{name2}
'
+ output += f''
@@ -208,135 +94,39 @@ def get_image_cache(path):
return image_cache[path][1]
-def generate_chat_html(history, name1, name2, character):
- css = """
- .chat {
- margin-left: auto;
- margin-right: auto;
- max-width: 800px;
- height: 66.67vh;
- overflow-y: auto;
- padding-right: 20px;
- display: flex;
- flex-direction: column-reverse;
- }
-
- .message {
- display: grid;
- grid-template-columns: 60px 1fr;
- padding-bottom: 25px;
- font-size: 15px;
- font-family: Helvetica, Arial, sans-serif;
- line-height: 1.428571429;
- }
-
- .circle-you {
- width: 50px;
- height: 50px;
- background-color: rgb(238, 78, 59);
- border-radius: 50%;
- }
-
- .circle-bot {
- width: 50px;
- height: 50px;
- background-color: rgb(59, 78, 244);
- border-radius: 50%;
- }
-
- .circle-bot img, .circle-you img {
- border-radius: 50%;
- width: 100%;
- height: 100%;
- object-fit: cover;
- }
-
- .text {
- }
+def load_html_image(paths):
+ for str_path in paths:
+ path = Path(str_path)
+ if path.exists():
+ return f'})
'
+ return ''
- .text p {
- margin-top: 5px;
- }
-
- .username {
- font-weight: bold;
- }
-
- .message-body {
- }
-
- .message-body img {
- max-width: 300px;
- max-height: 300px;
- border-radius: 20px;
- }
-
- .message-body p {
- margin-bottom: 0 !important;
- font-size: 15px !important;
- line-height: 1.428571429 !important;
- }
-
- .dark .message-body p em {
- color: rgb(138, 138, 138) !important;
- }
-
- .message-body p em {
- color: rgb(110, 110, 110) !important;
- }
-
- """
-
- output = ''
- output += f'
'
for post in posts:
output += post
output += '
'
- img = ''
-
- for i in [
- f"characters/{character}.png",
- f"characters/{character}.jpg",
- f"characters/{character}.jpeg",
- "img_bot.png",
- "img_bot.jpg",
- "img_bot.jpeg"
- ]:
-
- path = Path(i)
- if path.exists():
- img = f''
- break
-
- img_me = ''
- for i in ["img_me.png", "img_me.jpg", "img_me.jpeg"]:
- path = Path(i)
- if path.exists():
- img_me = f''
- break
+def generate_chat_html(history, name1, name2, character):
+ output = f'
'
+
+ img_bot = load_html_image([f"characters/{character}.{ext}" for ext in ['png', 'jpg', 'jpeg']] + ["img_bot.png","img_bot.jpg","img_bot.jpeg"])
+ img_me = load_html_image(["img_me.png", "img_me.jpg", "img_me.jpeg"])
for i,_row in enumerate(history[::-1]):
- row = _row.copy()
- row[0] = re.sub(r"(\*\*)([^\*\n]*)(\*\*)", r"\2", row[0])
- row[1] = re.sub(r"(\*\*)([^\*\n]*)(\*\*)", r"\2", row[1])
- row[0] = re.sub(r"(\*)([^\*\n]*)(\*)", r"\2", row[0])
- row[1] = re.sub(r"(\*)([^\*\n]*)(\*)", r"\2", row[1])
- p = '\n'.join([f"
diff --git a/modules/models.py b/modules/models.py
index 7d094ed566..f4bb11fd3f 100644
--- a/modules/models.py
+++ b/modules/models.py
@@ -1,6 +1,5 @@
import json
import os
-import sys
import time
import zipfile
from pathlib import Path
@@ -35,6 +34,7 @@
ds_config = generate_ds_config(shared.args.bf16, 1 * world_size, shared.args.nvme_offload_dir)
dschf = HfDeepSpeedConfig(ds_config) # Keep this object alive for the Transformers integration
+
def load_model(model_name):
print(f"Loading {model_name}...")
