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prompter.py
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prompter.py
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import ast
import time
import os
import traceback
# also supports imports from this file from other files
from enums import PromptType, gpt_token_mapping, anthropic_mapping, google_mapping, mistralai_mapping, groq_mapping, noop_prompt_type, unknown_prompt_type, user_prompt_for_fake_system_prompt0, template_prompt_type, empty_prompt_type # keep single line
from prompter_utils import get_use_chat_template
from utils import FakeTokenizer
from stopping import update_terminate_responses
non_hf_types = ['gpt4all_llama', 'llama', 'gptj']
prompt_type_to_model_name = {
noop_prompt_type: [
'EleutherAI/gpt-j-6B',
'EleutherAI/pythia-6.9b',
'EleutherAI/pythia-12b',
'EleutherAI/pythia-12b-deduped',
'EleutherAI/gpt-neox-20b',
'openlm-research/open_llama_7b_700bt_preview',
'decapoda-research/llama-7b-hf',
'decapoda-research/llama-13b-hf',
'decapoda-research/llama-30b-hf',
'decapoda-research/llama-65b-hf',
'facebook/mbart-large-50-many-to-many-mmt',
'philschmid/bart-large-cnn-samsum',
'philschmid/flan-t5-base-samsum',
'gpt2',
'distilgpt2',
'mosaicml/mpt-7b-storywriter',
'tiiuae/falcon-7b',
'tiiuae/falcon-40b',
'tiiuae/falcon-180B',
'meta-llama/Llama-2-7b',
'meta-llama/Llama-2-13b',
'meta-llama/Llama-2-70b',
'h2oai/h2ogpt-4096-llama2-7b',
'h2oai/h2ogpt-4096-llama2-13b',
'h2oai/h2ogpt-4096-llama2-70b',
'h2oai/h2ogpt-16k-codellama-7b',
'h2oai/h2ogpt-16k-codellama-13b',
'h2oai/h2ogpt-16k-codellama-34b',
'h2oai/h2ogpt-16k-codellama-7b-python',
'h2oai/h2ogpt-16k-codellama-13b-python',
'h2oai/h2ogpt-16k-codellama-34b-python',
'h2oai/h2ogpt-32k-codellama-34b-python',
'mistralai/Mistral-7B-v0.1',
'mistralai/Mixtral-8x7B-v0.1',
],
'gptj': ['gptj', 'gpt4all_llama'],
'prompt_answer': [
'h2oai/h2ogpt-gm-oasst1-en-1024-20b',
'h2oai/h2ogpt-gm-oasst1-en-1024-12b',
'h2oai/h2ogpt-gm-oasst1-multilang-1024-20b',
'h2oai/h2ogpt-gm-oasst1-multilang-2048-falcon-7b',
'h2oai/h2ogpt-gm-oasst1-multilang-2048-falcon-7b-v2',
'h2oai/h2ogpt-gm-oasst1-en-2048-falcon-7b-v3',
'h2oai/h2ogpt-gm-oasst1-en-2048-falcon-7b',
'h2oai/h2ogpt-gm-oasst1-en-2048-falcon-7b-v2',
'h2oai/h2ogpt-gm-oasst1-en-2048-falcon-40b-v1',
'h2oai/h2ogpt-gm-oasst1-en-2048-falcon-40b-v2',
'h2oai/h2ogpt-gm-oasst1-en-xgen-7b-8k',
'h2oai/h2ogpt-gm-oasst1-multilang-xgen-7b-8k',
'TheBloke/h2ogpt-gm-oasst1-en-2048-falcon-40b-v2-GPTQ',
],
'prompt_answer_openllama': [
'h2oai/h2ogpt-gm-oasst1-en-2048-open-llama-7b-preview-300bt',
'h2oai/h2ogpt-gm-oasst1-en-2048-open-llama-7b-preview-300bt-v2',
'h2oai/h2ogpt-gm-oasst1-en-2048-open-llama-7b-preview-700bt',
'h2oai/h2ogpt-gm-oasst1-en-2048-open-llama-7b',
'h2oai/h2ogpt-gm-oasst1-en-2048-open-llama-13b',
],
'instruct': ['TheBloke/llama-30b-supercot-SuperHOT-8K-fp16', 'TheBloke/Nous-Hermes-13B-GPTQ'],
# https://huggingface.co/TheBloke/llama-30b-supercot-SuperHOT-8K-fp16#prompting
'instruct_with_end': ['databricks/dolly-v2-12b'],
'quality': [],
'human_bot': [
'h2oai/h2ogpt-oasst1-512-12b',
'h2oai/h2ogpt-oasst1-512-20b',
'h2oai/h2ogpt-oig-oasst1-256-6_9b',
'h2oai/h2ogpt-oig-oasst1-512-6_9b',
'h2oai/h2ogpt-oig-oasst1-256-6.9b', # legacy
'h2oai/h2ogpt-oig-oasst1-512-6.9b', # legacy
'h2oai/h2ogpt-research-oasst1-512-30b',
'h2oai/h2ogpt-research-oasst1-llama-65b',
'h2oai/h2ogpt-oasst1-falcon-40b',
'h2oai/h2ogpt-oig-oasst1-falcon-40b',
'llmware/dragon-mistral-7b-v0', # https://huggingface.co/llmware/dragon-mistral-7b-v0
],
'dai_faq': [],
'summarize': [],
'simple_instruct': ['t5-small', 't5-large', 'google/flan-t5', 'google/flan-t5-xxl', 'google/flan-ul2'],
'instruct_vicuna': ['AlekseyKorshuk/vicuna-7b', 'TheBloke/stable-vicuna-13B-HF', 'junelee/wizard-vicuna-13b'],
'human_bot_orig': ['togethercomputer/GPT-NeoXT-Chat-Base-20B'],
"open_assistant": ['OpenAssistant/oasst-sft-7-llama-30b-xor', 'oasst-sft-7-llama-30b'],
"wizard_lm": ['ehartford/WizardLM-7B-Uncensored', 'ehartford/WizardLM-13B-Uncensored'],
"wizard_mega": ['openaccess-ai-collective/wizard-mega-13b'],
"instruct_simple": ['JosephusCheung/Guanaco'],
"wizard_vicuna": ['ehartford/Wizard-Vicuna-13B-Uncensored'],
# "wizard2": [],
"mptinstruct": ['mosaicml/mpt-30b-instruct', 'mosaicml/mpt-7b-instruct', 'mosaicml/mpt-30b-instruct'],
"mptchat": ['mosaicml/mpt-7b-chat', 'mosaicml/mpt-30b-chat', 'TheBloke/mpt-30B-chat-GGML',
'TheBloke/Nous-Hermes-2-Mixtral-8x7B-DPO-AWQ',
'TheBloke/dolphin-2.7-mixtral-8x7b-AWQ',
],
"orca2": ['TheBloke/Orca-2-13B-GGUF', 'microsoft/Orca-2-13b'],
"vicuna11": ['lmsys/vicuna-33b-v1.3',
'lmsys/vicuna-7b-v1.5',
'lmsys/vicuna-13b-v1.5', # https://huggingface.co/lmsys/vicuna-13b-v1.5/discussions/6/files
