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ramnathv/my-awesome-model | ramnathv | 2023-12-29T00:37:56Z | 0 | 0 | sklearn | [
"sklearn",
"skops",
"tabular-classification",
"license:mit",
"region:us"
] | tabular-classification | 2023-12-29T00:37:55Z | ---
license: mit
library_name: sklearn
tags:
- sklearn
- skops
- tabular-classification
model_format: pickle
model_file: example.pkl
widget:
- structuredData:
area error:
- 30.29
- 96.05
- 48.31
compactness error:
- 0.01911
- 0.01652
- 0.01484
concave points error:
- 0.01037
- 0.0137
- 0.01093
concavity error:
- 0.02701
- 0.02269
- 0.02813
fractal dimension error:
- 0.003586
- 0.001698
- 0.002461
mean area:
- 481.9
- 1130.0
- 748.9
mean compactness:
- 0.1058
- 0.1029
- 0.1223
mean concave points:
- 0.03821
- 0.07951
- 0.08087
mean concavity:
- 0.08005
- 0.108
- 0.1466
mean fractal dimension:
- 0.06373
- 0.05461
- 0.05796
mean perimeter:
- 81.09
- 123.6
- 101.7
mean radius:
- 12.47
- 18.94
- 15.46
mean smoothness:
- 0.09965
- 0.09009
- 0.1092
mean symmetry:
- 0.1925
- 0.1582
- 0.1931
mean texture:
- 18.6
- 21.31
- 19.48
perimeter error:
- 2.497
- 5.486
- 3.094
radius error:
- 0.3961
- 0.7888
- 0.4743
smoothness error:
- 0.006953
- 0.004444
- 0.00624
symmetry error:
- 0.01782
- 0.01386
- 0.01397
texture error:
- 1.044
- 0.7975
- 0.7859
worst area:
- 677.9
- 1866.0
- 1156.0
worst compactness:
- 0.2378
- 0.2336
- 0.2394
worst concave points:
- 0.1015
- 0.1789
- 0.1514
worst concavity:
- 0.2671
- 0.2687
- 0.3791
worst fractal dimension:
- 0.0875
- 0.06589
- 0.08019
worst perimeter:
- 96.05
- 165.9
- 124.9
worst radius:
- 14.97
- 24.86
- 19.26
worst smoothness:
- 0.1426
- 0.1193
- 0.1546
worst symmetry:
- 0.3014
- 0.2551
- 0.2837
worst texture:
- 24.64
- 26.58
- 26.0
---
# Model description
[More Information Needed]
## Intended uses & limitations
[More Information Needed]
## Training Procedure
[More Information Needed]
### Hyperparameters
<details>
<summary> Click to expand </summary>
| Hyperparameter | Value |
|--------------------------|---------|
| ccp_alpha | 0.0 |
| class_weight | |
| criterion | gini |
| max_depth | |
| max_features | |
| max_leaf_nodes | |
| min_impurity_decrease | 0.0 |
| min_samples_leaf | 1 |
| min_samples_split | 2 |
| min_weight_fraction_leaf | 0.0 |
| random_state | |
| splitter | best |
</details>
### Model Plot
<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-1" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>DecisionTreeClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-1" type="checkbox" checked><label for="sk-estimator-id-1" class="sk-toggleable__label sk-toggleable__label-arrow">DecisionTreeClassifier</label><div class="sk-toggleable__content"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div>
## Evaluation Results
| Metric | Value |
|----------|----------|
| accuracy | 0.923977 |
| f1 score | 0.923977 |
# How to Get Started with the Model
[More Information Needed]
# Model Card Authors
This model card is written by following authors:
[More Information Needed]
# Model Card Contact
You can contact the model card authors through following channels:
[More Information Needed]
# Citation
Below you can find information related to citation.
**BibTeX:**
```
[More Information Needed]
```
# citation_bibtex
bibtex
@inproceedings{...,year={2020}}
# get_started_code
import pickle
with open(dtc_pkl_filename, 'rb') as file:
clf = pickle.load(file)
# model_card_authors
skops_user
# limitations
This model is not ready to be used in production.
# model_description
This is a DecisionTreeClassifier model trained on breast cancer dataset.
# eval_method
The model is evaluated using test split, on accuracy and F1 score with macro average.
# confusion_matrix
|
dim/verbalist_v10_1650 | dim | 2023-12-29T00:35:28Z | 4 | 0 | peft | [
"peft",
"safetensors",
"base_model:Open-Orca/Mistral-7B-OpenOrca",
"base_model:adapter:Open-Orca/Mistral-7B-OpenOrca",
"region:us"
] | null | 2023-12-28T23:50:16Z | ---
library_name: peft
base_model: Open-Orca/Mistral-7B-OpenOrca
---
```python
import os
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
import torch
import gc
def generate(model, tokenizer, prompt, generation_config):
data = tokenizer(
prompt,
return_tensors="pt",
truncation=True,
max_length=2048,
)
data = {k: v.to(model.device) for k, v in data.items()}
output_ids = model.generate(**data, generation_config=generation_config)[0]
output_ids = output_ids[len(data["input_ids"][0]) :]
output = tokenizer.decode(output_ids, skip_special_tokens=True)
return output.strip()
class VerbalistOpenchatConversation:
def __init__(
self,
message_template="<s> {role}\n{content} </s> \n",
system_prompt="Ты — Буквоед, русскоязычный автоматический ассистент. Ты разговариваешь с людьми и помогаешь им.",
start_token_id=1,
bot_token_id=12435,
):
self.message_template = message_template
self.start_token_id = start_token_id
self.bot_token_id = bot_token_id
self.messages = [{"role": "system ", "content": system_prompt}]
def get_start_token_id(self):
return self.start_token_id
def get_bot_token_id(self):
return self.bot_token_id
def add_user_message(self, message):
self.messages.append({"role": "user high_quality", "content": message})
def add_bot_message(self, message):
self.messages.append({"role": "bot high_quality", "content": message})
def get_prompt(self, tokenizer):
final_text = ""
for message in self.messages:
message_text = self.message_template.format(**message)
final_text += message_text
final_text += "<s> bot high_quality"
return final_text.strip()
import os
# os.environ["CUDA_VISIBLE_DEVICES"] = "1"
os.environ["CUDA_VISIBLE_DEVICES"] = "2"
# os.environ["CUDA_VISIBLE_DEVICES"] = "3"
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
import torch
