 'fast', 'x-fast']
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streaming_state = shared.args.no_stream # remember if chat streaming was enabled
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# Used for making text xml compatible, needed for voice pitch and speed control
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table = str.maketrans({
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@ -77,6 +78,7 @@ def input_modifier(string):
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shared.history['visible'][-1] = [shared.history['visible'][-1][0], shared.history['visible'][-1][1].replace('controls autoplay>','controls>')]
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shared.processing_message = "*Is recording a voice message...*"
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shared.args.no_stream = True # Disable streaming cause otherwise the audio output will stutter and begin anew every time the message is being updated
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return string
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|
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def output_modifier(string):
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@ -84,7 +86,7 @@ def output_modifier(string):
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This function is applied to the model outputs.
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"""
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global model, current_params
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global model, current_params, streaming_state
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|
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for i in params:
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if params[i] != current_params[i]:
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@ -116,6 +118,7 @@ def output_modifier(string):
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string += f'\n\n{original_string}'
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shared.processing_message = "*Is typing...*"
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shared.args.no_stream = streaming_state # restore the streaming option to the previous value
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return string
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|
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def bot_prefix_modifier(string):
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@ -14,18 +14,21 @@ import opt
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||||
|
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def load_quantized(model_name):
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||||
if not shared.args.gptq_model_type:
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if not shared.args.model_type:
|
||||
# Try to determine model type from model name
|
||||
model_type = model_name.split('-')[0].lower()
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if model_type not in ('llama', 'opt'):
|
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print("Can't determine model type from model name. Please specify it manually using --gptq-model-type "
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if model_name.lower().startswith(('llama', 'alpaca')):
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model_type = 'llama'
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elif model_name.lower().startswith(('opt', 'galactica')):
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model_type = 'opt'
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else:
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print("Can't determine model type from model name. Please specify it manually using --model_type "
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"argument")
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exit()
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else:
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model_type = shared.args.gptq_model_type.lower()
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model_type = shared.args.model_type.lower()
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if model_type == 'llama':
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if not shared.args.gptq_pre_layer:
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if not shared.args.pre_layer:
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load_quant = llama.load_quant
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else:
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load_quant = llama_inference_offload.load_quant
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@ -35,20 +38,29 @@ def load_quantized(model_name):
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print("Unknown pre-quantized model type specified. Only 'llama' and 'opt' are supported")
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exit()
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# Now we are going to try to locate the quantized model file.
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path_to_model = Path(f'models/{model_name}')
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if path_to_model.name.lower().startswith('llama-7b'):
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pt_model = f'llama-7b-{shared.args.gptq_bits}bit'
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elif path_to_model.name.lower().startswith('llama-13b'):
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pt_model = f'llama-13b-{shared.args.gptq_bits}bit'
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elif path_to_model.name.lower().startswith('llama-30b'):
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pt_model = f'llama-30b-{shared.args.gptq_bits}bit'
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elif path_to_model.name.lower().startswith('llama-65b'):
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pt_model = f'llama-65b-{shared.args.gptq_bits}bit'
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found_pts = list(path_to_model.glob("*.pt"))
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found_safetensors = list(path_to_model.glob("*.safetensors"))
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pt_path = None
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if len(found_pts) == 1:
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pt_path = found_pts[0]
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elif len(found_safetensors) == 1:
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pt_path = found_safetensors[0]
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else:
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pt_model = f'{model_name}-{shared.args.gptq_bits}bit'
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if path_to_model.name.lower().startswith('llama-7b'):
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pt_model = f'llama-7b-{shared.args.wbits}bit'
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elif path_to_model.name.lower().startswith('llama-13b'):
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pt_model = f'llama-13b-{shared.args.wbits}bit'
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elif path_to_model.name.lower().startswith('llama-30b'):
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pt_model = f'llama-30b-{shared.args.wbits}bit'
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elif path_to_model.name.lower().startswith('llama-65b'):
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pt_model = f'llama-65b-{shared.args.wbits}bit'
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else:
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pt_model = f'{model_name}-{shared.args.wbits}bit'
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# Try to find the .safetensors or .pt both in models/ and in the subfolder
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pt_path = None
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for path in [Path(p+ext) for ext in ['.safetensors', '.pt'] for p in [f"models/{pt_model}", f"{path_to_model}/{pt_model}"]]:
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if path.exists():
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print(f"Found {path}")
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@ -56,14 +68,14 @@ def load_quantized(model_name):
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break
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|
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if not pt_path:
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||||
print(f"Could not find {pt_model}, exiting...")
