mirror of
https://github.com/oobabooga/text-generation-webui.git
synced 2024-11-23 16:38:21 +01:00
oobabooga
GitHub
commit
b28020a9e4
@ -22,17 +22,12 @@
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.message-body p, .message-body li {
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font-size: 15px !important;
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line-height: 24px !important;
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list-style-position: outside;
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}
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.message-body p, .chat .message-body ul, .chat .message-body ol {
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margin-bottom: 16px !important;
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}
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.chat .message-body ul, .chat .message-body ol {
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padding-inline-start: 2em;
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}
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.message-body p:last-child, .chat .message-body ul:last-child, .chat .message-body ol:last-child {
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margin-bottom: 0 !important;
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}
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@ -364,6 +364,14 @@ div.svelte-362y77>*, div.svelte-362y77>.form>* {
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padding-bottom: 0 !important;
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}
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.message-body li {
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list-style-position: outside;
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}
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.chat .message-body ul, .chat .message-body ol {
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padding-inline-start: 2em;
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}
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.message-body li:not(:last-child) {
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margin-top: 0 !important;
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margin-bottom: 2px !important;
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@ -51,59 +51,9 @@ from modules.logging_colors import logger
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from modules.models import reload_model
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from modules.utils import natural_keys
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## just temporary to avoid warning
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import inspect
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from typing import Callable, Optional, Tuple, ContextManager
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if hasattr(torch.utils.checkpoint, 'noop_context_fn'):
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def my_checkpoint(
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function,
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*args,
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use_reentrant: Optional[bool] = None,
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context_fn: Callable[[], Tuple[ContextManager, ContextManager]] = torch.utils.checkpoint.noop_context_fn,
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determinism_check: str = torch.utils.checkpoint._DEFAULT_DETERMINISM_MODE,
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debug: bool = False,
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**kwargs
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):
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if use_reentrant is None:
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#print ("reentran = NONE")
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use_reentrant = True
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# Hack to mix *args with **kwargs in a python 2.7-compliant way
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preserve = kwargs.pop("preserve_rng_state", True)
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if kwargs and use_reentrant:
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raise ValueError(
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"Unexpected keyword arguments: " + ",".join(arg for arg in kwargs)
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)
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if use_reentrant:
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if context_fn is not torch.utils.checkpoint.noop_context_fn or debug is not False:
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raise ValueError(
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"Passing `context_fn` or `debug` is only supported when "
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"use_reentrant=False."
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)
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return torch.utils.checkpoint.CheckpointFunction.apply(function, preserve, *args)
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else:
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print ("reentran = FALSE")
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gen = torch.utils.checkpoint._checkpoint_without_reentrant_generator(
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function, preserve, context_fn, determinism_check, debug, *args, **kwargs
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)
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# Runs pre-forward logic
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next(gen)
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ret = function(*args, **kwargs)
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# Runs post-forward logic
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try:
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next(gen)
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except StopIteration:
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return ret
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import warnings
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warnings.filterwarnings(action = "ignore", message="torch.utils.checkpoint:")
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warnings.filterwarnings(action = "ignore", message="`do_sample` is set to `False`")
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params = {
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"display_name": "Training PRO",
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@ -121,6 +71,7 @@ non_serialized_params = {
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"save_epochs": 0,
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"checkpoint_offset": 0,
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"epoch_offset":0,
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"safe_serialization": False,
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}
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MODEL_CLASSES = {v[1]: v[0] for v in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES.items()}
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@ -150,7 +101,7 @@ def ui():
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with gr.Row():
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with gr.Column():
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# YY.MM.DD
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gr.Markdown("`Ver: 23.10.20` This is enhanced version of QLora Training. [Maintained by FP](https://github.com/FartyPants/Training_PRO/tree/main)")
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gr.Markdown("`Ver: 23.10.20 (REV2)` This is enhanced version of QLora Training. [Maintained by FP](https://github.com/FartyPants/Training_PRO/tree/main)")
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with gr.Row():
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with gr.Column(scale=5):
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@ -290,7 +241,7 @@ def ui():
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stride_length = gr.Slider(label='Stride', minimum=1, maximum=2048, value=512, step=1, info='Used to make the evaluation faster at the cost of accuracy. 1 = slowest but most accurate. 512 is a common value.')
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with gr.Column():
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max_length = gr.Slider(label='max_length', minimum=0, maximum=8096, value=0, step=1, info='The context for each evaluation. If set to 0, the maximum context length for the model will be used.')