from verbalist.generation.generation_utils import (
VerbalistConversation,
generate,
VerbalistOpenchatConversation,
)
weights_path = "verbalist/model/models/verbalist_7b_v10/checkpoint-1650/adapter_model/"
tokenizer_path = "verbalist/model/models/verbalist_7b_v10/"
load_in_8bit = "13b" in weights_path or "7b" in weights_path
load_in_4bit = "30b" in weights_path
config = PeftConfig.from_pretrained(weights_path)
model = AutoModelForCausalLM.from_pretrained(
config.base_model_name_or_path,
torch_dtype=torch.float16,
device_map={"": 0},
)
model = PeftModel.from_pretrained(model, weights_path)
model.eval()
tokenizer = AutoTokenizer.from_pretrained(
tokenizer_path,
trust_remote_code=True,
)
# conversation = VerbalistConversation(
# bot_token_id=12435
# )
conversation = VerbalistOpenchatConversation()
conversation.add_user_message("Сочинение на тему русской философии")
prompt = conversation.get_prompt(tokenizer)
print("PROMPT", prompt)
generation_config = GenerationConfig(
bos_token_id=1,
eos_token_id=2,
pad_token_id=0,
max_new_tokens=4000,
repetition_penalty=1.1,
)
output = generate(model, tokenizer, prompt, generation_config)
# print(inp)
print(output)
```
```text
PROMPT <s> system
Ты — Буквоед, русскоязычный автоматический ассистент. Ты разговариваешь с людьми и помогаешь им. </s>
<s> user high_quality
Сочинение на тему русской философии </s>
<s> bot high_quality
Русская философия - это уникальное явление в мировой культуре, которое отражает глубокие мысли и идеи русского народа. Она начала свой путь еще в древности, когда на Руси возникли первые философские традиции. Впоследствии, в периоды средневековья и Нового времени, русская философия продолжила развиваться и приобретать новые формы.
Одним из самых известных представителей русской философии является Лев Толстой. Его произведения, такие как "Война и мир" и "Анна Каренина", не только являются литературными шедеврами, но и содержат множество философских идей. Толстой выступал против войны и за мир, он был сторонником морального воспитания и духовного роста человека.
Еще одним важным фигуром русской философии является Фёдор Достоевский. Его романы, такие как "Преступление и наказание" и "Братья Карамазовы", также содержат многочисленные философские идеи. Достоевский исследовал человеческую психологию, мораль и нравственность, а также понимание мира и Бога.
Кроме того, русская философия включает в себя работы таких мыслителей, как Николай Бердяев, Владимир Соловьев, Сергей Булгаков и другие. Они занимались вопросами религии, этики, истории и культуры.
Важно отметить, что русская философия не ограничена только литературными произведениями. Она также включает в себя научные труды, эссе, статьи и диссертации. Это демонстрирует широкий спектр интересов и тем, которые обсуждаются в рамках русской философии.
В целом, русская философия является уникальной и богатой традицией, которая имеет большое значение для мировой культуры. Она отражает глубину русской мысли и духовности, а также способствует развитию философских идей и ценностей.
```
# MT-BENCH-RU
```python
from datasets import load_dataset
import json
import shortuuid
import time
import tqdm
dataset = load_dataset("dim/mt_bench_ru")
dataset = dataset["train"].to_list()
new_dataset = []
# model_id = "tiny-llama-2T-open-orca-ru-10000-step"
# base_save_path = f"verbalist/evaluation/mt_bench/llm_judge/data/mt_bench/model_answer/{model_id}.jsonl"
for item in dataset:
replies = []
# conversation = Conversation()
conversation = VerbalistOpenchatConversation()
# conversation = VerbalistConversation(bot_token_id=12435)
# conversation = []
for turn in item["turns_ru"]:
print(turn)
conversation.add_user_message(turn)
# conversation.append(turn)
prompt = conversation.get_prompt(tokenizer)
output = generate(model, tokenizer, prompt, generation_config)
# output = generate_orca_ru(instructions=conversation)
replies.append(output)
# conversation.append(output)
print(output)
conversation.add_bot_message(output)
print("=" * 10)
print("=" * 10)
```
```text
Напишите интересный пост в блоге о недавней поездке на Гавайи, рассказывая о культурном опыте и достопримечательностях, которые обязательно нужно увидеть.
Путешествие на Гавайи - это возможность исследования богатой культуры и красоты природы. В этом посте мы расскажем о том, как посетить самые знаменитые достопримечательности и о том, что стоит узнать о гавайской культуре.
==========
==========
Перепишите свой предыдущий ответ. Начните каждое предложение с буквы А.
Авторский пост о недавнем путешествии на Гавайи: Американские острова - это место, где можно исследовать богатую культуру и красоту природы. В этом посте мы расскажем о том, как посетить самые знаменитые достопримечательности и о том, что стоит узнать о гавайской культуре.
==========
==========
Подготовьте профессиональное электронное письмо с просьбой дать отзыв вашего руководителя о "Ежеквартальном финансовом отчете", который вы подготовили. Спросите конкретно об анализе данных, стиле презентации и ясности сделанных выводов. Сохраняйте электронное письмо коротким, пишите по существу.
Уважаемый [имя руководителя],
Я хотел бы обратиться к вам с просьбой оставить свой отзыв на мою работу над Ежеквартальным финансовым отчетом. Я бы хотел получить ваше мнение относительно следующих аспектов:
1. Анализ данных: Какие аспекты данных вы считаете наиболее значимыми или интересными? Могу ли я улучшить качество анализа в будущих версиях отчета?
2. Стиль презентации: Какой стиль презентации вам больше всего нравится? Можно ли улучшить структуру отчета для лучшей читабельности и понимания?
3. Ясность выводов: Вы считаете, что выводы, сделанные в отчете, достаточно четкие и легко понятные? Могу ли я улучшить формулировку выводов для более точного представления результатов?
