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import gradio as gr
from modules import loaders, presets, shared, ui, utils
from modules.utils import gradio
def create_ui(default_preset):
generate_params = presets.load_preset(default_preset)
with gr.Tab("Parameters", elem_id="parameters"):
with gr.Row():
with gr.Column():
with gr.Row():
shared.gradio['preset_menu'] = gr.Dropdown(choices=utils.get_available_presets(), value=default_preset, label='Generation parameters preset', elem_classes='slim-dropdown')
ui.create_refresh_button(shared.gradio['preset_menu'], lambda: None, lambda: {'choices': utils.get_available_presets()}, 'refresh-button')
shared.gradio['save_preset'] = gr.Button('💾', elem_classes='refresh-button')
shared.gradio['delete_preset'] = gr.Button('🗑️', elem_classes='refresh-button')
with gr.Column():
shared.gradio['filter_by_loader'] = gr.Dropdown(label="Filter by loader", choices=["All", "Transformers", "ExLlama_HF", "ExLlama", "AutoGPTQ", "GPTQ-for-LLaMa", "llama.cpp", "llamacpp_HF"], value="All", elem_classes='slim-dropdown')
with gr.Row():
with gr.Column():
with gr.Box():
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')
shared.gradio['epsilon_cutoff'] = gr.Slider(0, 9, value=generate_params['epsilon_cutoff'], step=0.01, label='epsilon_cutoff')
shared.gradio['eta_cutoff'] = gr.Slider(0, 20, value=generate_params['eta_cutoff'], step=0.01, label='eta_cutoff')
shared.gradio['tfs'] = gr.Slider(0.0, 1.0, value=generate_params['tfs'], step=0.01, label='tfs')
shared.gradio['top_a'] = gr.Slider(0.0, 1.0, value=generate_params['top_a'], step=0.01, label='top_a')
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['repetition_penalty_range'] = gr.Slider(0, 4096, step=64, value=generate_params['repetition_penalty_range'], label='repetition_penalty_range')
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'], label='min_length')
shared.gradio['seed'] = gr.Number(value=shared.settings['seed'], label='Seed (-1 for random)')
shared.gradio['do_sample'] = gr.Checkbox(value=generate_params['do_sample'], label='do_sample')
with gr.Accordion("Learn more", open=False):
gr.Markdown("""
For a technical description of the parameters, the [transformers documentation](https://huggingface.co/docs/transformers/main_classes/text_generation#transformers.GenerationConfig) is a good reference.
The best presets, according to the [Preset Arena](https://github.com/oobabooga/oobabooga.github.io/blob/main/arena/results.md) experiment, are:
* Instruction following:
1) Divine Intellect
2) Big O
3) simple-1
4) Space Alien
5) StarChat
6) Titanic
7) tfs-with-top-a
8) Asterism
9) Contrastive Search
* Chat:
1) Midnight Enigma
2) Yara
3) Shortwave
### Temperature
Primary factor to control randomness of outputs. 0 = deterministic (only the most likely token is used). Higher value = more randomness.
### top_p
If not set to 1, select tokens with probabilities adding up to less than this number. Higher value = higher range of possible random results.
### top_k
Similar to top_p, but select instead only the top_k most likely tokens. Higher value = higher range of possible random results.
### typical_p
If not set to 1, select only tokens that are at least this much more likely to appear than random tokens, given the prior text.
### epsilon_cutoff
In units of 1e-4; a reasonable value is 3. This sets a probability floor below which tokens are excluded from being sampled. Should be used with top_p, top_k, and eta_cutoff set to 0.
### eta_cutoff
In units of 1e-4; a reasonable value is 3. Should be used with top_p, top_k, and epsilon_cutoff set to 0.
### repetition_penalty
Exponential penalty factor for repeating prior tokens. 1 means no penalty, higher value = less repetition, lower value = more repetition.
### repetition_penalty_range
The number of most recent tokens to consider for repetition penalty. 0 makes all tokens be used.
### encoder_repetition_penalty
Also known as the "Hallucinations filter". Used to penalize tokens that are *not* in the prior text. Higher value = more likely to stay in context, lower value = more likely to diverge.