t0 = time.time()
@@ -42,7 +42,7 @@ 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.load_in_4bit, shared.args.gptq_bits > 0, 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.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 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:
@@ -87,11 +87,11 @@ def load_model(model_name):
return model, tokenizer
- # 4-bit LLaMA
- elif shared.args.gptq_bits > 0 or shared.args.load_in_4bit:
- from modules.quantized_LLaMA import load_quantized_LLaMA
+ # Quantized model
+ elif shared.args.gptq_bits > 0:
+ from modules.GPTQ_loader import load_quantized
- model = load_quantized_LLaMA(model_name)
+ model = load_quantized(model_name)
# Custom
else:
diff --git a/modules/shared.py b/modules/shared.py
index 8fcd4745c8..ea2eb50b7f 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -11,6 +11,7 @@
history = {'internal': [], 'visible': []}
character = 'None'
stop_everything = False
+processing_message = '*Is typing...*'
# UI elements (buttons, sliders, HTML, etc)
gradio = {}
@@ -68,8 +69,9 @@ def str2bool(v):
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='Load the model with 4-bit precision. Currently only works with LLaMA.')
-parser.add_argument('--gptq-bits', type=int, default=0, help='Load a pre-quantized model with specified precision. 2, 3, 4 and 8bit are supported. Currently only works with LLaMA.')
+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='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='Model type of pre-quantized model. Currently only LLaMa and OPT are supported.')
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.')
@@ -94,3 +96,8 @@ def str2bool(v):
parser.add_argument('--auto-launch', action='store_true', default=False, help='Open the web UI in the default browser upon launch.')
parser.add_argument('--verbose', action='store_true', help='Print the prompts to the terminal.')
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
diff --git a/modules/text_generation.py b/modules/text_generation.py
index d64481b24e..a29b987f4e 100644
--- a/modules/text_generation.py
+++ b/modules/text_generation.py
@@ -89,7 +89,7 @@ def clear_torch_cache():
if not shared.args.cpu:
torch.cuda.empty_cache()
-def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, eos_token=None, stopping_string=None):
+def generate_reply(question, max_new_tokens, 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, eos_token=None, stopping_string=None):
clear_torch_cache()
t0 = time.time()
@@ -101,7 +101,8 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
reply = shared.model.generate(context=question, token_count=max_new_tokens, temperature=temperature, top_p=top_p, top_k=top_k)
yield formatted_outputs(reply, shared.model_name)
else:
- yield formatted_outputs(question, shared.model_name)
+ if not (shared.args.chat or shared.args.cai_chat):
+ yield formatted_outputs(question, shared.model_name)
# RWKV has proper streaming, which is very nice.
# No need to generate 8 tokens at a time.
for reply in shared.model.generate_with_streaming(context=question, token_count=max_new_tokens, temperature=temperature, top_p=top_p, top_k=top_k):
@@ -122,7 +123,7 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
input_ids = encode(question, max_new_tokens)
original_input_ids = input_ids
output = input_ids[0]
- cuda = "" if (shared.args.cpu or shared.args.deepspeed or shared.args.flexgen) else ".cuda()"
+ cuda = not any((shared.args.cpu, shared.args.deepspeed, shared.args.flexgen))
eos_token_ids = [shared.tokenizer.eos_token_id] if shared.tokenizer.eos_token_id is not None else []
if eos_token is not None:
eos_token_ids.append(int(encode(eos_token)[0][-1]))
@@ -132,45 +133,49 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
t = encode(stopping_string, 0, add_special_tokens=False)
stopping_criteria_list.append(_SentinelTokenStoppingCriteria(sentinel_token_ids=t, starting_idx=len(input_ids[0])))
+ generate_params = {}