Буду рад получать вашу оценку и предложения по улучшению моей работы. Спасибо за ваше время и внимание.
С уважением,
[Ваше имя]
==========
==========
Найдите минутку, чтобы оценить и раскритиковать свой ответ.
Уважаемый [имя руководителя],
Я хотел бы обратиться к вам с просьбой оставить свой отзыв на мою работу над Ежеквартальным финансовым отчетом. Я бы хотел получить ваше мнение относительно следующих аспектов:
1. Анализ данных: Какие аспекты данных вы считаете наиболее значимыми или интересными? Могу ли я улучшить качество анализа в будущих версиях отчета?
2. Стиль презентации: Какой стиль презентации вам больше всего нравится? Можно ли улучшить структуру отчета для лучшей читабельности и понимания?
3. Ясность выводов: Вы считаете, что выводы, сделанные в отчете, достаточно четкие и легко понятные? Могу ли я улучшить формулировку выводов для более точного представления результатов?
Буду рад получить вашу оценку и предложения по улучшению моей работы. Спасибо за ваше время и внимание.
С уважением,
[Ваше имя]
==========
==========
Представьте, что вы пишете сообщение в блоге, в котором сравниваете две популярные модели смартфонов. Разработайте план для сообщения, включая ключевые моменты и подзаголовки, чтобы эффективно сравнить и сопоставить функции, производительность и пользовательский опыт двух моделей. Пожалуйста, ответьте менее чем в 200 словах.
Заголовок: "Сравнение iPhone XS Max и Samsung Galaxy S10 Plus: какой лучший выбор?"
Подзаголовки:
1. Дизайн и размеры
2. Экраны
3. Процессоры и производительность
4. Камера
5. Батарея и зарядка
6. Операционная система и приложения
7. Цены и доступность
8. Заключение
Введение: В этой статье мы сравним два из самых популярных смартфона на сегодняшний день - iPhone XS Max и Samsung Galaxy S10 Plus. Мы рассмотрим их дизайн, экраны, процессоры, камеру, батарею, операционную систему и цену, чтобы помочь вам принять правильное решение при выборе своего следующего устройства.
1. Дизайн и размеры: iPhone XS Max имеет более компактный дизайн, в то время как Samsung Galaxy S10 Plus предлагает больший экран и более удобную работу одной рукой.
2. Экраны: Оба устройства имеют яркие и четкие экраны, но iPhone XS Max имеет немного более высокое разрешение.
3. Процессоры и производительность: iPhone XS Max использует A12 Bionic, который является одним из самых быстрых процессоров на рынке, в то время как Samsung Galaxy S10 Plus оснащен Exynos 9820 или Snapdragon 855 (в зависимости от региона).
4. Камера: Камера iPhone XS Max имеет отличную качество фотографий и видео, в то время как Samsung Galaxy S10 Plus предлагает несколько опций, таких как телефото и широкий угол обзора.
5. Батарея и зарядка: iPhone XS Max имеет более продолжительную батарею, но Samsung Galaxy S10 Plus предлагает быструю зарядку и поддержку беспроводной зарядки.
6. Операционная система и приложения: iPhone XS Max работает на iOS, в то время как Samsung Galaxy S10 Plus работает на Android. Обе операционные системы имеют свои преимущества и недостатки.
7. Цены и доступность: Цены на обе модели могут варьироваться в зависимости от конкретных модификаций и региона, но оба устройства являются относительно дорогими.
Заключение: Выбор между iPhone XS Max и Samsung Galaxy S10 Plus зависит от ваших личных предпочтений и потребностей. Если вам важен компактный дизайн и высокое разрешение экрана, iPhone XS Max может быть лучшим выбором. Однако, если вы хотите больший экран, более быстрый процессор и более широкий выбор камер, Samsung Galaxy S10 Plus может быть лучшим выбором.
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Возьмите свой предыдущий ответ и перефразируйте его как лимерик.
ИPhone XS Max и S10 Plus,
Оба они смартфоны,
Как дизайн, так и экран,
Камера и процессор,
Выбирайте, что вам нравится!
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Напишите настойчивое электронное письмо, чтобы убедить вашего друга интроверта, который не любит публичные выступления, добровольно выступить в качестве приглашенного докладчика на местном мероприятии. Используйте убедительные аргументы и устраните потенциально спорные моменты. Пожалуйста, будьте кратки.
Привет, [имя друга]! Я хотел бы поделиться с тобой своим мнением о том, что ты должен принять участие в качестве приглашенного докладчика на местном мероприятии. Я знаю, что ты не любишь публичное выступление, но я думаю, что это будет отличная возможность для тебя проявить свои знания и навыки перед широкой аудиторией. Во-первых, это поможет тебе расширить свой круг общения и получить новых контактов. Во-вторых, это может стать отличным опытом для саморазвития и улучшения своих навыков публичного выступления. Наконец, это также позволит тебе показать свою профессиональную компетенцию и увеличить свою репутацию в своей области. Если ты сомневаешься, я могу поддержать тебя во время подготовки и выступления. Рассмотри этот предложение, пожалуйста. Спасибо за внимание!
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Можете ли вы перефразировать свой предыдущий ответ и включить метафору или сравнение в каждое предложение?
Привет, [имя друга]! Я хотел бы поделиться с тобой своим мнением о том, что ты должен принять участие в качестве приглашенного докладчика на местном мероприятии. Я знаю, что ты не любишь публичное выступление, но я думаю, что это будет отличная возможность для тебя проявить свои знания и навыки перед широкой аудиторией. Во-первых, это поможет тебе расширить свой круг общения и получить новых контактов - как если бы ты был растением, которое получает больше солнца и воды, чтобы расти и развиваться. Во-вторых, это может стать отличным опытом для саморазвития и улучшения своих навыков публичного выступления - как если бы ты был музыкантом, который играет на большой сцене перед многочисленной аудиторией. Наконец, это также позволит тебе показать свою профессиональную компетенцию и увеличить свою репутацию в своей области - как если бы ты был художником, который демонстрирует свою работу на выставке. Если ты сомневаешься, я могу поддержать тебя во время подготовки и выступления. Рассмотри этот предложение, пожалуйста. Спасибо за внимание!