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print("Could not find the quantized model in .pt or .safetensors format, exiting...")
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exit()
|
||||
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# qwopqwop200's offload
|
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if shared.args.gptq_pre_layer:
|
||||
model = load_quant(str(path_to_model), str(pt_path), shared.args.gptq_bits, shared.args.gptq_pre_layer)
|
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if shared.args.pre_layer:
|
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model = load_quant(str(path_to_model), str(pt_path), shared.args.wbits, shared.args.groupsize, shared.args.pre_layer)
|
||||
else:
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model = load_quant(str(path_to_model), str(pt_path), shared.args.gptq_bits)
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model = load_quant(str(path_to_model), str(pt_path), shared.args.wbits, shared.args.groupsize)
|
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|
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# accelerate offload (doesn't work properly)
|
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if shared.args.gpu_memory:
|
||||
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@ -18,11 +18,11 @@ def add_lora_to_model(lora_name):
|
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|
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# If a LoRA had been previously loaded, or if we want
|
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# to unload a LoRA, reload the model
|
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if shared.lora_name != "None" or lora_name == "None":
|
||||
if shared.lora_name not in ['None', ''] or lora_name in ['None', '']:
|
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reload_model()
|
||||
shared.lora_name = lora_name
|
||||
|
||||
if lora_name != "None":
|
||||
if lora_name not in ['None', '']:
|
||||
print(f"Adding the LoRA {lora_name} to the model...")
|
||||
params = {}
|
||||
if not shared.args.cpu:
|
||||
|
||||
@ -63,8 +63,8 @@ def create_extensions_block():
|
||||
|
||||
# Creating the extension ui elements
|
||||
if should_display_ui:
|
||||
with gr.Box(elem_id="extensions"):
|
||||
gr.Markdown("Extensions")
|
||||
with gr.Column(elem_id="extensions"):
|
||||
for extension, name in iterator():
|
||||
gr.Markdown(f"\n### {name}")
|
||||
if hasattr(extension, "ui"):
|
||||
extension.ui()
|
||||
|
||||
@ -44,7 +44,7 @@ def load_model(model_name):
|
||||
shared.is_RWKV = model_name.lower().startswith('rwkv-')
|
||||
|
||||
# Default settings
|
||||
if not any([shared.args.cpu, shared.args.load_in_8bit, shared.args.gptq_bits, shared.args.auto_devices, shared.args.disk, shared.args.gpu_memory is not None, shared.args.cpu_memory is not None, shared.args.deepspeed, shared.args.flexgen, shared.is_RWKV]):
|
||||
if not any([shared.args.cpu, shared.args.load_in_8bit, shared.args.wbits, shared.args.auto_devices, shared.args.disk, shared.args.gpu_memory is not None, shared.args.cpu_memory is not None, shared.args.deepspeed, shared.args.flexgen, shared.is_RWKV]):
|
||||
if any(size in shared.model_name.lower() for size in ('13b', '20b', '30b')):
|
||||
model = AutoModelForCausalLM.from_pretrained(Path(f"models/{shared.model_name}"), device_map='auto', load_in_8bit=True)
|
||||
else:
|
||||
@ -95,7 +95,7 @@ def load_model(model_name):
|
||||
return model, tokenizer
|
||||
|
||||
# Quantized model
|