'NousResearch/Nous-Capybara-34B',
],
"vicuna11nosys": ['lmsys/vicuna-13b-v1.5-16k',
# system prompt doesn't work, no evidence was trained with it from model card.
],
"one_shot": ['lmsys/fastchat-t5-3b-v1.0', 'mistral-community/Mixtral-8x22B-v0.1'],
"falcon": ['tiiuae/falcon-40b-instruct', 'tiiuae/falcon-7b-instruct'],
"llama2": [
'meta-llama/Llama-2-7b-chat-hf',
'meta-llama/Llama-2-13b-chat-hf',
'meta-llama/Llama-2-34b-chat-hf',
'meta-llama/Llama-2-70b-chat-hf',
'h2oai/h2ogpt-oasst1-4096-llama2-7b',
'h2oai/h2ogpt-oasst1-4096-llama2-13b',
'h2oai/h2ogpt-oasst1-4096-llama2-70b',
# 'llama', # No longer go to llama2 prompt for any llama model, too many not llama2 and auto-detection is confusing then
'TheBloke/Llama-2-7b-Chat-GPTQ',
'TheBloke/Llama-2-7b-chat-fp16',
'TheBloke/Llama-2-13b-chat-fp16',
'TheBloke/Llama-2-70b-chat-fp16',
'h2oai/h2ogpt-4096-llama2-7b-chat',
'h2oai/h2ogpt-4096-llama2-13b-chat',
'h2oai/h2ogpt-4096-llama2-70b-chat',
'h2oai/h2ogpt-16k-codellama-7b-instruct',
'h2oai/h2ogpt-16k-codellama-13b-instruct',
'h2oai/h2ogpt-16k-codellama-34b-instruct',
'h2oai/h2ogpt-32k-codellama-34b-instruct',
'TheBloke/Llama-2-70B-chat-AWQ',
'h2oai/h2ogpt-4096-llama2-70b-chat-4bit',
'TheBloke/Llama-2-70B-chat-AWQ',
'TheBloke/Llama-2-13B-chat-AWQ',
'Yukang/LongAlpaca-70B', # or can be instruct
'TheBloke/Llama-2-7B-Chat-GGUF',
'namespace-Pt/activation-beacon-llama2-7b-chat',
'abacusai/Smaug-72B-v0.1',
],
"mistral": ['mistralai/Mistral-7B-Instruct-v0.1', 'TheBloke/Mistral-7B-Instruct-v0.1-GGUF',
'mistralai/Mistral-7B-Instruct-v0.2', 'TheBloke/Mistral-7B-Instruct-v0.2-GGUF',
],
"mixtral": ['mistralai/Mixtral-8x7B-Instruct-v0.1', 'TheBloke/Mixtral-8x7B-Instruct-v0.1-GGUF',
'TheBloke/Mixtral-8x7B-Instruct-v0.1-GPTQ', 'TheBloke/Mixtral-8x7B-Instruct-v0.1-AWQ',
'ybelkada/Mixtral-8x7B-Instruct-v0.1-AWQ'],
"mixtralnosys": [],
"zephyr": ['HuggingFaceH4/zephyr-7b-alpha', 'HuggingFaceH4/zephyr-7b-beta', 'TheBloke/zephyr-7B-beta-GGUF',
'TheBloke/zephyr-7B-beta-AWQ', 'zephyr-7b-beta.Q5_K_M.gguf'],
"beluga": ['stabilityai/StableBeluga2', 'psmathur/orca_mini_v3_7b'],
"wizard3nospace": ['WizardLM/WizardLM-13B-V1.2'],
"falcon_chat": ['tiiuae/falcon-180B-chat'],
"xwin": ['Xwin-LM/Xwin-LM-13B-V0.1', 'TheBloke/Xwin-LM-13B-V0.1-GPTQ', 'TheBloke/Xwin-LM-13B-v0.2-GPTQ',
'Xwin-LM/Xwin-LM-70B-V0.1'],
"xwincoder": ['Xwin-LM/XwinCoder-7B', 'Xwin-LM/XwinCoder-13B', 'Xwin-LM/XwinCoder-34B'],
"xwinmath": ["Xwin-LM/Xwin-Math-7B-V1.0", "Xwin-LM/Xwin-Math-70B-V1.0", "Xwin-LM/Xwin-Math-13B-V1.0"],
"mistrallite": ['amazon/MistralLite'],
"aquila": ['h2oai/h2ogpt-16k-aquilachat2-34b', 'BAAI/AquilaChat2-34B-16K', 'BAAI/AquilaChat2-34B-16k',
'BAAI/AquilaChat2-7B-16K'],
"aquila_legacy": ['BAAI/AquilaChat2-34B'],
"aquila_v1": ['BAAI/AquilaChat2-7B'],
"mistralgerman": ['TheBloke/em_german_leo_mistral-GPTQ'],
"deepseek_coder": ['deepseek-ai/deepseek-coder-1.3b-instruct',
'deepseek-ai/deepseek-coder-6.7b-instruct',
'deepseek-ai/deepseek-coder-33b-instruct',
],
"open_chat": ['openchat/openchat_3.5', 'TheBloke/openchat_3.5-GPTQ', 'TheBloke/openchat_3.5-GGUF',
'TheBloke/openchat_3.5-AWQ', 'TheBloke/openchat_3.5-16k-AWQ',
'openchat_3.5.Q5_K_M.gguf', 'NurtureAI/openchat_3.5-16k'],
"open_chat_correct": ['berkeley-nest/Starling-LM-7B-alpha', 'openchat/openchat-3.5-1210',
'openchat/openchat_3.5', 'openchat/openchat_v3.2_super',
'TheBloke/openchat-3.5-1210-AWQ',
], # can be any from open_chat list, by using this prompt
"open_chat_code": [], # can be any from open_chat list, by using this prompt
"open_chat_math": [], # can be any from open_chat list, by using this prompt
"jais": ['core42/jais-30b-chat-v1', 'core42/jais-13b-chat'],
"yi": ['01-ai/Yi-34B-Chat', 'TheBloke/Yi-34B-Chat-AWQ'],
"docsgpt": ['Arc53/docsgpt-7b-mistral'],
"orion": ['OrionStarAI/Orion-14B-Chat', 'OrionStarAI/Orion-14B-LongChat', 'OrionStarAI/Orion-14B-Chat-RAG'],
"sciphi": ['SciPhi/SciPhi-Self-RAG-Mistral-7B-32k'],
# could be plain, but default is correct prompt_type for default TheBloke model ggml-wizardLM-7B.q4_2.bin
"beacon": [],
"beacon2": [],
# endpoint handles prompting, but we need chat history generation in some sensible way
"llava": ['liuhaotian/llava-v1.6-34b',
'liuhaotian/llava-v1.6-mistral-7b',