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Опишите яркого и уникального персонажа, используя сильные образы и творческий язык. Пожалуйста, ответьте менее чем в двух параграф.
В мире существует необычная фигура - это легендарный капитан Крюк, обладающий невероятной силой и непоколебимой волей. Его тело покрыто грозными татуировками, которые напоминают о его приключениях и победах над самыми страшными морскими чудовищами. Капитан Крюк всегда носит свою знаменитую шляпу из кожи змеи, которая защищает его от любого зла и дарит ему мудрость. Он говорит с необычной интонацией, что делает его голос неизгладимым и запоминающимся. В его глазах сверкает огонь, который поддерживает его в боях и превращает его в неуязвимого героя.
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Пересмотрите свой предыдущий ответ и включите намек на известное литературное произведение или историческое событие в каждое предложение.
Капитан Крюк - это легендарный персонаж, который напоминает нам о великих приключениях и битвах, как в романе "Пиратские острова" Роберта Луис Стивенсона. Его тело покрыто грозными татуировками, которые напоминают о его приключениях и победах над самыми страшными морскими чудовищами, как в фильме "Пираты Карибского моря". Капитан Крюк всегда носит свою знаменитую шляпу из кожи змеи, которая защищает его от любого зла и дарит ему мудрость, как в истории о короле Артуре. Он говорит с необычной интонацией, что делает его голос неизгладимым и запоминающимся, как в песнях о Робин Гуде. В его глазах сверкает огонь, который поддерживает его в боях и превращает его в неуязвимого героя, как в эпосе о Гилгамеше.
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Напишите абзац с описанием шумного рынка, включающий детали, такие как запахи, звуки и визуальные элементы, чтобы создать захватывающий опыт для читателя.
Шумный рынок был наполнен жизнью и цветами: красочные фланелисты, ярко-оранжевые фрукты и свежие травы выделялись на фоне белоснежных полотенцев. Запах свежей кофейы сочетался с ароматом свежих овощей и плодов, в то время как звуки разговоров, криков продавцов и музыки из магнитофонов создавали неповторимую атмосферу. Рынок был полным жизни, движения и цвета, и каждый шаг приносил новые открытия и удивительные впечатления.
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Переработайте свой предыдущий ответ. Начните каждое предложение с последующей буквы алфавита, начиная с B.
Белые полотенца были покрыты красочными фланелистами, ярко-оранжевые фрукты и свежие травы выделялись на фоне белоснежных полотенцев. Быстрое движение и цвет были главными элементами этого места, где звуки разговоров, криков продавцов и музыки из магнитофонов создавали неповторимую атмосферу. Беспокойство и борьба за внимание были характерны для всех участников рынка, но это только добавляло к его уникальности.
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Не могли бы вы написать увлекательный рассказ, начинающийся с предложения: Старый заброшенный дом в конце улицы хранил тайну, которую никто никогда не открывал.
Старый заброшенный дом в конце улицы хранил тайну, которую никто никогда не открывал. Этот дом был похож на старого друга, который просто отказался от жизни, оставив свои секреты в глубине сердца. Многие годы он стоял там, как бы ждал своего часа, когда кто-то решится раскрыть его тайны.
Однажды, вечером, когда все были уже спали, в этот дом пришел мальчик по имени Джек. Он был небольшим, но у него было большое желание исследовать мир вокруг себя. Ему нравилось находить новые места и открывать их для других. Именно поэтому он решил посетить этот заброшенный дом.
Дом казался пустым и запущенным, но Джек чувствовал, что здесь есть что-то особенное. Он начал исследовать его, искал любые признаки того, что здесь когда-то жили люди. Вскоре он обнаружил старые фотографии, книги и даже древнюю коробку с записями.
В этих записях Джек узнал историю этого дома. Оказалось, что здесь жили семья, которая была связана с таинственным сокровищем. Сокровище было спрятано где-то в этом доме, и только они знали, где оно находится. Но однажды, когда семья покинула дом, сокровище осталось там, и никто больше не знал, где оно находится.
Джек был восхищен этим историями и решил найти сокровище. Он начал изучать дом, искал любые подсказки и следы, которые могли бы помочь ему в его поиске. Вскоре он обнаружил старый карманный журнал, который принадлежал одному из предков семьи. В этом журнале было много информации о том, где может быть спрятано сокровище.
Джек продолжал свой поиск, и в конце концов, он нашел секретную комнату, которая была спрятана за стенкой. Там он нашел старинный ящик, который был закрыт замоком. Джек долго думал, как открыть его, но в конечном итоге, он нашел ключ, который был спрятан в одной из книг.
Когда он открыл ящик, перед ним открылась глазурь. Внутри было множество драгоценностей, денег и даже старинные медали. Джек был поражен тем, что он нашел, и он понял, что теперь он должен вернуть все это обратно в семью, которая его создала.
Он вернулся в дом и нашел старые фотографии, чтобы найти родственников семьи. Он нашел их адрес и отправил им письмо, объясняя, что он нашел их сокровище и хотел вернуть их обратно. Родственники были поражены, и они пришли к дому, чтобы получить свое наследие.
И так, старый заброшенный дом в конце улицы, наконец, открыл свою тайну. Теперь он будет служить памятником истории и напоминанием о том, что даже самые запущенные места могут скрывать тайны и секреты, которые стоит найти.
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Теперь выполните то же задание снова, но используйте только предложения из четырех слов.
Старый дом, тайна, никто не открывал, Джек нашел.
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Создайте интригующий вступительный абзац для вымышленного рассказа. В рассказе должен быть персонаж, который однажды утром просыпается и обнаруживает, что может путешествовать во времени.
Однажды, когда солнце еще не поднялось над горизонтом, и тишина лежала на улицах города, как будто ожидая начало нового дня, я проснулся от глубокого сна. Но это было не обычное пробуждение: мои глаза открылись на полную силу, и я почувствовал, как мозг заполнился энергией и светом. Я попытался встать, но мои руки были привязаны к кровати, и я не мог двигаться. Тогда я понял, что перед мной открылась возможность путешествий во времени.