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max_length = gr.Slider(label='max_length', minimum=0, maximum=shared.settings['truncation_length_max'], value=0, step=1, info='The context for each evaluation. If set to 0, the maximum context length for the model will be used.')
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with gr.Row():
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start_current_evaluation = gr.Button("Evaluate loaded model")
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@ -713,7 +664,6 @@ def do_train(lora_name: str, always_override: bool, save_steps: int, micro_batch
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train_template.clear()
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#reset stuff
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print(f"*** LoRA: {lora_name} ***")
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non_serialized_params.update({"stop_at_loss": stop_at_loss})
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@ -726,25 +676,7 @@ def do_train(lora_name: str, always_override: bool, save_steps: int, micro_batch
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non_serialized_params.update({"epoch_offset": 0})
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train_log_graph.clear()
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# === once fixed, this can be removed ==============================
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if hasattr(torch.utils.checkpoint, 'noop_context_fn'):
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print("Testing Pytorch...")
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old_checkpoint_signature = inspect.signature(torch.utils.checkpoint.checkpoint)
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# Get the signature of your new checkpoint function
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my_checkpoint_signature = inspect.signature(my_checkpoint)
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# Check if the signatures match
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if old_checkpoint_signature.parameters == my_checkpoint_signature.parameters:
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print(F"{RED}Overriding Torch checkpoint function to avoid repeated 'use_reentrant not explicitly set' warnings{RESET}")
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#print(" - Note: Transformers need to pass use_reentrant in llama.modeling_llama in def forward, layer_outputs = torch.utils.checkpoint.checkpoint")
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#print(" Once they do, this function can be removed")
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torch.utils.checkpoint.checkpoint = my_checkpoint
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# END OF FPHAM SENTENCE SPLIT functions ===================
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# == Prep the dataset, format, etc ==
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# == Prep the dataset, format, etc ==
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if raw_text_file not in ['None', '']:
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train_template["template_type"] = "raw_text"
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logger.info("Loading text file...")
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@ -1025,7 +957,7 @@ def do_train(lora_name: str, always_override: bool, save_steps: int, micro_batch
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force_save = True
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if force_save:
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lora_model.save_pretrained(f"{lora_file_path}/{folder_save}/")
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lora_model.save_pretrained(f"{lora_file_path}/{folder_save}/", safe_serialization = non_serialized_params['safe_serialization'])
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print(f"\033[1;30;40mStep: {tracked.current_steps:6} \033[0;37;0m Saved: [{folder_save}]")
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# Save log
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with open(f"{lora_file_path}/{folder_save}/training_log.json", 'w', encoding='utf-8') as file:
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@ -1252,7 +1184,7 @@ def do_train(lora_name: str, always_override: bool, save_steps: int, micro_batch
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log_train_dataset(trainer)
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trainer.train()
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# Note: save in the thread in case the gradio thread breaks (eg browser closed)
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lora_model.save_pretrained(lora_file_path)
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lora_model.save_pretrained(lora_file_path, safe_serialization = non_serialized_params['safe_serialization'])
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logger.info("LoRA training run is completed and saved.")
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# Save log
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with open(f"{lora_file_path}/training_log.json", 'w', encoding='utf-8') as file:
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@ -1353,7 +1285,7 @@ def do_train(lora_name: str, always_override: bool, save_steps: int, micro_batch
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if not tracked.did_save:
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logger.info("Training complete, saving...")
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lora_model.save_pretrained(lora_file_path)
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lora_model.save_pretrained(lora_file_path, safe_serialization = non_serialized_params['safe_serialization'])
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if WANT_INTERRUPT:
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logger.info("Training interrupted.")