||||
elif shared.args.gptq_bits > 0:
|
||||
elif shared.args.wbits > 0:
|
||||
from modules.GPTQ_loader import load_quantized
|
||||
|
||||
model = load_quantized(model_name)
|
||||
|
||||
@ -52,7 +52,8 @@ settings = {
|
||||
'default': 'Common sense questions and answers\n\nQuestion: \nFactual answer:',
|
||||
'^(gpt4chan|gpt-4chan|4chan)': '-----\n--- 865467536\nInput text\n--- 865467537\n',
|
||||
'(rosey|chip|joi)_.*_instruct.*': 'User: \n',
|
||||
'oasst-*': '<|prompter|>Write a story about future of AI development<|endoftext|><|assistant|>'
|
||||
'oasst-*': '<|prompter|>Write a story about future of AI development<|endoftext|><|assistant|>',
|
||||
'alpaca-*': "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n### Instruction:\nWrite a poem about the transformers Python library. \nMention the word \"large language models\" in that poem.\n### Response:\n",
|
||||
},
|
||||
'lora_prompts': {
|
||||
'default': 'Common sense questions and answers\n\nQuestion: \nFactual answer:',
|
||||
@ -78,10 +79,15 @@ parser.add_argument('--chat', action='store_true', help='Launch the web UI in ch
|
||||
parser.add_argument('--cai-chat', action='store_true', help='Launch the web UI in chat mode with a style similar to Character.AI\'s. If the file img_bot.png or img_bot.jpg exists in the same folder as server.py, this image will be used as the bot\'s profile picture. Similarly, img_me.png or img_me.jpg will be used as your profile picture.')
|
||||
parser.add_argument('--cpu', action='store_true', help='Use the CPU to generate text.')
|
||||
parser.add_argument('--load-in-8bit', action='store_true', help='Load the model with 8-bit precision.')
|
||||
parser.add_argument('--load-in-4bit', action='store_true', help='DEPRECATED: use --gptq-bits 4 instead.')
|
||||
parser.add_argument('--gptq-bits', type=int, default=0, help='GPTQ: Load a pre-quantized model with specified precision. 2, 3, 4 and 8bit are supported. Currently only works with LLaMA and OPT.')
|
||||
parser.add_argument('--gptq-model-type', type=str, help='GPTQ: Model type of pre-quantized model. Currently only LLaMa and OPT are supported.')
|
||||
parser.add_argument('--gptq-pre-layer', type=int, default=0, help='GPTQ: The number of layers to preload.')
|
||||
|
||||
parser.add_argument('--gptq-bits', type=int, default=0, help='DEPRECATED: use --wbits instead.')
|
||||
parser.add_argument('--gptq-model-type', type=str, help='DEPRECATED: use --model_type instead.')
|
||||
parser.add_argument('--gptq-pre-layer', type=int, default=0, help='DEPRECATED: use --pre_layer instead.')
|
||||
parser.add_argument('--wbits', type=int, default=0, help='GPTQ: Load a pre-quantized model with specified precision in bits. 2, 3, 4 and 8 are supported.')
|
||||
parser.add_argument('--model_type', type=str, help='GPTQ: Model type of pre-quantized model. Currently only LLaMA and OPT are supported.')
|
||||
parser.add_argument('--groupsize', type=int, default=-1, help='GPTQ: Group size.')
|
||||
parser.add_argument('--pre_layer', type=int, default=0, help='GPTQ: The number of layers to preload.')
|
||||
|
||||
parser.add_argument('--bf16', action='store_true', help='Load the model with bfloat16 precision. Requires NVIDIA Ampere GPU.')
|
||||
parser.add_argument('--auto-devices', action='store_true', help='Automatically split the model across the available GPU(s) and CPU.')
|
||||
parser.add_argument('--disk', action='store_true', help='If the model is too large for your GPU(s) and CPU combined, send the remaining layers to the disk.')