### no_repeat_ngram_size
If not set to 0, specifies the length of token sets that are completely blocked from repeating at all. Higher values = blocks larger phrases, lower values = blocks words or letters from repeating. Only 0 or high values are a good idea in most cases.
### min_length
Minimum generation length in tokens.
### penalty_alpha
Contrastive Search is enabled by setting this to greater than zero and unchecking "do_sample". It should be used with a low value of top_k, for instance, top_k = 4.
""", elem_classes="markdown")
with gr.Column():
create_chat_settings_menus()
with gr.Box():
with gr.Row():
with gr.Column():
shared.gradio['guidance_scale'] = gr.Slider(-0.5, 2.5, step=0.05, value=generate_params['guidance_scale'], label='guidance_scale', info='For CFG. 1.5 is a good value.')
shared.gradio['negative_prompt'] = gr.Textbox(value=shared.settings['negative_prompt'], label='Negative prompt')
shared.gradio['mirostat_mode'] = gr.Slider(0, 2, step=1, value=generate_params['mirostat_mode'], label='mirostat_mode', info='mode=1 is for llama.cpp only.')
shared.gradio['mirostat_tau'] = gr.Slider(0, 10, step=0.01, value=generate_params['mirostat_tau'], label='mirostat_tau')
shared.gradio['mirostat_eta'] = gr.Slider(0, 1, step=0.01, value=generate_params['mirostat_eta'], label='mirostat_eta')
with gr.Column():
shared.gradio['penalty_alpha'] = gr.Slider(0, 5, value=generate_params['penalty_alpha'], label='penalty_alpha', info='For Contrastive Search. do_sample must be unchecked.')
shared.gradio['num_beams'] = gr.Slider(1, 20, step=1, value=generate_params['num_beams'], label='num_beams', info='For Beam Search, along with length_penalty and early_stopping.')
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():
with gr.Row():
with gr.Column():
shared.gradio['truncation_length'] = gr.Slider(value=shared.settings['truncation_length'], minimum=shared.settings['truncation_length_min'], maximum=shared.settings['truncation_length_max'], step=256, label='Truncate the prompt up to this length', info='The leftmost tokens are removed if the prompt exceeds this length. Most models require this to be at most 2048.')
shared.gradio['custom_stopping_strings'] = gr.Textbox(lines=1, value=shared.settings["custom_stopping_strings"] or None, label='Custom stopping strings', info='In addition to the defaults. Written between "" and separated by commas. For instance: "\\nYour Assistant:", "\\nThe assistant:"')
with gr.Column():
shared.gradio['auto_max_new_tokens'] = gr.Checkbox(value=shared.settings['auto_max_new_tokens'], label='auto_max_new_tokens', info='Expand max_new_tokens to the available context length.')
shared.gradio['ban_eos_token'] = gr.Checkbox(value=shared.settings['ban_eos_token'], label='Ban the eos_token', info='Forces the model to never end the generation prematurely.')
shared.gradio['add_bos_token'] = gr.Checkbox(value=shared.settings['add_bos_token'], label='Add the bos_token to the beginning of prompts', info='Disabling this can make the replies more creative.')
shared.gradio['skip_special_tokens'] = gr.Checkbox(value=shared.settings['skip_special_tokens'], label='Skip special tokens', info='Some specific models need this unset.')
shared.gradio['stream'] = gr.Checkbox(value=not shared.args.no_stream, label='Activate text streaming')
def create_event_handlers():
shared.gradio['filter_by_loader'].change(loaders.blacklist_samplers, gradio('filter_by_loader'), gradio(loaders.list_all_samplers()), show_progress=False)
shared.gradio['preset_menu'].change(presets.load_preset_for_ui, gradio('preset_menu', 'interface_state'), gradio('interface_state') + gradio(presets.presets_params()))
def create_chat_settings_menus():
if not shared.is_chat():
return
with gr.Box():
gr.Markdown("Chat parameters")
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_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)', info='New generations will be called until either this number is reached or no new content is generated between two iterations.')
with gr.Column():
shared.gradio['stop_at_newline'] = gr.Checkbox(value=shared.settings['stop_at_newline'], label='Stop generating at new line character')
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