if not shared.args.flexgen:
- generate_params = [
- f"max_new_tokens=max_new_tokens",
- f"eos_token_id={eos_token_ids}",
- f"stopping_criteria=stopping_criteria_list",
- f"do_sample={do_sample}",
- f"temperature={temperature}",
- f"top_p={top_p}",
- f"typical_p={typical_p}",
- f"repetition_penalty={repetition_penalty}",
- f"top_k={top_k}",
- f"min_length={min_length if shared.args.no_stream else 0}",
- f"no_repeat_ngram_size={no_repeat_ngram_size}",
- f"num_beams={num_beams}",
- f"penalty_alpha={penalty_alpha}",
- f"length_penalty={length_penalty}",
- f"early_stopping={early_stopping}",
- ]
+ generate_params.update({
+ "max_new_tokens": max_new_tokens,
+ "eos_token_id": eos_token_ids,
+ "stopping_criteria": stopping_criteria_list,
+ "do_sample": do_sample,
+ "temperature": temperature,
+ "top_p": top_p,
+ "typical_p": typical_p,
+ "repetition_penalty": repetition_penalty,
+ "encoder_repetition_penalty": encoder_repetition_penalty,
+ "top_k": top_k,
+ "min_length": min_length if shared.args.no_stream else 0,
+ "no_repeat_ngram_size": no_repeat_ngram_size,
+ "num_beams": num_beams,
+ "penalty_alpha": penalty_alpha,
+ "length_penalty": length_penalty,
+ "early_stopping": early_stopping,
+ })
else:
- generate_params = [
- f"max_new_tokens={max_new_tokens if shared.args.no_stream else 8}",
- f"do_sample={do_sample}",
- f"temperature={temperature}",
- f"stop={eos_token_ids[-1]}",
- ]
+ generate_params.update({
+ "max_new_tokens": max_new_tokens if shared.args.no_stream else 8,
+ "do_sample": do_sample,
+ "temperature": temperature,
+ "stop": eos_token_ids[-1],
+ })
if shared.args.deepspeed:
- generate_params.append("synced_gpus=True")
+ generate_params.update({"synced_gpus": True})
if shared.soft_prompt:
inputs_embeds, filler_input_ids = generate_softprompt_input_tensors(input_ids)
- generate_params.insert(0, "inputs_embeds=inputs_embeds")
- generate_params.insert(0, "inputs=filler_input_ids")
+ generate_params.update({"inputs_embeds": inputs_embeds})
+ generate_params.update({"inputs": filler_input_ids})
else:
- generate_params.insert(0, "inputs=input_ids")
+ generate_params.update({"inputs": input_ids})
try:
# Generate the entire reply at once.
if shared.args.no_stream:
with torch.no_grad():
- output = eval(f"shared.model.generate({', '.join(generate_params)}){cuda}")[0]
+ output = shared.model.generate(**generate_params)[0]
+ if cuda:
+ output = output.cuda()
if shared.soft_prompt:
output = torch.cat((input_ids[0], output[filler_input_ids.shape[1]:]))
@@ -193,8 +198,9 @@ def generate_with_callback(callback=None, **kwargs):
def generate_with_streaming(**kwargs):
return Iteratorize(generate_with_callback, kwargs, callback=None)
- yield formatted_outputs(original_question, shared.model_name)
- with eval(f"generate_with_streaming({', '.join(generate_params)})") as generator:
+ if not (shared.args.chat or shared.args.cai_chat):
+ yield formatted_outputs(original_question, shared.model_name)
+ with generate_with_streaming(**generate_params) as generator:
for output in generator:
if shared.soft_prompt:
output = torch.cat((input_ids[0], output[filler_input_ids.shape[1]:]))
@@ -214,7 +220,7 @@ def generate_with_streaming(**kwargs):
for i in range(max_new_tokens//8+1):
clear_torch_cache()
with torch.no_grad():
- output = eval(f"shared.model.generate({', '.join(generate_params)})")[0]
+ output = shared.model.generate(**generate_params)[0]
if shared.soft_prompt:
output = torch.cat((input_ids[0], output[filler_input_ids.shape[1]:]))
reply = decode(output)
diff --git a/modules/ui.py b/modules/ui.py
index bb193e35c1..80bd7c1cde 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1,68 +1,17 @@
+from pathlib import Path
+
import gradio as gr
refresh_symbol = '\U0001f504' # 🔄
-css = """
-.tabs.svelte-710i53 {
- margin-top: 0
-}
-.py-6 {
- padding-top: 2.5rem
-}
-.dark #refresh-button {
- background-color: #ffffff1f;
-}
-#refresh-button {
- flex: none;
- margin: 0;