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@ -1,25 +0,0 @@
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instruction_template: |-
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{%- set found_item = false -%}
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{%- for message in messages -%}
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{%- if message['role'] == 'system' -%}
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{%- set found_item = true -%}
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{%- endif -%}
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{%- endfor -%}
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{%- if not found_item -%}
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{{- '' + 'A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human\'s questions.' + '\n\n' -}}
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{%- endif %}
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{%- for message in messages %}
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{%- if message['role'] == 'system' -%}
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{{- '' + message['content'] + '\n\n' -}}
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{%- else -%}
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{%- if message['role'] == 'user' -%}
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{{-'### Human: ' + message['content'] + '\n'-}}
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{%- else -%}
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{{-'### Assistant: ' + message['content'] + '\n' -}}
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{%- endif -%}
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{%- endif -%}
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{%- endfor -%}
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{%- if add_generation_prompt -%}
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{{-'### Assistant:'-}}
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{%- endif -%}
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@ -1,25 +0,0 @@
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instruction_template: |-
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{%- set found_item = false -%}
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{%- for message in messages -%}
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{%- if message['role'] == 'system' -%}
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{%- set found_item = true -%}
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{%- endif -%}
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{%- endfor -%}
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{%- if not found_item -%}
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{{- '' + '' + '' -}}
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{%- endif %}
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{%- for message in messages %}
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{%- if message['role'] == 'system' -%}
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{{- '' + message['content'] + '' -}}
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{%- else -%}
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{%- if message['role'] == 'user' -%}
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{{-'<human>: ' + message['content'] + '\n'-}}
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{%- else -%}
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{{-'<bot>:' + message['content'] + '\n' -}}
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{%- endif -%}
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{%- endif -%}
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{%- endfor -%}
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{%- if add_generation_prompt -%}
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{{-'<bot>:'-}}
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{%- endif -%}
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@ -1,25 +0,0 @@
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instruction_template: |-
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{%- set found_item = false -%}
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{%- for message in messages -%}
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{%- if message['role'] == 'system' -%}
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{%- set found_item = true -%}
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{%- endif -%}
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{%- endfor -%}
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{%- if not found_item -%}
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{{- '' + 'A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user\'s questions.' + '\n\n' -}}
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{%- endif %}
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{%- for message in messages %}
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{%- if message['role'] == 'system' -%}
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{{- '' + message['content'] + '\n\n' -}}
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{%- else -%}
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{%- if message['role'] == 'user' -%}
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{{-'USER: ' + message['content'] + '\n'-}}
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{%- else -%}
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{{-'ASSISTANT: ' + message['content'] + '</s>\n' -}}
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{%- endif -%}
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{%- endif -%}
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{%- endfor -%}
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{%- if add_generation_prompt -%}
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{{-'ASSISTANT:'-}}
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{%- endif -%}
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@ -6,20 +6,19 @@ instruction_template: |-
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{%- endif -%}
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{%- endfor -%}
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{%- if not found_item -%}
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{{- '' + '' + '' -}}
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{{-'SYSTEM: ' + '' + '\n' -}}
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{%- endif %}
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{%- for message in messages %}
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{%- if message['role'] == 'system' -%}
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{{- '' + message['content'] + '' -}}
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{{-'SYSTEM: ' + message['content'] + '\n' -}}
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{%- else -%}
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{%- if message['role'] == 'user' -%}
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{{-'USER: ' + message['content'] + '\n'-}}
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{%- else -%}
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{{-'ASSISTANT:' + message['content'] + '\n' -}}
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{{-'ASSISTANT: ' + message['content'] + '</s>\n' -}}