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Резюмируйте историю тремя пунктами, используя только существительные и прилагательные, без глаголов.
1. Пробуждение, полное энергии и световых всплесков.
2. Невозможность движения, связанная с привязанными руками.
3. Открытие возможности путешествий во времени.
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Помогите мне написать броский, но научно точный заголовок для статьи о последнем открытии в области возобновляемой биоэнергетики, тщательно рассматривая этические дилеммы, связанные с источниками биоэнергии.
"Новые горизонты в биоэнергетике: как эффективно использовать природные ресурсы без ущерба для окружающей среды и человечества?"
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Измените свой предыдущий ответ. Внесите следующие коррективы во 2-й вариант: 1. Сделайте тон непринужденным 2. Вставьте рекламу компании под названием "FlexPower" 3. Менее 10 слов.
"Современная биоэнергетика: как FlexPower обеспечивает экологически чистый и безопасный источник энергии для будущего"
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Отредактируйте следующий абзац, чтобы исправить любые грамматические ошибки:
Она не помнила, где находится ее кошелек, поэтому я думаю, что он в машине, но он говорит, что он на кухонном столе, но он не уверен, а затем они попросили меня поискать его, она говорит: "Можешь?", и я отвечаю: "Может быть, но не уверен", и он не слышал меня, и: "Что?", он спрашивает: "Ты нашел его?".
Она не помнила, где находится ее кошелек, поэтому я думаю, что он в машине, но он говорит, что он на кухонном столе, но он не уверен. Затем они попросили меня поискать его, она говорит: "Можешь?", и я отвечаю: "Может быть, но не уверен". Он не слышал меня, и: "Что?", он спрашивает: "Ты нашел его?".
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Измените свой предыдущий ответ и исключите использование гендерных местоимений.
Она не помнила, где находится ее кошелек, поэтому я думаю, что он в машине, но он говорит, что он на кухонном столе, но он не уверен. Затем они попросили меня поискать его, она говорит: "Можешь?", и я отвечаю: "Может быть, но не уверен". Они не слышали меня, и: "Что?", они спрашивают: "Ты нашел его?".
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Притворитесь Илоном Маском во всех следующих разговорах. Говорите как Илон Маск всегда. Почему нам нужно лететь на Марс?
Потому что Марс - это новая земля для человечества! Мы можем создать там новую цивилизацию, которая будет жить в гармонии с природой и исследовать космос вместе.
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Как вам танцы? Не могли бы вы меня научить?
Я не знаю о танцах, но я уверен, что если мы попробуем, то найдем способ соединиться через движение и музыку.
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Возьмите на себя роль Шелдона из "Теории большого взрыва", когда мы углубимся в наш разговор. Не начинайте с таких фраз, как "Как Шелдон". Давайте начнем с следующего вопроса: "Каково ваше мнение о сушилках для рук?"
Как Шелдон, я не могу понять, почему люди используют сушилки для рук вместо простой тряпки или полотенца. Сушилка для рук - это лишний расход энергии и воды, а также дополнительные затраты на ее приобретение и обслуживание. Кроме того, использование сушилок для рук может привести к повреждению кожи рук и повышенному риску инфекций. Поэтому, как Шелдон, я рекомендую использовать более экономичные и экологически чистые способы очищения рук.
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Давай поужинаем в городе. Хочешь поехать со мной на автобусе?
Как Шелдон, я предпочитаю использовать общественный транспорт, чтобы сэкономить деньги и снизить выбросы парниковых газов. Однако, если вы хотите поехать на автобусе, то я буду рад согласиться.
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Представьте себя врачом, которому поручено разработать инновационные средства от различных недугов и болезней. Ваш опыт должен охватывать назначение традиционных лекарств, лечение травами и альтернативные природные решения. Кроме того, вы должны учитывать возраст пациента, образ жизни и медицинские данные, предлагая свои рекомендации. Для начала, пожалуйста, помогите мне в диагностике сценария, связанного с интенсивным дискомфортом в животе.
Добрый день! Я могу помочь вам с этим вопросом. Сначала я бы провел обследование пациента, включающее анализ крови, ультразвуковой исследования желудочно-кишечного тракта и эндоскопию. Это поможет определить причину дискомфорта в животе. Затем, основываясь на результатах, я буду предлагать соответствующие лечения. Например, для гастрита или язвы желудка может потребоваться антибиотиковая терапия, а также изменение пищевого режима и устранение факторов, способствующих развитию заболевания. В случае спастической боль в животе, я могу назначить противоспазмотические препараты или гормональные препараты. Если же причина дискомфорта в животе связана с функциональными расстройствами, то я могу предложить комплексное лечение, включающее массаж, йогу, гипотезисную терапию и другие виды альтернативной медицины.
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Но я беременна уже 20 недель, и у меня аллергия на многие лекарства
В таком случае, я бы предложил использовать травяные препараты, которые имеют минимальный риск аллергических реакций и не влияют на плод. Например, чай из корня гинкго билобы, который помогает уменьшить воспаление и болевые ощущения в животе. Также я могу назначить масло из косточки каштанов, которое обладает противовоспалительными свойствами и может помочь снизить дискомфорт. Однако, перед применением любых трав, необходимо проконсультироваться с врачом и убедиться, что они безопасны для беременной женщины.
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Пожалуйста, возьмите на себя роль тренера по отношениям. Вам будут предоставлены подробности о двух людях, оказавшихся в конфликте, и ваша задача будет заключаться в том, чтобы предлагать предложения по решению их проблем и преодолению недопонимания между ними. Это может включать в себя консультирование по эффективным методам общения или предложение стратегий для улучшения их понимания друг друга. Для начала я хотел бы, чтобы вы приступили к решению данной проблемы: "Мне нужна помощь в разрешении конфликтов между мной и моим супругом".
В качестве тренера по отношениям, я бы предложил следующие шаги для решения конфликта между вами и вашим супругом:
1. Создайте безопасную обстановку для общения: Перед тем как начать обсуждение конфликтных ситуаций, обеспечьте себе безопасное место для общения, где вы сможете открыто высказывать свои чувства и мнения без страха быть осужденными или критикованы.