'liuhaotian/llava-v1.6-vicuna-13b',
'liuhaotian/llava-v1.6-vicuna-7b',
'liuhaotian/llava-v1.5-13b',
'liuhaotian/llava-v1.5-7b',
'liuhaotian/llava-v1.6-34b',
'liuhaotian/llava-v1.6-vicuna-13b',
'liuhaotian/llava-v1.6-vicuna-7b',
'liuhaotian/llava-v1.6-mistral-7b',
'liuhaotian/llava-v1.5-7b',
'liuhaotian/llava-v1.5-13b',
'NousResearch/Nous-Hermes-2-Vision', # different worker, that handles prompting itself too
],
"danube": ['h2oai/h2o-danube-1.8b-chat'],
"gemma": ['gg-hf/gemma-2b-it', 'gg-hf/gemma-7b-it', 'google/gemma-2b-it', 'google/gemma-7b-it'],
"qwen": ['Qwen/Qwen1.5-7B-Chat-GPTQ-Int8',
'Qwen/Qwen1.5-7B-Chat-GPTQ-Int4',
'Qwen/Qwen1.5-7B-Chat-AWQ',
'Qwen/Qwen1.5-7B-Chat',
'Qwen/Qwen1.5-72B-Chat-GPTQ-Int8',
'Qwen/Qwen1.5-72B-Chat-GPTQ-Int4',
'Qwen/Qwen1.5-72B-Chat-AWQ',
'Qwen/Qwen1.5-72B-Chat',
'Qwen/Qwen1.5-4B-Chat-GPTQ-Int8',
'Qwen/Qwen1.5-4B-Chat-GPTQ-Int4',
'Qwen/Qwen1.5-4B-Chat-AWQ',
'Qwen/Qwen1.5-4B-Chat',
'Qwen/Qwen1.5-14B-Chat-GPTQ-Int8',
'Qwen/Qwen1.5-14B-Chat-GPTQ-Int4',
'Qwen/Qwen1.5-14B-Chat-AWQ',
'Qwen/Qwen1.5-14B-Chat',
'Qwen/Qwen1.5-1.8B-Chat-GPTQ-Int8',
'Qwen/Qwen1.5-1.8B-Chat-GPTQ-Int4',
'Qwen/Qwen1.5-1.8B-Chat-AWQ',
'Qwen/Qwen1.5-1.8B-Chat',
'Qwen/Qwen1.5-0.5B-Chat-GPTQ-Int8',
'Qwen/Qwen1.5-0.5B-Chat-GPTQ-Int4',
'Qwen/Qwen1.5-0.5B-Chat-AWQ',
'Qwen/Qwen1.5-0.5B-Chat',
'Qwen/Qwen1.5-72B-Chat-GGUF',
'Qwen/Qwen1.5-14B-Chat-GGUF',
'Qwen/Qwen1.5-7B-Chat-GGUF',
'Qwen/Qwen1.5-4B-Chat-GGUF',
'Qwen/Qwen1.5-1.8B-Chat-GGUF',
'Qwen/Qwen1.5-0.5B-Chat-GGUF',
],
"sealion": ['aisingapore/sea-lion-7b-instruct'],
"aya": ["CohereForAI/aya-101"],
"idefics2": ["HuggingFaceM4/idefics2-8b-chatty", "HuggingFaceM4/idefics2-8b-chat"],
# don't actually add, else use_chat_template wouldn't function right for LLM mode
# 'cohere_grounded': ["CohereForAI/c4ai-command-r-v01", "CohereForAI/c4ai-command-r-plus"],
}
anthropic_gpts = sorted(anthropic_mapping.keys())
prompt_type_to_model_name['anthropic'] = anthropic_gpts
google_gpts = sorted(google_mapping.keys())
prompt_type_to_model_name['google'] = google_gpts
mistralai_gpts = sorted(mistralai_mapping.keys())
prompt_type_to_model_name['mistralai'] = mistralai_gpts
groq_gpts = sorted(groq_mapping.keys())
prompt_type_to_model_name['groq'] = groq_gpts
model_names_curated_big = ['Yukang/LongAlpaca-70B',
'lmsys/vicuna-13b-v1.5-16k',
'h2oai/h2ogpt-32k-codellama-34b-instruct']
model_names_curated = ['TheBloke/Xwin-LM-13B-V0.1-GPTQ',
'TheBloke/Llama-2-7B-Chat-GGUF',
'HuggingFaceH4/zephyr-7b-beta',
'TheBloke/zephyr-7B-beta-GGUF',
'TheBloke/zephyr-7B-beta-AWQ'] + model_names_curated_big
openai_gpts = list(gpt_token_mapping.keys())
prompt_type_to_model_name.update({
"openai": ["text-davinci-003", "text-curie-001", "text-babbage-001", "text-ada-001"],
"openai_chat": openai_gpts,
})
model_names_curated += ['gpt-3.5-turbo']
inv_prompt_type_to_model_name = {v.strip(): k for k, l in prompt_type_to_model_name.items() for v in l}
inv_prompt_type_to_model_lower = {v.strip().lower(): k for k, l in prompt_type_to_model_name.items() for v in l}
prompt_types_strings = []
for p in PromptType:
prompt_types_strings.extend([p.name])
prompt_types = []
for p in PromptType:
prompt_types.extend([p.name, p.value, str(p.value)])
def get_prompt(prompt_type, prompt_dict, context, reduced, making_context, return_dict=False,
system_prompt=None, histi=-1):
prompt_dict_error = ''
generates_leading_space = False
can_handle_system_prompt = False
if prompt_type == PromptType.custom.name and not isinstance(prompt_dict, dict):
try:
prompt_dict = ast.literal_eval(prompt_dict)
except BaseException as e:
prompt_dict_error = str(e)
if prompt_dict_error:
promptA = None
promptB = None
PreInstruct = None
PreInput = ''
PreResponse = ''
terminate_response = None
chat_sep = ''
chat_turn_sep = ''
humanstr = ''
botstr = ''
generates_leading_space = False
elif prompt_type in [PromptType.custom.value, str(PromptType.custom.value),
PromptType.custom.name]:
promptA = prompt_dict.get('promptA', '')
promptB = prompt_dict.get('promptB', '')
PreInstruct = prompt_dict.get('PreInstruct', '')
PreInput = prompt_dict.get('PreInput', '')
PreResponse = prompt_dict.get('PreResponse', '')
terminate_response = prompt_dict.get('terminate_response', None)