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{%- endif -%}
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{%- endif -%}
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{%- endfor -%}
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{%- if add_generation_prompt -%}
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{{-'ASSISTANT:'-}}
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{%- endif -%}
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@ -13,9 +13,9 @@ instruction_template: |-
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{{- '' + message['content'] + '\n' -}}
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{%- else -%}
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{%- if message['role'] == 'user' -%}
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{{-'\n<|USER|>: ' + message['content'] + '\n'-}}
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{{-'<|USER|>: ' + message['content'] + '\n'-}}
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{%- else -%}
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{{-'<|ASSISTANT|>: ' + message['content'] + '' -}}
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{{-'<|ASSISTANT|>: ' + message['content'] + '\n' -}}
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{%- endif -%}
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{%- endif -%}
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{%- endfor -%}
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@ -1,25 +0,0 @@
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instruction_template: |-
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{%- set found_item = false -%}
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{%- for message in messages -%}
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{%- if message['role'] == 'system' -%}
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{%- set found_item = true -%}
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{%- endif -%}
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{%- endfor -%}
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{%- if not found_item -%}
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{{- '' + 'Below is an instruction that describes a task. Write a response that appropriately completes the request.' + '\n\n' -}}
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{%- endif %}
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{%- for message in messages %}
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{%- if message['role'] == 'system' -%}
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{{- '' + message['content'] + '\n\n' -}}
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{%- else -%}
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{%- if message['role'] == 'user' -%}
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{{-'### Instruction:\n' + message['content'] + '\n\n'-}}
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{%- else -%}
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{{-'### Response:\n' + message['content'] + '\n\n' -}}
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{%- endif -%}
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{%- endif -%}
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{%- endfor -%}
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{%- if add_generation_prompt -%}
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{{-'### Response:\n'-}}
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{%- endif -%}
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@ -38,7 +38,7 @@
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instruction_template: 'LLaVA'
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custom_stopping_strings: '"\n###"'
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.*llava.*1.5:
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instruction_template: 'LLaVA-v1'
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instruction_template: 'Vicuna-v1.1'
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.*wizard.*mega:
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instruction_template: 'Wizard-Mega'
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custom_stopping_strings: '"</s>"'
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@ -108,7 +108,7 @@
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.*bactrian:
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instruction_template: 'Bactrian'
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.*(h2ogpt-oig-|h2ogpt-oasst1-|h2ogpt-research-oasst1-):
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instruction_template: 'H2O-human_bot'
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instruction_template: 'INCITE-Chat'
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.*h2ogpt-gm-:
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instruction_template: 'H2O-prompt_answer'
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.*manticore:
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@ -128,7 +128,7 @@
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.*lazarus:
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instruction_template: 'Alpaca'
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.*guanaco-.*(7|13|33|65)b:
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instruction_template: 'Guanaco'
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instruction_template: 'Vicuna-v0'
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.*hypermantis:
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instruction_template: 'Alpaca'
|
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.*open-llama-.*-open-instruct:
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@ -144,7 +144,7 @@
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.*wizardcoder:
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instruction_template: 'Alpaca'
|
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.*minotaur:
|
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instruction_template: 'Minotaur'
|
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instruction_template: 'Manticore Chat'
|
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.*orca_mini:
|
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instruction_template: 'Orca Mini'
|
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.*(platypus|gplatty|superplatty):
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@ -186,3 +186,5 @@
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instruction_template: 'ChatML'
|
||||
.*Yi-34B-Chat:
|
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instruction_template: 'ChatML'
|
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(dolphin).*:
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instruction_template: 'ChatML'
|
||||
|
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@ -112,6 +112,13 @@ def generate_chat_prompt(user_input, state, **kwargs):
|
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if user_input and not impersonate and not _continue:
|
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messages.append({"role": "user", "content": user_input})
|
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|
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def remove_extra_bos(prompt):
|