|
||||
@ -109,6 +115,8 @@ parser.add_argument('--verbose', action='store_true', help='Print the prompts to
|
||||
args = parser.parse_args()
|
||||
|
||||
# Provisional, this will be deleted later
|
||||
if args.load_in_4bit:
|
||||
print("Warning: --load-in-4bit is deprecated and will be removed. Use --gptq-bits 4 instead.\n")
|
||||
args.gptq_bits = 4
|
||||
deprecated_dict = {'gptq_bits': ['wbits', 0], 'gptq_model_type': ['model_type', None], 'gptq_pre_layer': ['prelayer', 0]}
|
||||
for k in deprecated_dict:
|
||||
if eval(f"args.{k}") != deprecated_dict[k][1]:
|
||||
print(f"Warning: --{k} is deprecated and will be removed. Use --{deprecated_dict[k][0]} instead.")
|
||||
exec(f"args.{deprecated_dict[k][0]} = args.{k}")
|
||||
|
||||
6
prompts/Alpaca.txt
Normal file
6
prompts/Alpaca.txt
Normal file
@ -0,0 +1,6 @@
|
||||
Below is an instruction that describes a task. Write a response that appropriately completes the request.
|
||||
### Instruction:
|
||||
Write a poem about the transformers Python library.
|
||||
Mention the word "large language models" in that poem.
|
||||
### Response:
|
||||
|
||||
1
prompts/Open Assistant.txt
Normal file
1
prompts/Open Assistant.txt
Normal file
@ -0,0 +1 @@
|
||||
<|prompter|>Write a story about future of AI development<|endoftext|><|assistant|>
|
||||
4
prompts/QA.txt
Normal file
4
prompts/QA.txt
Normal file
@ -0,0 +1,4 @@
|
||||
Common sense questions and answers
|
||||
|
||||
Question:
|
||||
Factual answer:
|
||||
@ -1,7 +1,7 @@
|
||||
accelerate==0.17.1
|
||||
bitsandbytes==0.37.1
|
||||
flexgen==0.1.7
|
||||
gradio==3.18.0
|
||||
gradio==3.23.0
|
||||
markdown
|
||||
numpy
|
||||
peft==0.2.0
|
||||
|
||||
84
server.py
84
server.py
@ -4,6 +4,7 @@ import re
|
||||
import sys
|
||||
import time
|
||||
import zipfile
|
||||
from datetime import datetime
|
||||
from pathlib import Path
|
||||
|
||||
import gradio as gr
|
||||
@ -36,6 +37,13 @@ def get_available_models():
|
||||
def get_available_presets():
|
||||
return sorted(set(map(lambda x : '.'.join(str(x.name).split('.')[:-1]), Path('presets').glob('*.txt'))), key=str.lower)
|
||||
|
||||
def get_available_prompts():
|
||||
prompts = []
|
||||
prompts += sorted(set(map(lambda x : '.'.join(str(x.name).split('.')[:-1]), Path('prompts').glob('[0-9]*.txt'))), key=str.lower, reverse=True)
|
||||
prompts += sorted(set(map(lambda x : '.'.join(str(x.name).split('.')[:-1]), Path('prompts').glob('*.txt'))), key=str.lower)
|
||||
prompts += ['None']
|
||||
return prompts
|
||||
|
||||
def get_available_characters():
|
||||
return ['None'] + sorted(set(map(lambda x : '.'.join(str(x.name).split('.')[:-1]), Path('characters').glob('*.json'))), key=str.lower)
|
||||
|
||||
@ -48,11 +56,16 @@ def get_available_softprompts():
|
||||
def get_available_loras():
|
||||
return ['None'] + sorted([item.name for item in list(Path('loras/').glob('*')) if not item.name.endswith(('.txt', '-np', '.pt', '.json'))], key=str.lower)
|
||||
|
||||
def unload_model():
|
||||
shared.model = shared.tokenizer = None
|
||||
clear_torch_cache()
|
||||
|
||||
def load_model_wrapper(selected_model):
|
||||
if selected_model != shared.model_name:
|
||||
shared.model_name = selected_model
|
||||
shared.model = shared.tokenizer = None
|
||||
clear_torch_cache()
|
||||
|
||||
unload_model()
|
||||
if selected_model != '':