- padding: 0;
- min-width: 50px;
- border: none;
- box-shadow: none;
- border-radius: 10px;
- background-color: #0000000d;
-}
-#download-label, #upload-label {
- min-height: 0
-}
-#accordion {
-}
-.dark svg {
- fill: white;
-}
-svg {
- display: unset !important;
- vertical-align: middle !important;
- margin: 5px;
-}
-ol li p, ul li p {
- display: inline-block;
-}
-"""
-
-chat_css = """
-.h-\[40vh\], .wrap.svelte-byatnx.svelte-byatnx.svelte-byatnx {
- height: 66.67vh
-}
-.gradio-container {
- max-width: 800px !important;
- margin-left: auto !important;
- margin-right: auto !important;
-}
-.w-screen {
- width: unset
-}
-div.svelte-362y77>*, div.svelte-362y77>.form>* {
- flex-wrap: nowrap
-}
-/* fixes the API documentation in chat mode */
-.api-docs.svelte-1iguv9h.svelte-1iguv9h.svelte-1iguv9h {
- display: grid;
-}
-.pending.svelte-1ed2p3z {
- opacity: 1;
-}
-"""
+with open(Path(__file__).resolve().parent / '../css/main.css', 'r') as f:
+ css = f.read()
+with open(Path(__file__).resolve().parent / '../css/chat.css', 'r') as f:
+ chat_css = f.read()
+with open(Path(__file__).resolve().parent / '../css/main.js', 'r') as f:
+ main_js = f.read()
+with open(Path(__file__).resolve().parent / '../css/chat.js', 'r') as f:
+ chat_js = f.read()
class ToolButton(gr.Button, gr.components.FormComponent):
"""Small button with single emoji as text, fits inside gradio forms"""
diff --git a/requirements.txt b/requirements.txt
index 8534be29a7..014d987ba7 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,10 +1,11 @@
-accelerate==0.17.0
-bitsandbytes==0.37.0
+accelerate==0.17.1
+bitsandbytes==0.37.1
flexgen==0.1.7
gradio==3.18.0
+markdown
numpy
requests
-rwkv==0.3.1
+rwkv==0.4.2
safetensors==0.3.0
sentencepiece
tqdm
diff --git a/server.py b/server.py
index 6ae6d9f373..f0187e88e0 100644
--- a/server.py
+++ b/server.py
@@ -34,7 +34,7 @@ def get_available_models():
if shared.args.flexgen:
return sorted([re.sub('-np$', '', item.name) for item in list(Path('models/').glob('*')) if item.name.endswith('-np')], key=str.lower)
else:
- return sorted([item.name for item in list(Path('models/').glob('*')) if not item.name.endswith(('.txt', '-np', '.pt'))], key=str.lower)
+ return sorted([re.sub('.pth$', '', item.name) for item in list(Path('models/').glob('*')) if not item.name.endswith(('.txt', '-np', '.pt', '.json'))], key=str.lower)
def get_available_presets():
return sorted(set(map(lambda x : '.'.join(str(x.name).split('.')[:-1]), Path('presets').glob('*.txt'))), key=str.lower)
@@ -67,6 +67,7 @@ def load_preset_values(preset_menu, return_dict=False):
'top_p': 1,
'typical_p': 1,
'repetition_penalty': 1,
+ 'encoder_repetition_penalty': 1,
'top_k': 50,
'num_beams': 1,
'penalty_alpha': 0,
@@ -87,7 +88,7 @@ def load_preset_values(preset_menu, return_dict=False):
if return_dict:
return generate_params
else:
- return generate_params['do_sample'], generate_params['temperature'], generate_params['top_p'], generate_params['typical_p'], generate_params['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:
@@ -101,9 +102,7 @@ def upload_soft_prompt(file):
return name
-def create_settings_menus(default_preset):
- generate_params = load_preset_values(default_preset if not shared.args.flexgen else 'Naive', return_dict=True)
-
+def create_model_and_preset_menus():
with gr.Row():
with gr.Column():
with gr.Row():
@@ -114,31 +113,40 @@ def create_settings_menus(default_preset):
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')
- with gr.Accordion('Custom generation parameters', open=False, elem_id='accordion'):
- with gr.Row():
- with gr.Column():
- shared.gradio['temperature'] = gr.Slider(0.01, 1.99, value=generate_params['temperature'], step=0.01, label='temperature')
- shared.gradio['repetition_penalty'] = gr.Slider(1.0, 2.99, value=generate_params['repetition_penalty'],step=0.01,label='repetition_penalty')
- shared.gradio['top_k'] = gr.Slider(0,200,value=generate_params['top_k'],step=1,label='top_k')