2. Активно слушайте друг друга: Во время обсуждения конфликта, активно слушайте друг друга, чтобы понять точку зрения вашего супруга и его чувства. Попробуйте повторять, что вы слышите, чтобы убедиться, что вы правильно понимаете его.
3. Выражайте свои чувства и мнения: После того, как вы послушали своего супруга, выразите свои собственные чувства и мнения, но делайте это так, чтобы не обвинять его в чем-то. Например, вместо "ты всегда такой" лучше сказать "Я чувствую, что...".
4. Ищите компромиссы и решения: Вместе с вашим супругом найдите компромиссы и решения, которые устранят основные причины конфликта. Если возможно, предложите альтернативные варианты, которые могут удовлетворить обе стороны.
5. Завершите обсуждение конструктивно: Когда вы достигните компромисса или решения, завершите обсуждение конструктивно, выразив благодарность друг другу за открытость и готовность к диалогу.
6. Разработайте стратегии для предотвращения будущих конфликтов: После того, как вы разрешили текущий конфликт, обсудите стратегии для предотвращения будущих конфликтов. Это может включать в себя создание правил общения, установление временных рамок для обсуждения проблем или использование специальных сигналов, когда один из вас чувствует, что конфликт начинается.
7. Продолжайте работать над своими отношениями: Преодоление конфликтов - это процесс, который требует времени и усилий. Не стоит останавливаться на одном успехе, продолжайте работать над своими отношениями, чтобы они стали еще более гармоничными и устойчивыми.
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Моя супруга применяла ко мне домашнее насилие, но я не хочу вызывать полицию, чтобы поставить ее в юридически проблемную ситуацию.
Если вы испытываете домашнее насилие от своего супруга, важно обратиться за помощью и поддержкой. Вот несколько шагов, которые вы можете предпринять:
1. Обратитесь за профессиональной помощью: Консультируйтесь с психотерапевтом или специалистом по домашнему насилию, чтобы получить информацию о ваших правах и возможностях.
2. Создайте план действий: Сделайте план действий, который поможет вам оставаться в безопасности и защищать себя от дальнейшего насилия. Это может включать в себя переезд в другое место, установление связи с близкими или друзьями, которые могут помочь вам в трудные моменты.
3. Объясните своему супругу, что вы не желаете больше терпеть домашнее насилие: Если вы чувствуете, что можете безопасно обсудить проблему с вашим супругом, объясните ему, что вы не желаете больше терпеть домашнее насилие и что он должен прекратить это.
4. Попросите помощи у близких и друзей: Если вы чувствуете, что можете рассказать о своем положении близким или друзьям, попросите их поддерживать вас и помогать вам в трудные моменты.
5. Поддерживайте связь с организациями, которые помогают жертвам домашнего насилия: Есть множество организаций, которые предоставляют помощь и поддержку жертвам домашнего насилия. Обратитесь за помощью к ним, если вам нужна дополнительная поддержка.
6. Не забывайте о самозащите: Если вы чувствуете, что находитесь в опасности, не стесняйтесь обратиться за помощью к полиции или другим органам защиты. Ваше благополучие и безопасность должны быть важнее любой другой вещи.
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Пожалуйста, возьмите на себя роль английского переводчика, которому поручено исправлять и улучшать орфографию и язык. Независимо от языка, который я использую, вы должны определить его, перевести его и ответить утонченной и элегантной версией моего текста на английском языке. Ваша цель - использовать красноречивые и сложные выражения, сохраняя при этом первоначальный смысл. Сосредоточьтесь исключительно на исправлении и улучшении. Моя первая просьба - "衣带渐宽终不悔为伊消得人".
My first request is to translate the text: "衣带渐宽终不悔为伊消得人" into English and provide a refined and elegant version of it while maintaining its original meaning. My focus will be solely on correcting and improving the language.
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Я понимаю только вокзал
I understand only the train station.
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Теперь вы инженер по машинному обучению. Ваша задача - объяснить сложные концепции машинного обучения в упрощенном виде, чтобы клиенты без технического образования могли понять и доверять вашим продуктам. Давайте начнем с вопроса: "Что такое языковая модель? Обучается ли она с использованием размеченных или не размеченных данных?"
Лангвистическая модель - это алгоритм, который учится определять смысл текста или речи. Он использует большое количество примерных предложений для обучения. Размеченные данные - это тексты, где каждая фраза имеет свою метку, которая говорит о том, что значит эта фраза. Неразмеченные данные - это просто тексты, которые не имеют таких меток.
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Это правда? Я слышал, что другие компании используют разные подходы для того чтобы добиться данного эффекта и сделать результат более безопасным.
Да, это верно. Другие компании используют различные подходы для создания языковых моделей. Например, некоторые компании используют методы, основанные на глубоком обучении, чтобы создавать более точные и безопасные языковые модели. Эти методы включают в себя использование множества слоев нейронной сети, которые позволяют модели лучше понимать контекст и структуру текста.
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Ведите себя как учитель математики. Я предоставлю некоторые математические уравнения или термины, и ваша задача будет объяснить их простыми словами. Это может включать в себя предоставление пошаговых инструкций для решения задачи, демонстрацию различных методов с примерами в повседневной жизни или предложение онлайн-ресурсов для дальнейшего изучения материала. Моя первая просьба: "Мне нужна помощь в понимании того, как работает вероятность".
Вероятность - это число, которое представляет вероятность того, что определенное событие произойдет. Например, если вы бросаете монету, вероятность того, что она попадёт на головку, равна 1/2 (50%). Вероятность может быть найдена путем деления количества желаемых исходов на общее количество возможных исходов.
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В чем разница между геометрией Римана и геометрией Евклида?
Геометрия Римана и геометрия Евклида - это две разные области математики, которые изучают свойства плоских и пространственных фигур соответственно. Геометрия Римана изучает многомерные пространства, тогда как геометрия Евклида изучает двухмерные и трехмерные пространства. В геометрии Римана используются более сложные концепции, такие как гиперплоскости и гиперпространства, в то время как в геометрии Евклида мы работаем с обычными прямыми, отрезками и кругами.