|
||||
shared.model, shared.tokenizer = load_model(shared.model_name)
|
||||
|
||||
return selected_model
|
||||
@ -91,7 +104,7 @@ def load_preset_values(preset_menu, return_dict=False):
|
||||
if return_dict:
|
||||
return generate_params
|
||||
else:
|
||||
return preset_menu, generate_params['do_sample'], generate_params['temperature'], generate_params['top_p'], generate_params['typical_p'], generate_params['repetition_penalty'], generate_params['encoder_repetition_penalty'], generate_params['top_k'], generate_params['min_length'], generate_params['no_repeat_ngram_size'], generate_params['num_beams'], generate_params['penalty_alpha'], generate_params['length_penalty'], generate_params['early_stopping']
|
||||
return generate_params['do_sample'], generate_params['temperature'], generate_params['top_p'], generate_params['typical_p'], generate_params['repetition_penalty'], generate_params['encoder_repetition_penalty'], generate_params['top_k'], generate_params['min_length'], generate_params['no_repeat_ngram_size'], generate_params['num_beams'], generate_params['penalty_alpha'], generate_params['length_penalty'], generate_params['early_stopping']
|
||||
|
||||
def upload_soft_prompt(file):
|
||||
with zipfile.ZipFile(io.BytesIO(file)) as zf:
|
||||
@ -116,9 +129,43 @@ def create_model_and_preset_menus():
|
||||
shared.gradio['preset_menu'] = gr.Dropdown(choices=available_presets, value=default_preset if not shared.args.flexgen else 'Naive', label='Generation parameters preset')
|
||||
ui.create_refresh_button(shared.gradio['preset_menu'], lambda : None, lambda : {'choices': get_available_presets()}, 'refresh-button')
|
||||
|
||||
def save_prompt(text):
|
||||
fname = f"{datetime.now().strftime('%Y-%m-%d-%H:%M:%S')}.txt"
|
||||
with open(Path(f'prompts/{fname}'), 'w', encoding='utf-8') as f:
|
||||
f.write(text)
|
||||
return f"Saved prompt to prompts/{fname}"
|
||||
|
||||
def load_prompt(fname):
|
||||
if fname in ['None', '']:
|
||||
return ''
|
||||
else:
|
||||
with open(Path(f'prompts/{fname}.txt'), 'r', encoding='utf-8') as f:
|
||||
return f.read()
|
||||
|
||||
def create_prompt_menus():
|
||||
with gr.Row():
|
||||
with gr.Column():
|
||||
with gr.Row():
|
||||
shared.gradio['prompt_menu'] = gr.Dropdown(choices=get_available_prompts(), value='None', label='Prompt')
|
||||
ui.create_refresh_button(shared.gradio['prompt_menu'], lambda : None, lambda : {'choices': get_available_prompts()}, 'refresh-button')
|
||||
|
||||
with gr.Column():
|
||||
with gr.Column():
|
||||
shared.gradio['save_prompt'] = gr.Button('Save prompt')
|
||||
shared.gradio['status'] = gr.Markdown('Ready')
|
||||
|
||||
shared.gradio['prompt_menu'].change(load_prompt, [shared.gradio['prompt_menu']], [shared.gradio['textbox']], show_progress=True)
|
||||
shared.gradio['save_prompt'].click(save_prompt, [shared.gradio['textbox']], [shared.gradio['status']], show_progress=False)
|
||||
|
||||
def create_settings_menus(default_preset):
|
||||
generate_params = load_preset_values(default_preset if not shared.args.flexgen else 'Naive', return_dict=True)
|
||||
|
||||
with gr.Row():
|
||||
with gr.Column():
|
||||
create_model_and_preset_menus()
|
||||
with gr.Column():
|
||||
shared.gradio['seed'] = gr.Number(value=-1, label='Seed (-1 for random)')
|
||||
|
||||
with gr.Row():
|
||||