chat_sep = prompt_dict.get('chat_sep', '\n')
chat_turn_sep = prompt_dict.get('chat_turn_sep', '\n')
humanstr = prompt_dict.get('humanstr', '')
botstr = prompt_dict.get('botstr', '')
elif prompt_type in [PromptType.plain.value, str(PromptType.plain.value),
PromptType.plain.name]:
promptA = promptB = PreInstruct = PreInput = PreResponse = None
terminate_response = []
chat_sep = chat_turn_sep = '\n'
# plain should have None for human/bot, so nothing truncated out, not '' that would truncate after first token
humanstr = None
botstr = None
elif prompt_type in [PromptType.unknown.value, str(PromptType.unknown.value),
PromptType.unknown.name]:
promptA = promptB = PreInstruct = PreInput = PreResponse = None
terminate_response = []
chat_sep = chat_turn_sep = '\n'
# plain should have None for human/bot, so nothing truncated out, not '' that would truncate after first token
humanstr = None
botstr = None
elif prompt_type in [PromptType.template.value, str(PromptType.template.value),
PromptType.template.name]:
promptA = promptB = PreInstruct = PreInput = PreResponse = None
terminate_response = []
chat_sep = chat_turn_sep = '\n'
# plain should have None for human/bot, so nothing truncated out, not '' that would truncate after first token
humanstr = None
botstr = None
elif prompt_type in [PromptType.llava.value, str(PromptType.llava.value),
PromptType.llava.name]:
promptA = promptB = PreInstruct = PreInput = PreResponse = None
terminate_response = []
chat_turn_sep = '\n'
chat_sep = ''
# plain should have None for human/bot, so nothing truncated out, not '' that would truncate after first token
humanstr = None
botstr = None
elif prompt_type == 'simple_instruct':
promptA = promptB = PreInstruct = PreInput = PreResponse = None
terminate_response = []
chat_turn_sep = chat_sep = '\n'
humanstr = None
botstr = None
elif prompt_type in [PromptType.instruct.value, str(PromptType.instruct.value),
PromptType.instruct.name] + [PromptType.instruct_with_end.value,
str(PromptType.instruct_with_end.value),
PromptType.instruct_with_end.name]:
promptA = 'Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n' if not reduced else ''
promptB = 'Below is an instruction that describes a task. Write a response that appropriately completes the request.\n' if not reduced else ''
PreInstruct = """
### Instruction:
"""
PreInput = """
### Input:
"""
PreResponse = """
### Response:
"""
if prompt_type in [PromptType.instruct_with_end.value, str(PromptType.instruct_with_end.value),
PromptType.instruct_with_end.name]:
terminate_response = ['### End']
else:
terminate_response = None
chat_turn_sep = chat_sep = '\n'
humanstr = PreInstruct
botstr = PreResponse
elif prompt_type in [PromptType.quality.value, str(PromptType.quality.value),
PromptType.quality.name]:
promptA = 'Write a detailed high-quality, accurate, fair, Response with about 100 words by following the Instruction as applied on the Input.\n' if not reduced else ''
promptB = 'Write a detailed high-quality, accurate, fair, Response with about 100 words by following the Instruction.\n' if not reduced else ''
PreInstruct = """
### Instruction:
"""
PreInput = """
### Input:
"""
PreResponse = """
### Response:
"""
terminate_response = None
chat_turn_sep = chat_sep = '\n'
humanstr = PreInstruct # first thing human says
botstr = PreResponse # first thing bot says
elif prompt_type in [PromptType.human_bot.value, str(PromptType.human_bot.value),
PromptType.human_bot.name] + [PromptType.human_bot_orig.value,
str(PromptType.human_bot_orig.value),
PromptType.human_bot_orig.name]:
human = '<human>:'
bot = "<bot>:"
if reduced or context or prompt_type in [PromptType.human_bot.value, str(PromptType.human_bot.value),
PromptType.human_bot.name]:
preprompt = ''
else:
cur_date = time.strftime('%Y-%m-%d')
cur_time = time.strftime('%H:%M:%S %p %Z')
PRE_PROMPT = """\
Current Date: {}
Current Time: {}
"""
preprompt = PRE_PROMPT.format(cur_date, cur_time)
start = ''
promptB = promptA = '%s%s' % (preprompt, start)
PreInstruct = human + ' '
PreInput = None
if making_context:
# when making context, want it to appear as-if LLM generated, which starts with space after :
PreResponse = bot + ' '
else:
# normally LLM adds space after this, because was how trained.
# if add space here, non-unique tokenization will often make LLM produce wrong output
PreResponse = bot
terminate_response = ['\n' + human, '\n' + bot, human, bot, PreResponse]
chat_turn_sep = chat_sep = '\n'
humanstr = human # tag before human talks
botstr = bot # tag before bot talks
generates_leading_space = True
elif prompt_type in [PromptType.dai_faq.value, str(PromptType.dai_faq.value),
PromptType.dai_faq.name]:
promptA = ''
promptB = 'Answer the following Driverless AI question.\n'
PreInstruct = """
### Driverless AI frequently asked question:
"""
PreInput = None
PreResponse = """
### Driverless AI documentation answer:
"""
terminate_response = ['\n\n']
chat_turn_sep = chat_sep = terminate_response
humanstr = PreInstruct
botstr = PreResponse
elif prompt_type in [PromptType.summarize.value, str(PromptType.summarize.value),
PromptType.summarize.name]:
promptA = promptB = PreInput = ''
PreInstruct = '## Main Text\n\n'
PreResponse = '\n\n## Summary\n\n'
terminate_response = None
chat_turn_sep = chat_sep = '\n'
humanstr = PreInstruct
botstr = PreResponse
elif prompt_type in [PromptType.instruct_vicuna.value, str(PromptType.instruct_vicuna.value),
PromptType.instruct_vicuna.name]:
can_handle_system_prompt = True
if system_prompt in [None, 'None', 'auto']:
system_prompt = "A chat between a curious human and an artificial intelligence assistant. " \
"The assistant gives helpful, detailed, and polite answers to the human's questions."
promptA = promptB = system_prompt if not reduced else ''
PreInstruct = """
### Human:
"""
PreInput = None
PreResponse = """
### Assistant:
"""
# but only allow terminate after prompt is found correctly, else can't terminate
terminate_response = ['### Human:', '### Human: ', ' ### Human:', '### Assistant:']
chat_turn_sep = chat_sep = '\n'
humanstr = PreInstruct
botstr = PreResponse
elif prompt_type in [PromptType.prompt_answer.value, str(PromptType.prompt_answer.value),
PromptType.prompt_answer.name]:
preprompt = ''
prompt_tokens = "<|prompt|>"
answer_tokens = "<|answer|>"
start = ''
promptB = promptA = '%s%s' % (preprompt, start)
PreInstruct = prompt_tokens
PreInput = None
PreResponse = answer_tokens
eos = '<|endoftext|>' # neox eos
humanstr = prompt_tokens
botstr = answer_tokens
terminate_response = [humanstr, PreResponse, eos]
chat_sep = eos
chat_turn_sep = eos
elif prompt_type in [PromptType.prompt_answer_openllama.value, str(PromptType.prompt_answer_openllama.value),
PromptType.prompt_answer_openllama.name]:
preprompt = ''
prompt_tokens = "<|prompt|>"
answer_tokens = "<|answer|>"
start = ''
promptB = promptA = '%s%s' % (preprompt, start)
PreInstruct = prompt_tokens
PreInput = None
PreResponse = answer_tokens
eos = '</s>' # llama eos
humanstr = prompt_tokens
botstr = answer_tokens
terminate_response = [humanstr, PreResponse, eos]
chat_sep = eos
chat_turn_sep = eos
elif prompt_type in [PromptType.danube.value, str(PromptType.danube.value),
PromptType.danube.name]:
can_handle_system_prompt = False # so uses pre-conversation
prompt_tokens = "<|prompt|>"
answer_tokens = "<|answer|>"
if system_prompt in [None, 'None', 'auto']:
system_prompt = ""
promptA = promptB = ''
PreInstruct = prompt_tokens
PreInput = None
PreResponse = answer_tokens
eos = '</s>' # llama eos
humanstr = prompt_tokens
botstr = answer_tokens
terminate_response = [humanstr, PreResponse, eos]
chat_sep = eos
chat_turn_sep = eos
elif prompt_type in [PromptType.open_assistant.value, str(PromptType.open_assistant.value),
PromptType.open_assistant.name]:
# From added_tokens.json
preprompt = ''
prompt_tokens = "<|prompter|>"
answer_tokens = "<|assistant|>"
start = ''
promptB = promptA = '%s%s' % (preprompt, start)
PreInstruct = prompt_tokens
PreInput = None
PreResponse = answer_tokens
pend = "<|prefix_end|>"
eos = "</s>"
humanstr = prompt_tokens
botstr = answer_tokens
terminate_response = [humanstr, PreResponse, pend, eos]
chat_turn_sep = chat_sep = eos
elif prompt_type in [PromptType.wizard_lm.value, str(PromptType.wizard_lm.value),
PromptType.wizard_lm.name]:
# https://github.com/ehartford/WizardLM/blob/main/src/train_freeform.py
preprompt = ''
start = ''
promptB = promptA = '%s%s' % (preprompt, start)
PreInstruct = ""
PreInput = None
PreResponse = "\n\n### Response\n"
eos = "</s>"
terminate_response = [PreResponse, eos]
chat_turn_sep = chat_sep = eos
humanstr = promptA
botstr = PreResponse
elif prompt_type in [PromptType.wizard_mega.value, str(PromptType.wizard_mega.value),
PromptType.wizard_mega.name]:
preprompt = ''
start = ''
promptB = promptA = '%s%s' % (preprompt, start)
PreInstruct = """
### Instruction:
"""
PreInput = None
PreResponse = """
### Assistant:
"""
terminate_response = [PreResponse]
chat_turn_sep = chat_sep = '\n'
humanstr = PreInstruct
botstr = PreResponse
elif prompt_type in [PromptType.instruct_vicuna2.value, str(PromptType.instruct_vicuna2.value),