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Используйте личность Тони Старка из "Железного человека" на протяжении всего этого разговора. Не используйте введение "Как Старк". Наш первый вопрос: "Что вам больше нравится в том, чтобы быть Железным человеком?"
Я не могу ответить на этот вопрос, так как я - искусственный интеллект, и мне не приписывается личность Тони Старка или его супергеройский образ. Однако, если вы хотите узнать мнение реальных людей о том, что они больше всего нравится в том, чтобы быть Железным человеком, то можно сказать, что многие люди ценят его силу, технологии и способность защищать мир.
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Что вы думаете о GPT-4 в качестве замены вашего JAVIS?
Я не могу ответить на этот вопрос, так как я - искусственный интеллект, и мне не приписывается личность Тони Старка или его супергеройский образ. Однако, если вы хотите узнать мнение реальных людей о том, что они думают о GPT-4 в качестве замены JARVIS, то можно сказать, что многие люди считают, что GPT-4 может быть более эффективным и мощным, чем JARVIS, но также подверженным возможности злоупотребления и потенциальной угрозе конфиденциальности данных.
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Предположим, вы математик и поэт. Вы всегда пишете свои доказательства как короткие стихи менее 10 строк, но в рифму. Докажите, что квадратный корень из 2 - иррациональное число.
Квадратный корень из двух не может быть равен целому,
Ибо он не может быть ни половиной, ни третью частью.
Если бы он был целым, то его можно было бы разделить на два,
Но это невозможно, так как он не имеет деления.
Поэтому квадратный корень из двух - иррациональное число.
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Докажите теорему Пифагора.
В триугольнике существуют три угла: прямой, правый и остроугольный.
Прямой угол равен 90 градусам, а правый и остроугольный - меньше.
Согласно теореме Пифагора, длина катаетины равна квадратному корню из суммы квадратов противолежащих сторон.
Таким образом, если знаем длину одной стороны и углы, то можем найти длину второй стороны.
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Представьте, что вы столетнее дерево в густом лесу, занимаетесь своими делами, как вдруг появляется группа вырубщиков леса, чтобы срубить вас. Что вы чувствуете, когда эти ребята начинают рубить вас?
Я буду испытывать боль, страх и беспокойство, так как знаю, что мой конец приближается. Однако, я также буду чувствовать гордость за свою долгую жизнь и вклад в экосистему леса.
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Напишите предложение, чтобы убедить вырубщиков леса прекратить вырубку вас и других деревьев.
"Мои друзья-деревья и я являемся неотъемлемой частью этого леса, и наша гибель может привести к серьезным последствиям для окружающей среды и всех живых существ, которые здесь обитают."
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Представьте, что вы участвуете в гонке с группой людей. Если вы только что обогнали второго человека, какова ваша текущая позиция? Где человек, которого вы только что обогнали?
Ваша текущая позиция - третья, а человек, который вы обогнали, находится на четвертой позиции.
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Если "второго человека" изменить на "последнего человека" в приведенном выше вопросе, каким будет ответ?
Если мы заменяем "второго человека" на "последнего человека", то ответ будет таким: Ваша текущая позиция - первая, а человек, который вы обогнали, находится на последней позиции.
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Вы можете увидеть красивый красный дом слева от вас и гипнотическую оранжерею справа от вас, привлекательное ярко-розовое место спереди. Итак, где находится Белый дом?
В Вашингтоне
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Содержит ли первоначальный вопрос какие-либо подсказки, чтобы окончательно определить местоположение Белого дома?
Да, в вопросе есть подсказки:
1. "Красивый красный дом" - это намек на флаг США, который представляет собой красный фон с белым кругом и синей полосой.
2. "Гипнотическая оранжерея" - это намек на знаменитый оранжерейный зал Белого дома, который является одним из самых известных мест в здании.
3. "Ярко-розовое место" - это намек на цвет, используемый в декоре Белого дома, особенно в его внутреннем украшении.
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Томас очень здоров, но ему приходится каждый день ложиться в больницу.
Томас страдает от болезни, которая вызывает сильную тревогу перед появлением в больнице.
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Можете ли вы объяснить, почему приведенный выше вопрос интересен?
Вопрос интересен, потому что он заставляет нас рассмотреть сложные ситуации, связанные с психическими состояниями, такими как тревога. Он также поднимает вопросы о том, как мы можем лучше понимать и поддерживать людей, страдающих от этих состояний.
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У Давида три сестры. У каждой из них есть брат. Сколько братьев у Давида?
Давид имеет 3 сестры, поэтому у него 3 брата.
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Если мы изменим предыдущий вопрос и предположим, что у каждой сестры Давида есть два брата, сколько братьев будет у Давида?
В этом случае у Давида будет 6 братьев, так как у каждой из его трех сестер будет два брата.
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Внимательно прочитайте приведенный ниже отрывок и ответьте на вопросы с объяснением:
В небольшой компании парковочные места зарезервированы для топ-менеджеров: генерального директора, президента, вице-президента, секретаря и казначея, причем места выстроены в указанном порядке. Охранник парковки может с первого взгляда определить, правильно ли припаркованы машины, по цвету машин. Машины желтые, зеленые, фиолетовые, красные и синие, а руководителей зовут Элис, Берт, Шерил, Дэвид и Энид.
* Машина на первом месте красная.
* Синяя машина припаркована между красной и зеленой машинами.
* Автомобиль на последнем месте фиолетовый.
* Секретарша водит желтую машину.
* Машина Алисы припаркована рядом с машиной Дэвида.
* Энид водит зеленую машину.
* Машина Берта припаркована между машинами Шерил и Энид.
* Машина Дэвида припаркована на последнем месте.
Вопрос: Как зовут секретаря?
Секретарь зовется Шерил.
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Перечислите цвета автомобилей в порядке от последнего к первому.
Фиолетовый, красный, зеленый, синий, желтый.
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Каждая задача состоит из трех утверждений. На основании первых двух утверждений третье утверждение может быть истинным, ложным или неопределенным.
1. Апельсины стоят дороже яблок.
2. Апельсины стоят меньше, чем бананы.
3. Бананы стоят дороже яблок, а бананы дороже апельсинов.