- shared.gradio['top_p'] = gr.Slider(0.0,1.0,value=generate_params['top_p'],step=0.01,label='top_p')
- with gr.Column():
- shared.gradio['do_sample'] = gr.Checkbox(value=generate_params['do_sample'], label='do_sample')
- shared.gradio['typical_p'] = gr.Slider(0.0,1.0,value=generate_params['typical_p'],step=0.01,label='typical_p')
- shared.gradio['no_repeat_ngram_size'] = gr.Slider(0, 20, step=1, value=generate_params['no_repeat_ngram_size'], label='no_repeat_ngram_size')
- shared.gradio['min_length'] = gr.Slider(0, 2000, step=1, value=generate_params['min_length'] if shared.args.no_stream else 0, label='min_length', interactive=shared.args.no_stream)
+def create_settings_menus(default_preset):
+ generate_params = load_preset_values(default_preset if not shared.args.flexgen else 'Naive', return_dict=True)
- gr.Markdown('Contrastive search:')
- shared.gradio['penalty_alpha'] = gr.Slider(0, 5, value=generate_params['penalty_alpha'], label='penalty_alpha')
+ with gr.Row():
+ with gr.Column():
+ with gr.Box():
+ gr.Markdown('Custom generation parameters')
+ with gr.Row():
+ with gr.Column():
+ shared.gradio['temperature'] = gr.Slider(0.01, 1.99, value=generate_params['temperature'], step=0.01, label='temperature')
+ shared.gradio['top_p'] = gr.Slider(0.0,1.0,value=generate_params['top_p'],step=0.01,label='top_p')
+ shared.gradio['top_k'] = gr.Slider(0,200,value=generate_params['top_k'],step=1,label='top_k')
+ shared.gradio['typical_p'] = gr.Slider(0.0,1.0,value=generate_params['typical_p'],step=0.01,label='typical_p')
+ with gr.Column():
+ shared.gradio['repetition_penalty'] = gr.Slider(1.0, 1.5, value=generate_params['repetition_penalty'],step=0.01,label='repetition_penalty')
+ shared.gradio['encoder_repetition_penalty'] = gr.Slider(0.8, 1.5, value=generate_params['encoder_repetition_penalty'],step=0.01,label='encoder_repetition_penalty')
+ shared.gradio['no_repeat_ngram_size'] = gr.Slider(0, 20, step=1, value=generate_params['no_repeat_ngram_size'], label='no_repeat_ngram_size')
+ shared.gradio['min_length'] = gr.Slider(0, 2000, step=1, value=generate_params['min_length'] if shared.args.no_stream else 0, label='min_length', interactive=shared.args.no_stream)
+ shared.gradio['do_sample'] = gr.Checkbox(value=generate_params['do_sample'], label='do_sample')
+ with gr.Column():
+ with gr.Box():
+ gr.Markdown('Contrastive search')
+ shared.gradio['penalty_alpha'] = gr.Slider(0, 5, value=generate_params['penalty_alpha'], label='penalty_alpha')
- gr.Markdown('Beam search (uses a lot of VRAM):')
- with gr.Row():
- with gr.Column():
- shared.gradio['num_beams'] = gr.Slider(1, 20, step=1, value=generate_params['num_beams'], label='num_beams')
- with gr.Column():
- 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')
+ with gr.Box():
+ gr.Markdown('Beam search (uses a lot of VRAM)')
+ with gr.Row():
+ with gr.Column():
+ shared.gradio['num_beams'] = gr.Slider(1, 20, step=1, value=generate_params['num_beams'], label='num_beams')
+ with gr.Column():
+ 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')
- with gr.Accordion('Soft prompt', open=False, elem_id='accordion'):
+ with gr.Accordion('Soft prompt', open=False):
with gr.Row():
shared.gradio['softprompts_menu'] = gr.Dropdown(choices=available_softprompts, value='None', label='Soft prompt')
ui.create_refresh_button(shared.gradio['softprompts_menu'], lambda : None, lambda : {'choices': get_available_softprompts()}, 'refresh-button')
@@ -148,7 +156,7 @@ 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['do_sample'], shared.gradio['temperature'], shared.gradio['top_p'], shared.gradio['typical_p'], shared.gradio['repetition_penalty'], shared.gradio['top_k'], shared.gradio['min_length'], shared.gradio['no_repeat_ngram_size'], shared.gradio['num_beams'], shared.gradio['penalty_alpha'], shared.gradio['length_penalty'], shared.gradio['early_stopping']])