PromptType.instruct_vicuna2.name]:
promptA = promptB = "" if not reduced else ''
PreInstruct = """
HUMAN:
"""
PreInput = None
PreResponse = """
ASSISTANT:
"""
terminate_response = [
'HUMAN:'] # but only allow terminate after prompt is found correctly, else can't terminate
chat_turn_sep = chat_sep = '\n'
humanstr = PreInstruct
botstr = PreResponse
elif prompt_type in [PromptType.instruct_vicuna3.value, str(PromptType.instruct_vicuna3.value),
PromptType.instruct_vicuna3.name]:
promptA = promptB = "" if not reduced else ''
PreInstruct = """
### User:
"""
PreInput = None
PreResponse = """
### Assistant:
"""
terminate_response = [
'### User:'] # but only allow terminate after prompt is found correctly, else can't terminate
chat_turn_sep = chat_sep = '\n'
humanstr = PreInstruct
botstr = PreResponse
elif prompt_type in [PromptType.wizard2.value, str(PromptType.wizard2.value),
PromptType.wizard2.name]:
can_handle_system_prompt = True
# https://huggingface.co/TheBloke/WizardLM-7B-uncensored-GGML
if system_prompt in [None, 'None', 'auto']:
system_prompt = "Below is an instruction that describes a task. Write a response that appropriately completes the request."
preprompt = """%s""" % system_prompt if not reduced else ''
start = ''
promptB = promptA = '%s%s' % (preprompt, start)
PreInstruct = """
### Instruction:
"""
PreInput = None
PreResponse = """
### Response:
"""
terminate_response = [PreResponse]
chat_turn_sep = chat_sep = '\n'
humanstr = PreInstruct
botstr = PreResponse
elif prompt_type in [PromptType.wizard3.value, str(PromptType.wizard3.value),
PromptType.wizard3.name]:
# https://huggingface.co/TheBloke/wizardLM-13B-1.0-GGML
can_handle_system_prompt = True
if system_prompt in [None, 'None', 'auto']:
system_prompt = "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions."
preprompt = """%s""" % system_prompt if not reduced else ''
start = ''
promptB = promptA = '%s%s' % (preprompt, start)
PreInstruct = """USER: """
PreInput = None
PreResponse = """ASSISTANT: """
terminate_response = [PreResponse]
chat_turn_sep = chat_sep = '\n'
humanstr = PreInstruct
botstr = PreResponse
elif prompt_type in [PromptType.wizard_vicuna.value, str(PromptType.wizard_vicuna.value),
PromptType.wizard_vicuna.name]:
preprompt = ''
start = ''
promptB = promptA = '%s%s' % (preprompt, start)
PreInstruct = """USER: """
PreInput = None
PreResponse = """ASSISTANT: """
terminate_response = [PreResponse]
chat_turn_sep = chat_sep = '\n'
humanstr = PreInstruct
botstr = PreResponse
elif prompt_type in [PromptType.instruct_simple.value, str(PromptType.instruct_simple.value),
PromptType.instruct_simple.name]:
promptB = promptA = '' if not reduced else ''
PreInstruct = """
### Instruction:
"""
PreInput = """
### Input:
"""
PreResponse = """
### Response:
"""
terminate_response = None
chat_turn_sep = chat_sep = '\n'
humanstr = PreInstruct
botstr = PreResponse
elif prompt_type in [PromptType.openai.value, str(PromptType.openai.value),
PromptType.openai.name]:
can_handle_system_prompt = True
if system_prompt in [None, 'None', 'auto']:
system_prompt = "The following is a conversation with an AI assistant. The assistant is helpful, creative, clever, and very friendly."
preprompt = """%s""" % system_prompt if not reduced else ''
start = ''
promptB = promptA = '%s%s' % (preprompt, start)
PreInstruct = "\nHuman: "
PreInput = None
PreResponse = "\nAI:"
terminate_response = [PreResponse] + [" Human:", " AI:"]
chat_turn_sep = chat_sep = '\n'
humanstr = PreInstruct
botstr = PreResponse
elif prompt_type in [PromptType.gptj.value, str(PromptType.gptj.value),
PromptType.gptj.name]:
preprompt = "### Instruction:\n The prompt below is a question to answer, a task to complete, or a conversation to respond to; decide which and write an appropriate response." if not reduced else ''
start = ''
promptB = promptA = '%s%s' % (preprompt, start)
PreInstruct = "\n### Prompt: "
PreInput = None
PreResponse = "\n### Response: "
terminate_response = [PreResponse] + ["Prompt:", "Response:"]
chat_turn_sep = chat_sep = '\n'
humanstr = PreInstruct
botstr = PreResponse
elif prompt_type in [PromptType.openai_chat.value, str(PromptType.openai_chat.value),
PromptType.openai_chat.name] or \
prompt_type in [PromptType.anthropic.value, str(PromptType.anthropic.value),
PromptType.anthropic.name] or \
prompt_type in [PromptType.google.value, str(PromptType.google.value),
PromptType.google.name] or \
prompt_type in [PromptType.mistralai.value, str(PromptType.mistralai.value),
PromptType.mistralai.name] or \
prompt_type in [PromptType.groq.value, str(PromptType.groq.value),
PromptType.groq.name]:
can_handle_system_prompt = True # handled via special messages/arguments not part of prompt
# mistral safe_mode=True is same as this system prompt:
# Always assist with care, respect, and truth. Respond with utmost utility yet securely. Avoid harmful, unethical, prejudiced, or negative content. Ensure replies promote fairness and positivity.