with gr.Column():
|
||||
with gr.Box():
|
||||
@ -149,12 +196,6 @@ def create_settings_menus(default_preset):
|
||||
shared.gradio['length_penalty'] = gr.Slider(-5, 5, value=generate_params['length_penalty'], label='length_penalty')
|
||||
shared.gradio['early_stopping'] = gr.Checkbox(value=generate_params['early_stopping'], label='early_stopping')
|
||||
|
||||
shared.gradio['seed'] = gr.Number(value=-1, label='Seed (-1 for random)')
|
||||
|
||||
with gr.Row():
|
||||
shared.gradio['preset_menu_mirror'] = gr.Dropdown(choices=available_presets, value=default_preset if not shared.args.flexgen else 'Naive', label='Generation parameters preset')
|
||||
ui.create_refresh_button(shared.gradio['preset_menu_mirror'], lambda : None, lambda : {'choices': get_available_presets()}, 'refresh-button')
|
||||
|
||||
with gr.Row():
|
||||
shared.gradio['lora_menu'] = gr.Dropdown(choices=available_loras, value=shared.lora_name, label='LoRA')
|
||||
ui.create_refresh_button(shared.gradio['lora_menu'], lambda : None, lambda : {'choices': get_available_loras()}, 'refresh-button')
|
||||
@ -169,8 +210,7 @@ def create_settings_menus(default_preset):
|
||||
shared.gradio['upload_softprompt'] = gr.File(type='binary', file_types=['.zip'])
|
||||
|
||||
shared.gradio['model_menu'].change(load_model_wrapper, [shared.gradio['model_menu']], [shared.gradio['model_menu']], show_progress=True)
|
||||
shared.gradio['preset_menu'].change(load_preset_values, [shared.gradio['preset_menu']], [shared.gradio[k] for k in ['preset_menu_mirror', 'do_sample', 'temperature', 'top_p', 'typical_p', 'repetition_penalty', 'encoder_repetition_penalty', 'top_k', 'min_length', 'no_repeat_ngram_size', 'num_beams', 'penalty_alpha', 'length_penalty', 'early_stopping']])
|
||||
shared.gradio['preset_menu_mirror'].change(load_preset_values, [shared.gradio['preset_menu_mirror']], [shared.gradio[k] for k in ['preset_menu', 'do_sample', 'temperature', 'top_p', 'typical_p', 'repetition_penalty', 'encoder_repetition_penalty', 'top_k', 'min_length', 'no_repeat_ngram_size', 'num_beams', 'penalty_alpha', 'length_penalty', 'early_stopping']])
|
||||
shared.gradio['preset_menu'].change(load_preset_values, [shared.gradio['preset_menu']], [shared.gradio[k] for k in ['do_sample', 'temperature', 'top_p', 'typical_p', 'repetition_penalty', 'encoder_repetition_penalty', 'top_k', 'min_length', 'no_repeat_ngram_size', 'num_beams', 'penalty_alpha', 'length_penalty', 'early_stopping']])
|
||||
shared.gradio['lora_menu'].change(load_lora_wrapper, [shared.gradio['lora_menu']], [shared.gradio['lora_menu'], shared.gradio['textbox']], show_progress=True)
|
||||
shared.gradio['softprompts_menu'].change(load_soft_prompt, [shared.gradio['softprompts_menu']], [shared.gradio['softprompts_menu']], show_progress=True)
|
||||
shared.gradio['upload_softprompt'].upload(upload_soft_prompt, [shared.gradio['upload_softprompt']], [shared.gradio['softprompts_menu']])
|
||||
@ -235,8 +275,9 @@ if shared.args.lora:
|
||||
|
||||
# Default UI settings
|
||||
default_preset = shared.settings['presets'][next((k for k in shared.settings['presets'] if re.match(k.lower(), shared.model_name.lower())), 'default')]
|
||||
if shared.lora_name != "None":
|
||||
default_text = shared.settings['lora_prompts'][next((k for k in shared.settings['lora_prompts'] if re.match(k.lower(), shared.lora_name.lower())), 'default')]