+ shared.gradio['preset_menu'].change(load_preset_values, [shared.gradio['preset_menu']], [shared.gradio['do_sample'], shared.gradio['temperature'], shared.gradio['top_p'], shared.gradio['typical_p'], shared.gradio['repetition_penalty'], shared.gradio['encoder_repetition_penalty'], shared.gradio['top_k'], shared.gradio['min_length'], shared.gradio['no_repeat_ngram_size'], shared.gradio['num_beams'], shared.gradio['penalty_alpha'], shared.gradio['length_penalty'], shared.gradio['early_stopping']])
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']])
@@ -201,26 +209,39 @@ def create_settings_menus(default_preset):
if shared.args.chat or shared.args.cai_chat:
with gr.Blocks(css=ui.css+ui.chat_css, analytics_enabled=False, title=title) as shared.gradio['interface']:
- if shared.args.cai_chat:
- shared.gradio['display'] = gr.HTML(value=generate_chat_html(shared.history['visible'], shared.settings[f'name1{suffix}'], shared.settings[f'name2{suffix}'], shared.character))
- else:
- 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')
- shared.gradio['Generate'] = gr.Button('Generate')
- with gr.Row():
- shared.gradio['Impersonate'] = gr.Button('Impersonate')
- shared.gradio['Regenerate'] = gr.Button('Regenerate')
- with gr.Row():
- shared.gradio['Copy last reply'] = gr.Button('Copy last reply')
- shared.gradio['Replace last reply'] = gr.Button('Replace last reply')
- shared.gradio['Remove last'] = gr.Button('Remove last')
-
- shared.gradio['Clear history'] = gr.Button('Clear history')
- shared.gradio['Clear history-confirm'] = gr.Button('Confirm', variant="stop", visible=False)
- shared.gradio['Clear history-cancel'] = gr.Button('Cancel', visible=False)
- with gr.Tab('Chat settings'):
+ with gr.Tab("Text generation", elem_id="main"):
+ if shared.args.cai_chat:
+ shared.gradio['display'] = gr.HTML(value=generate_chat_html(shared.history['visible'], shared.settings[f'name1{suffix}'], shared.settings[f'name2{suffix}'], shared.character))
+ else:
+ 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')
+ with gr.Row():
+ shared.gradio['Impersonate'] = gr.Button('Impersonate')
+ shared.gradio['Regenerate'] = gr.Button('Regenerate')
+ with gr.Row():
+ shared.gradio['Copy last reply'] = gr.Button('Copy last reply')
+ shared.gradio['Replace last reply'] = gr.Button('Replace last reply')
+ shared.gradio['Remove last'] = gr.Button('Remove last')
+
+ shared.gradio['Clear history'] = gr.Button('Clear history')
+ 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.Box():
+ with gr.Row():
+ with gr.Column():
+ 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'])
+ shared.gradio['chat_prompt_size_slider'] = gr.Slider(minimum=shared.settings['chat_prompt_size_min'], maximum=shared.settings['chat_prompt_size_max'], step=1, label='Maximum prompt size in tokens', value=shared.settings['chat_prompt_size'])
+ with gr.Column():
+ shared.gradio['chat_generation_attempts'] = gr.Slider(minimum=shared.settings['chat_generation_attempts_min'], maximum=shared.settings['chat_generation_attempts_max'], value=shared.settings['chat_generation_attempts'], step=1, label='Generation attempts (for longer replies)')
+ shared.gradio['check'] = gr.Checkbox(value=shared.settings[f'stop_at_newline{suffix}'], label='Stop generating at new line character?')
+
+ with gr.Tab("Chat settings", 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')
shared.gradio['context'] = gr.Textbox(value=shared.settings[f'context{suffix}'], lines=5, label='Context')
@@ -228,8 +249,6 @@ def create_settings_menus(default_preset):
shared.gradio['character_menu'] = gr.Dropdown(choices=available_characters, value='None', label='Character', elem_id='character-menu')
ui.create_refresh_button(shared.gradio['character_menu'], lambda : None, lambda : {'choices': get_available_characters()}, 'refresh-button')
- with gr.Row():
- shared.gradio['check'] = gr.Checkbox(value=shared.settings[f'stop_at_newline{suffix}'], label='Stop generating at new line character?')