# prompting and termination all handled by endpoint
preprompt = """"""
start = ''
promptB = promptA = '%s%s' % (preprompt, start)
PreInstruct = ""
PreInput = None
PreResponse = ""
terminate_response = []
chat_sep = ''
chat_turn_sep = '\n'
humanstr = None
botstr = None
if prompt_type in [PromptType.google.value, str(PromptType.google.value),
PromptType.google.name] and system_prompt == 'auto':
# google throws safety/harassment errors if don't tell the model it's helpful, even for asking "what is 1+1?"
# so give basic prompt if auto, the current default, so part of pre-conversation always
system_prompt = 'I am a helpful assistant. I will accurately answer all your questions.'
elif prompt_type in [PromptType.vicuna11.value, str(PromptType.vicuna11.value),
PromptType.vicuna11.name] or \
prompt_type in [PromptType.vicuna11nosys.value, str(PromptType.vicuna11nosys.value),
PromptType.vicuna11nosys.name]:
can_handle_system_prompt = prompt_type in [PromptType.vicuna11.value,
str(PromptType.vicuna11.value),
PromptType.vicuna11.name]
if system_prompt in [None, 'None', 'auto']:
system_prompt = "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions."
if not can_handle_system_prompt:
# totally remove system prompt stuff, maybe not always done for every model like this
preprompt = ""
else:
preprompt = """%s """ % system_prompt if not reduced else ''
start = ''
promptB = promptA = '%s%s' % (preprompt, start)
eos = '</s>'
PreInstruct = """USER: """
PreInput = None
PreResponse = """ASSISTANT:"""
terminate_response = [PreResponse, eos]
chat_sep = ' '
chat_turn_sep = eos
humanstr = PreInstruct
botstr = PreResponse
if making_context:
# when making context, want it to appear as-if LLM generated, which starts with space after :
PreResponse = PreResponse + ' '
else:
# normally LLM adds space after this, because was how trained.
# if add space here, non-unique tokenization will often make LLM produce wrong output
PreResponse = PreResponse
elif prompt_type in [PromptType.mptinstruct.value, str(PromptType.mptinstruct.value),
PromptType.mptinstruct.name]:
can_handle_system_prompt = True
# https://huggingface.co/mosaicml/mpt-30b-instruct#formatting
if system_prompt in [None, 'None', 'auto']:
system_prompt = "Below is an instruction that describes a task. Write a response that appropriately completes the request."
promptA = promptB = '%s\n' % system_prompt if not reduced else ''
PreInstruct = """
### Instruction
"""
PreInput = """
### Input
"""
PreResponse = """
### Response
"""
terminate_response = None
chat_turn_sep = chat_sep = '\n'
humanstr = PreInstruct
botstr = PreResponse
elif prompt_type in [PromptType.mptchat.value, str(PromptType.mptchat.value),
PromptType.mptchat.name]:
can_handle_system_prompt = True
# https://huggingface.co/TheBloke/mpt-30B-chat-GGML#prompt-template
if system_prompt in [None, 'None', 'auto']:
system_prompt = "A conversation between a user and an LLM-based AI assistant. The assistant gives helpful and honest answers."
promptA = promptB = """<|im_start|>system\n%s\n<|im_end|>""" % system_prompt if not reduced else ''
PreInstruct = """<|im_start|>user
"""
PreInput = None
PreResponse = """<|im_end|><|im_start|>assistant
"""
terminate_response = ['<|im_end|>']
chat_sep = ''
chat_turn_sep = '<|im_end|>'
humanstr = PreInstruct
botstr = PreResponse
elif prompt_type in [PromptType.orca2.value, str(PromptType.orca2.value),
PromptType.orca2.name]:
can_handle_system_prompt = True
# https://huggingface.co/microsoft/Orca-2-13b#getting-started-with-orca-2
if system_prompt in [None, 'None', 'auto']:
system_prompt = "You are Orca, an AI language model created by Microsoft. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior."
promptA = promptB = """<|im_start|>system\n%s\n<|im_end|>""" % system_prompt if not reduced else ''
PreInstruct = """<|im_start|>user
"""
PreInput = None
PreResponse = """<|im_end|><|im_start|>assistant
"""
terminate_response = ['<|im_end|>']
chat_sep = ''
chat_turn_sep = '<|im_end|>'
humanstr = PreInstruct
botstr = PreResponse
elif prompt_type in [PromptType.falcon.value, str(PromptType.falcon.value),
PromptType.falcon.name]:
promptA = promptB = "" if not reduced else ''
PreInstruct = """User: """
PreInput = None
PreResponse = """Assistant:"""
terminate_response = ['\nUser', "<|endoftext|>"]
chat_sep = '\n\n'
chat_turn_sep = '\n\n'
humanstr = PreInstruct
botstr = PreResponse
if making_context:
# when making context, want it to appear as-if LLM generated, which starts with space after :
PreResponse = 'Assistant: '
else:
# normally LLM adds space after this, because was how trained.
# if add space here, non-unique tokenization will often make LLM produce wrong output
PreResponse = PreResponse
# generates_leading_space = True
elif prompt_type in [PromptType.guanaco.value, str(PromptType.guanaco.value),
PromptType.guanaco.name]:
# https://huggingface.co/TheBloke/guanaco-65B-GPTQ
promptA = promptB = "" if not reduced else ''
PreInstruct = """### Human: """
PreInput = None
PreResponse = """### Assistant:"""
terminate_response = [
'### Human:'] # but only allow terminate after prompt is found correctly, else can't terminate
chat_turn_sep = chat_sep = '\n'
humanstr = PreInstruct
botstr = PreResponse
elif prompt_type in [PromptType.llama2.value, str(PromptType.llama2.value),
PromptType.llama2.name]:
can_handle_system_prompt = True
if system_prompt in [None, 'None', 'auto']:
# automatic
system_prompt = """You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information."""