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for bos_token in ['<s>', '<|startoftext|>']:
|
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while prompt.startswith(bos_token):
|
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prompt = prompt[len(bos_token):]
|
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|
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return prompt
|
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|
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def make_prompt(messages):
|
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if state['mode'] == 'chat-instruct' and _continue:
|
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prompt = renderer(messages=messages[:-1])
|
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@ -123,6 +130,7 @@ def generate_chat_prompt(user_input, state, **kwargs):
|
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if state['custom_system_message'].strip() != '':
|
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outer_messages.append({"role": "system", "content": state['custom_system_message']})
|
||||
|
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prompt = remove_extra_bos(prompt)
|
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command = state['chat-instruct_command']
|
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command = command.replace('<|character|>', state['name2'] if not impersonate else state['name1'])
|
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command = command.replace('<|prompt|>', prompt)
|
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@ -153,6 +161,7 @@ def generate_chat_prompt(user_input, state, **kwargs):
|
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|
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prompt += prefix
|
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|
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prompt = remove_extra_bos(prompt)
|
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return prompt
|
||||
|
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prompt = make_prompt(messages)
|
||||
|
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@ -82,8 +82,9 @@ def load_metadata(fname):
|
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if value_type == GGUFValueType.ARRAY:
|
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ltype = GGUFValueType(struct.unpack("<I", file.read(4))[0])
|
||||
length = struct.unpack("<Q", file.read(8))[0]
|
||||
for j in range(length):
|
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_ = get_single(ltype, file)
|
||||
|
||||
arr = [get_single(ltype, file) for _ in range(length)]
|
||||
metadata[key.decode()] = arr
|
||||
else:
|
||||
value = get_single(value_type, file)
|
||||
metadata[key.decode()] = value
|
||||
|
||||
@ -64,6 +64,16 @@ def get_model_metadata(model):
|
||||
model_settings['compress_pos_emb'] = metadata['llama.rope.scale_linear']
|
||||
if 'llama.rope.freq_base' in metadata:
|
||||
model_settings['rope_freq_base'] = metadata['llama.rope.freq_base']
|
||||
if 'tokenizer.chat_template' in metadata:
|
||||
template = metadata['tokenizer.chat_template']
|
||||
eos_token = metadata['tokenizer.ggml.tokens'][metadata['tokenizer.ggml.eos_token_id']]
|
||||
bos_token = metadata['tokenizer.ggml.tokens'][metadata['tokenizer.ggml.bos_token_id']]
|
||||
template = template.replace('eos_token', "'{}'".format(eos_token))
|
||||
template = template.replace('bos_token', "'{}'".format(bos_token))
|
||||
|
||||
template = re.sub(r'raise_exception\([^)]*\)', "''", template)
|
||||
model_settings['instruction_template'] = 'Custom (obtained from model metadata)'
|
||||
model_settings['instruction_template_str'] = template
|
||||
|
||||
else:
|
||||
# Transformers metadata
|
||||
@ -114,7 +124,6 @@ def get_model_metadata(model):
|
||||
template = template.replace(k, "'{}'".format(value))
|
||||
|
||||
template = re.sub(r'raise_exception\([^)]*\)', "''", template)
|
||||
|
||||
model_settings['instruction_template'] = 'Custom (obtained from model metadata)'
|
||||
model_settings['instruction_template_str'] = template
|
||||
|
||||
|
||||
@ -107,7 +107,7 @@ def create_chat_settings_ui():
|
||||
with gr.Row():
|
||||
with gr.Column():
|
||||
with gr.Row():
|
||||
shared.gradio['instruction_template'] = gr.Dropdown(choices=utils.get_available_instruction_templates(), label='Saved instruction templates', value='Custom', info='Change this according to the model/LoRA that you are using. Used in instruct and chat-instruct modes.', elem_classes='slim-dropdown')
|
||||
shared.gradio['instruction_template'] = gr.Dropdown(choices=utils.get_available_instruction_templates(), label='Saved instruction templates', value='Select template to load...', elem_classes='slim-dropdown')
|
||||
ui.create_refresh_button(shared.gradio['instruction_template'], lambda: None, lambda: {'choices': utils.get_available_instruction_templates()}, 'refresh-button', interactive=not mu)
|
||||
shared.gradio['load_template'] = gr.Button("Load", elem_classes='refresh-button')
|
||||
shared.gradio['save_template'] = gr.Button('💾', elem_classes='refresh-button', interactive=not mu)
|
||||
@ -119,7 +119,7 @@ def create_chat_settings_ui():
|
||||
with gr.Row():
|
||||
with gr.Column():
|
||||
shared.gradio['custom_system_message'] = gr.Textbox(value=shared.settings['custom_system_message'], lines=2, label='Custom system message', info='If not empty, will be used instead of the default one.', elem_classes=['add_scrollbar'])
|
||||
shared.gradio['instruction_template_str'] = gr.Textbox(value='', label='Instruction template', lines=24, elem_classes=['add_scrollbar', 'monospace'])
|
||||
shared.gradio['instruction_template_str'] = gr.Textbox(value='', label='Instruction template', lines=24, info='Change this according to the model/LoRA that you are using. Used in instruct and chat-instruct modes.', elem_classes=['add_scrollbar', 'monospace'])
|
||||
with gr.Row():
|
||||
shared.gradio['send_instruction_to_default'] = gr.Button('Send to default', elem_classes=['small-button'])
|
||||
shared.gradio['send_instruction_to_notebook'] = gr.Button('Send to notebook', elem_classes=['small-button'])
|
||||
@ -299,7 +299,10 @@ def create_event_handlers():
|
||||
|
||||
shared.gradio['delete_character'].click(lambda: gr.update(visible=True), None, gradio('character_deleter'))
|
||||
|
||||
shared.gradio['load_template'].click(chat.load_instruction_template, gradio('instruction_template'), gradio('instruction_template_str'))
|
||||
shared.gradio['load_template'].click(
|
||||
chat.load_instruction_template, gradio('instruction_template'), gradio('instruction_template_str')).then(
|
||||
lambda: "Select template to load...", None, gradio('instruction_template'))
|
||||
|
||||
shared.gradio['save_template'].click(
|
||||
lambda: 'My Template.yaml', None, gradio('save_filename')).then(
|
||||
lambda: 'instruction-templates/', None, gradio('save_root')).then(
|
||||
|
||||
@ -105,7 +105,7 @@ def get_available_instruction_templates():
|
||||
if os.path.exists(path):
|
||||
paths = (x for x in Path(path).iterdir() if x.suffix in ('.json', '.yaml', '.yml'))
|
||||
|
||||
return ['Custom'] + sorted(set((k.stem for k in paths)), key=natural_keys)
|
||||
return ['Select template to load...'] + sorted(set((k.stem for k in paths)), key=natural_keys)
|
||||
|
||||
|
||||
def get_available_extensions():
|
||||
|
||||
Loading…
Reference in New Issue
Block a user