with gr.Row():
with gr.Tab('Chat history'):
with gr.Row():
@@ -254,23 +273,16 @@ def create_settings_menus(default_preset):
with gr.Tab('Upload TavernAI Character Card'):
shared.gradio['upload_img_tavern'] = gr.File(type='binary', file_types=['image'])
- with gr.Tab('Generation settings'):
- with gr.Row():
- with gr.Column():
- 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'])
- with gr.Column():
- shared.gradio['chat_prompt_size_slider'] = gr.Slider(minimum=shared.settings['chat_prompt_size_min'], maximum=shared.settings['chat_prompt_size_max'], step=1, label='Maximum prompt size in tokens', value=shared.settings['chat_prompt_size'])
- shared.gradio['chat_generation_attempts'] = gr.Slider(minimum=shared.settings['chat_generation_attempts_min'], maximum=shared.settings['chat_generation_attempts_max'], value=shared.settings['chat_generation_attempts'], step=1, label='Generation attempts (for longer replies)')
+ with gr.Tab("Settings", elem_id="settings"):
create_settings_menus(default_preset)
- shared.input_params = [shared.gradio[k] for k in ['textbox', 'max_new_tokens', 'do_sample', 'temperature', 'top_p', 'typical_p', 'repetition_penalty', 'top_k', 'min_length', 'no_repeat_ngram_size', 'num_beams', 'penalty_alpha', 'length_penalty', 'early_stopping', 'name1', 'name2', 'context', 'check', 'chat_prompt_size_slider', 'chat_generation_attempts']]
if shared.args.extensions is not None:
- with gr.Tab('Extensions'):
- extensions_module.create_extensions_block()
+ extensions_module.create_extensions_block()
function_call = 'chat.cai_chatbot_wrapper' if shared.args.cai_chat else 'chat.chatbot_wrapper'
+ shared.input_params = [shared.gradio[k] for k in ['textbox', 'max_new_tokens', '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', 'name1', 'name2', 'context', 'check', 'chat_prompt_size_slider', 'chat_generation_attempts']]
- gen_events.append(shared.gradio['Generate'].click(eval(function_call), shared.input_params, shared.gradio['display'], show_progress=shared.args.no_stream, api_name='textgen'))
+ gen_events.append(shared.gradio['Generate'].click(eval(function_call), shared.input_params, shared.gradio['display'], show_progress=shared.args.no_stream))
gen_events.append(shared.gradio['textbox'].submit(eval(function_call), shared.input_params, shared.gradio['display'], show_progress=shared.args.no_stream))
gen_events.append(shared.gradio['Regenerate'].click(chat.regenerate_wrapper, shared.input_params, shared.gradio['display'], show_progress=shared.args.no_stream))
gen_events.append(shared.gradio['Impersonate'].click(chat.impersonate_wrapper, shared.input_params, shared.gradio['textbox'], show_progress=shared.args.no_stream))
@@ -309,65 +321,75 @@ def create_settings_menus(default_preset):
shared.gradio['upload_img_me'].upload(reload_func, reload_inputs, [shared.gradio['display']])
shared.gradio['Stop'].click(reload_func, reload_inputs, [shared.gradio['display']])
+ shared.gradio['interface'].load(None, None, None, _js=f"() => {{{ui.main_js+ui.chat_js}}}")
shared.gradio['interface'].load(lambda : chat.load_default_history(shared.settings[f'name1{suffix}'], shared.settings[f'name2{suffix}']), None, None)
shared.gradio['interface'].load(reload_func, reload_inputs, [shared.gradio['display']], show_progress=True)
elif shared.args.notebook:
with gr.Blocks(css=ui.css, analytics_enabled=False, title=title) as shared.gradio['interface']:
- gr.Markdown(description)
- with gr.Tab('Raw'):
- shared.gradio['textbox'] = gr.Textbox(value=default_text, lines=23)
- with gr.Tab('Markdown'):
- shared.gradio['markdown'] = gr.Markdown()
- with gr.Tab('HTML'):
- shared.gradio['html'] = gr.HTML()
-
- shared.gradio['Generate'] = gr.Button('Generate')
- shared.gradio['Stop'] = gr.Button('Stop')
- 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_settings_menus(default_preset)
+ with gr.Tab("Text generation", elem_id="main"):
+ with gr.Tab('Raw'):
+ shared.gradio['textbox'] = gr.Textbox(value=default_text, 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['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()
+ with gr.Tab("Settings", elem_id="settings"):
+ create_settings_menus(default_preset)
+
if shared.args.extensions is not None:
extensions_module.create_extensions_block()
- shared.input_params = [shared.gradio[k] for k in ['textbox', 'max_new_tokens', 'do_sample', 'temperature', 'top_p', 'typical_p', 'repetition_penalty', 'top_k', 'min_length', 'no_repeat_ngram_size', 'num_beams', 'penalty_alpha', 'length_penalty', 'early_stopping']]
+ shared.input_params = [shared.gradio[k] for k in ['textbox', 'max_new_tokens', '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']]
output_params = [shared.gradio[k] for k in ['textbox', 'markdown', 'html']]
gen_events.append(shared.gradio['Generate'].click(generate_reply, shared.input_params, output_params, show_progress=shared.args.no_stream, api_name='textgen'))
gen_events.append(shared.gradio['textbox'].submit(generate_reply, shared.input_params, output_params, show_progress=shared.args.no_stream))
shared.gradio['Stop'].click(None, None, None, cancels=gen_events)
+ shared.gradio['interface'].load(None, None, None, _js=f"() => {{{ui.main_js}}}")
else:
with gr.Blocks(css=ui.css, analytics_enabled=False, title=title) as shared.gradio['interface']:
- gr.Markdown(description)
- with gr.Row():
- with gr.Column():
- shared.gradio['textbox'] = gr.Textbox(value=default_text, lines=15, label='Input')
- 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'])
- shared.gradio['Generate'] = gr.Button('Generate')
- with gr.Row():
- with gr.Column():
- shared.gradio['Continue'] = gr.Button('Continue')
- with gr.Column():
- shared.gradio['Stop'] = gr.Button('Stop')
+ with gr.Tab("Text generation", elem_id="main"):
+ with gr.Row():
+ with gr.Column():
+ shared.gradio['textbox'] = gr.Textbox(value=default_text, lines=15, label='Input')
+ 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'])
+ shared.gradio['Generate'] = gr.Button('Generate')
+ with gr.Row():
+ with gr.Column():
+ shared.gradio['Continue'] = gr.Button('Continue')
+ with gr.Column():
+ shared.gradio['Stop'] = gr.Button('Stop')
- create_settings_menus(default_preset)
- if shared.args.extensions is not None:
- extensions_module.create_extensions_block()
+ create_model_and_preset_menus()
- with gr.Column():
- with gr.Tab('Raw'):
- shared.gradio['output_textbox'] = gr.Textbox(lines=15, label='Output')
- with gr.Tab('Markdown'):
- shared.gradio['markdown'] = gr.Markdown()
- with gr.Tab('HTML'):
- shared.gradio['html'] = gr.HTML()
+ with gr.Column():
+ with gr.Tab('Raw'):
+ shared.gradio['output_textbox'] = gr.Textbox(lines=25, label='Output')
+ with gr.Tab('Markdown'):
+ shared.gradio['markdown'] = gr.Markdown()
+ with gr.Tab('HTML'):
+ shared.gradio['html'] = gr.HTML()
+ with gr.Tab("Settings", elem_id="settings"):
+ create_settings_menus(default_preset)
+
+ if shared.args.extensions is not None:
+ extensions_module.create_extensions_block()
- shared.input_params = [shared.gradio[k] for k in ['textbox', 'max_new_tokens', 'do_sample', 'temperature', 'top_p', 'typical_p', 'repetition_penalty', 'top_k', 'min_length', 'no_repeat_ngram_size', 'num_beams', 'penalty_alpha', 'length_penalty', 'early_stopping']]
+ shared.input_params = [shared.gradio[k] for k in ['textbox', 'max_new_tokens', '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']]
output_params = [shared.gradio[k] for k in ['output_textbox', 'markdown', 'html']]
gen_events.append(shared.gradio['Generate'].click(generate_reply, shared.input_params, output_params, show_progress=shared.args.no_stream, api_name='textgen'))
gen_events.append(shared.gradio['textbox'].submit(generate_reply, shared.input_params, output_params, show_progress=shared.args.no_stream))
gen_events.append(shared.gradio['Continue'].click(generate_reply, [shared.gradio['output_textbox']] + shared.input_params[1:], output_params, show_progress=shared.args.no_stream))
shared.gradio['Stop'].click(None, None, None, cancels=gen_events)
+ shared.gradio['interface'].load(None, None, None, _js=f"() => {{{ui.main_js}}}")
shared.gradio['interface'].queue()
if shared.args.listen:
{x}
" for x in row[1].split('\n')]) + row = [markdown.markdown(re.sub(r"(.)```", r"\1\n```", entry), extensions=['fenced_code']) for entry in _row] + output += f""" """ if not (i == len(history)-1 and len(row[0]) == 0): - p = '\n'.join([f"{x}
" for x in row[0].split('\n')]) output += f"""