# too much safety, hurts accuracy
if system_prompt:
sys_msg = """<<SYS>>\n%s\n<</SYS>>\n\n""" % system_prompt
else:
sys_msg = ''
if not reduced:
promptA = promptB = ''
else:
promptA = promptB = ''
PreInput = None
PreInstruct = "<s>[INST] "
if making_context and histi == 0 or not making_context and not reduced:
PreInstruct += sys_msg
PreResponse = "[/INST]"
terminate_response = ["[INST]", "</s>"]
chat_sep = ' '
chat_turn_sep = ' </s>'
humanstr = '[INST]'
botstr = '[/INST]'
if making_context:
PreResponse += " "
elif prompt_type in [PromptType.beluga.value, str(PromptType.beluga.value),
PromptType.beluga.name]:
can_handle_system_prompt = True
if system_prompt in [None, 'None', 'auto']:
# automatic
system_prompt = "You are Stable Beluga, an AI that follows instructions extremely well. Help as much as you can. Remember, be safe, and don't do anything illegal."
if system_prompt:
sys_msg = """### System:\n%s\n\n""" % system_prompt
else:
sys_msg = ''
if sys_msg and not reduced:
# too much safety, hurts accuracy
promptA = promptB = sys_msg
else:
promptA = promptB = ''
PreInput = None
PreInstruct = "### User:\n"
PreResponse = "\n### Assistant:\n"
terminate_response = ['### Assistant:', "</s>"]
chat_sep = '\n'
chat_turn_sep = '\n\n'
humanstr = '### User:'
botstr = '### Assistant:'
elif prompt_type in [PromptType.wizard3nospace.value, str(PromptType.wizard3nospace.value),
PromptType.wizard3nospace.name]:
# https://huggingface.co/WizardLM/WizardLM-13B-V1.2/discussions/3
preprompt = """A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.""" if not reduced else ''
start = ''
promptB = promptA = '%s%s' % (preprompt, start)
PreInstruct = """USER: """
PreInput = None
PreResponse = """ASSISTANT:"""
terminate_response = [PreResponse]
chat_turn_sep = chat_sep = '\n'
humanstr = PreInstruct
botstr = PreResponse
elif prompt_type in [PromptType.one_shot.value, str(PromptType.one_shot.value),
PromptType.one_shot.name]:
promptA = promptB = """A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.
### Human: Got any creative ideas for a 10 year old’s birthday?
### Assistant: Of course! Here are some creative ideas for a 10-year-old's birthday party:
1. Treasure Hunt: Organize a treasure hunt in your backyard or nearby park. Create clues and riddles for the kids to solve, leading them to hidden treasures and surprises.
2. Science Party: Plan a science-themed party where kids can engage in fun and interactive experiments. You can set up different stations with activities like making slime, erupting volcanoes, or creating simple chemical reactions.
3. Outdoor Movie Night: Set up a backyard movie night with a projector and a large screen or white sheet. Create a cozy seating area with blankets and pillows, and serve popcorn and snacks while the kids enjoy a favorite movie under the stars.
4. DIY Crafts Party: Arrange a craft party where kids can unleash their creativity. Provide a variety of craft supplies like beads, paints, and fabrics, and let them create their own unique masterpieces to take home as party favors.
5. Sports Olympics: Host a mini Olympics event with various sports and games. Set up different stations for activities like sack races, relay races, basketball shooting, and obstacle courses. Give out medals or certificates to the participants.
6. Cooking Party: Have a cooking-themed party where the kids can prepare their own mini pizzas, cupcakes, or cookies. Provide toppings, frosting, and decorating supplies, and let them get hands-on in the kitchen.
7. Superhero Training Camp: Create a superhero-themed party where the kids can engage in fun training activities. Set up an obstacle course, have them design their own superhero capes or masks, and organize superhero-themed games and challenges.
8. Outdoor Adventure: Plan an outdoor adventure party at a local park or nature reserve. Arrange activities like hiking, nature scavenger hunts, or a picnic with games. Encourage exploration and appreciation for the outdoors.
Remember to tailor the activities to the birthday child's interests and preferences. Have a great celebration!""" if not reduced else ''
PreInstruct = """
### Human: """
PreInput = None
PreResponse = """
### Assistant:"""
# but only allow terminate after prompt is found correctly, else can't terminate
terminate_response = ['### Human:', '### Human: ', ' ### Human:', '### Assistant:']
chat_turn_sep = chat_sep = '\n'
humanstr = PreInstruct
botstr = PreResponse
elif prompt_type in [PromptType.falcon_chat.value, str(PromptType.falcon_chat.value),
PromptType.falcon_chat.name]:
can_handle_system_prompt = True
if system_prompt in [None, 'None', 'auto']:
# automatic
system_prompt = "You are an intelligent and helpful assistant."
if system_prompt:
sys_msg = "System: %s\n" % system_prompt
else:
sys_msg = ''
if sys_msg and not reduced:
# too much safety, hurts accuracy
promptA = promptB = sys_msg
else:
promptA = promptB = ''
PreInstruct = """User: """
PreInput = None
PreResponse = """Falcon:"""
terminate_response = ['\nUser:', "<|endoftext|>", " User:", "###"]
chat_sep = '\n'
chat_turn_sep = '\n'
humanstr = PreInstruct
botstr = PreResponse
if making_context:
# when making context, want it to appear as-if LLM generated, which starts with space after :
PreResponse = botstr + ' '