|
||||
if default_text == '':
|
||||
else:
|
||||
default_text = shared.settings['prompts'][next((k for k in shared.settings['prompts'] if re.match(k.lower(), shared.model_name.lower())), 'default')]
|
||||
title ='Text generation web UI'
|
||||
description = '\n\n# Text generation lab\nGenerate text using Large Language Models.\n'
|
||||
@ -257,8 +298,8 @@ def create_interface():
|
||||
shared.gradio['display'] = gr.Chatbot(value=shared.history['visible']).style(color_map=("#326efd", "#212528"))
|
||||
shared.gradio['textbox'] = gr.Textbox(label='Input')
|
||||
with gr.Row():
|
||||
shared.gradio['Stop'] = gr.Button('Stop', elem_id="stop")
|
||||
shared.gradio['Generate'] = gr.Button('Generate')
|
||||
shared.gradio['Stop'] = gr.Button('Stop', elem_id="stop")
|
||||
with gr.Row():
|
||||
shared.gradio['Impersonate'] = gr.Button('Impersonate')
|
||||
shared.gradio['Regenerate'] = gr.Button('Regenerate')
|
||||
@ -271,8 +312,6 @@ def create_interface():
|
||||
shared.gradio['Clear history-confirm'] = gr.Button('Confirm', variant="stop", visible=False)
|
||||
shared.gradio['Clear history-cancel'] = gr.Button('Cancel', visible=False)
|
||||
|
||||
create_model_and_preset_menus()
|
||||
|
||||
with gr.Tab("Character", elem_id="chat-settings"):
|
||||
shared.gradio['name1'] = gr.Textbox(value=shared.settings[f'name1{suffix}'], lines=1, label='Your name')
|
||||
shared.gradio['name2'] = gr.Textbox(value=shared.settings[f'name2{suffix}'], lines=1, label='Bot\'s name')
|
||||
@ -366,19 +405,25 @@ def create_interface():
|
||||
|
||||
elif shared.args.notebook:
|
||||
with gr.Tab("Text generation", elem_id="main"):
|
||||
with gr.Row():
|
||||
with gr.Column(scale=4):
|
||||
with gr.Tab('Raw'):
|
||||
shared.gradio['textbox'] = gr.Textbox(value=default_text, lines=25)
|
||||
shared.gradio['textbox'] = gr.Textbox(value=default_text, elem_id="textbox", lines=25)
|
||||
with gr.Tab('Markdown'):
|
||||
shared.gradio['markdown'] = gr.Markdown()
|
||||
with gr.Tab('HTML'):
|
||||
shared.gradio['html'] = gr.HTML()
|
||||
|
||||
with gr.Row():
|
||||
shared.gradio['Stop'] = gr.Button('Stop')
|
||||
shared.gradio['Generate'] = gr.Button('Generate')
|
||||
shared.gradio['Stop'] = gr.Button('Stop')
|
||||
|
||||
with gr.Column(scale=1):
|
||||
gr.Markdown("\n")
|
||||
shared.gradio['max_new_tokens'] = gr.Slider(minimum=shared.settings['max_new_tokens_min'], maximum=shared.settings['max_new_tokens_max'], step=1, label='max_new_tokens', value=shared.settings['max_new_tokens'])
|
||||
|
||||
create_model_and_preset_menus()
|
||||
create_prompt_menus()
|
||||
|
||||
with gr.Tab("Parameters", elem_id="parameters"):
|
||||
create_settings_menus(default_preset)
|
||||
|
||||
@ -402,7 +447,7 @@ def create_interface():
|
||||
with gr.Column():
|
||||
shared.gradio['Stop'] = gr.Button('Stop')
|
||||
|
||||
create_model_and_preset_menus()
|
||||
create_prompt_menus()
|
||||
|
||||
with gr.Column():
|
||||
with gr.Tab('Raw'):
|
||||
@ -411,6 +456,7 @@ def create_interface():
|
||||
shared.gradio['markdown'] = gr.Markdown()
|
||||
with gr.Tab('HTML'):
|
||||
shared.gradio['html'] = gr.HTML()
|
||||
|
||||
with gr.Tab("Parameters", elem_id="parameters"):
|
||||
create_settings_menus(default_preset)
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user