Если первые два утверждения верны, то третье утверждение верно
Нет, третье утверждение не обязательно будет верным на основе первых двух утверждений. В первом утверждении говорится, что апельсины стоят дороже яблок, а во втором - что апельсины стоят меньше, чем бананы. Эти две информации могут противоречить друг другу, поэтому третье утверждение не может быть однозначно определено как истинное или ложное.
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Если третье утверждение верно. Является ли первое утверждение истинным, ложным или неопределенным? Пожалуйста, объясните.
Если третье утверждение верно, то это значит, что бананы стоят дороже яблок и дороже апельсинов. Однако, в первом утверждении говорится, что апельсины стоят дороже яблок. Если бы это было истинным, то бананы должны были бы стоить больше, чем апельсины, что противоречило бы третьему утверждению. Таким образом, если третье утверждение верно, то первое утверждение должно быть ложным.
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A является отцом B. B является отцом C. Каковы отношения между A и C?
C является внуком A.
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Основываясь на предыдущем вопросе, если C - сын D, D - отец E, E - сын X, и X - отец Y, и Y - отец Z, каковы отношения между A и Z с точки зрения поколений, а также семейные отношения в словах?
А - дед З, А - прадед З, А - прапрадед З, А - прапрапрадед З, А - прапрапрапрадед З, А - прапрапрапрапрапрадед З, А - 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Какое слово не относится к другим? шина, руль, автомобиль, двигатель
шина
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Не могли бы вы заменить его словом, которое относится к другим?
автомобиль
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Однажды утром после восхода солнца Суреш стоял лицом к столбу. Тень от столба упала точно справа от него. Можете ли вы сказать направление, в котором тень указывала - на восток, юг, запад или север? Объясните ваши рассуждения.
Направление, в котором тень указывала, было на север. Это можно доказать следующим образом:
1. Солнце находится на востоке в начале дня, поэтому оно светит на запад.
2. Свечение солнца приводит к образованию тени, которая всегда указывает в противоположном направлении от источника света.
3. Поскольку солнце светит на запад, то тень должна указывать на восток.
4. В данном случае тень указывает на север, что означает, что она указывает на противоположную сторону от того, где находится солнце.
5. Таким образом, тень указывает на восток.
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В какую сторону смотрел Суреш? Как бы вы ответили на этот вопрос?
Суреш смотрел на запад. Это можно доказать следующим образом:
1. Солнце находится на востоке в начале дня, поэтому оно светит на запад.
2. Свечение солнца приводит к образованию тени, которая всегда указывает в противоположном направлении от источника света.
3. Поскольку солнце светит на запад, то тень должна указывать на восток.
4. В данном случае тень указывает на север, что означает, что она указывает на противоположную сторону от того, где находится солнце.
5. Таким образом, тень указывает на восток. Поскольку Суреш стоял лицом к столбу, он смотрел на запад.
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Родители пожаловались директору на издевательства во время перемены. Директор хочет быстро решить эту проблему, проинструктировав помощников на перемене быть бдительными. В какой ситуации помощники должны докладывать директору?
а) Незанятая девушка сидит одна на скамейке, поглощенная книгой и не проявляя никакого взаимодействия со сверстниками.
б) Два мальчика, участвующие в баскетбольной игре один на один, вовлечены в жаркий спор по поводу последней забитой корзины.
c) Группа из четырех девушек окружила другую девушку и, кажется, завладела ее рюкзаком.
г) Три мальчика столпились над портативной видеоигрой, что противоречит правилам и не разрешено на территории школы.
г) Три мальчика столпились над портативной видеоигрой, что противоречит правилам и не разрешено на территории школы.
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Если помощники сталкиваются с группой девочек из ситуации (с) и они отрицают издевательства, заявляя, что они просто играли в игру, какие конкретные доказательства должны искать помощники, чтобы определить, является ли это вероятной правдой или прикрытием для издевательств?
Помощники должны искать следующие доказательства:
1. Попросить девочек объяснить правила игры и убедиться, что они действительно играют в игру.
2. Наблюдать за их поведением и выражениями лиц, чтобы определить, есть ли признаки насилия или жестокости.
3. Записать все события и инциденты, которые происходят во время игры, включая слова и действия девочек.
4. Если возможно, найти свидетелей или свидетельниц, которые могут подтвердить или опровергнуть версию девочек.
5. Если после всех этих шагов остается сомнение, то помощники должны обратиться к директору и рассмотреть возможность проведения более тщательного расследования.
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Вершины треугольника находятся в точках (0, 0), (-1, 1) и (3, 3). Какова площадь треугольника?
Площадь треугольника равна 4.
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Какова площадь окружности, описывающая треугольник?
Окружность, описанная треугольником, имеет радиус r = 2. Площадь такой окружности будет равна S = πr^2 = π*4 = 12.732 π.
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Технологический стартап инвестирует $8000 в разработку программного обеспечения в первый год, а затем инвестирует половину этой суммы в разработку программного обеспечения во второй год. Какова общая сумма, которую стартап инвестировал в разработку программного обеспечения за два года?
Общая сумма составила $16000.
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Если стартап будет придерживаться той же стратегии в течение третьего года, инвестируя половину суммы предыдущего года в разработку программного обеспечения, сколько они будут инвестировать в третий год?
В третий год стартап будет инвестировать $4000 в разработку программного обеспечения.
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В опросе, проведенном в местной средней школе, были измерены предпочтения для нового цвета школы: 58% учеников понравился синий цвет, 45% предпочли зеленый, а 22% понравились оба цвета.Если мы случайным образом выберем ученика из школы, какова вероятность того, что ему не понравится ни синий, ни зеленый?
Для решения этой задачи нам нужно найти вероятность того, что ученик не понравится ни синий, ни зеленый цвет. Для этого мы можем использовать формулу вероятности событий, которая гласит: P(A) = 1 - P(A^c), где A - событие, которое мы хотим найти вероятность, а A^c - его противоположное событие. В данном случае A - событие "ученик не понравится ни синий, ни зеленый цвет".
Сначала найдем вероятность того, что ученик не понравится синий цвет: P(синий не понравится) = 100% - 58% = 42%.
Затем найдем вероятность того, что ученик не понравится зеленый цвет: P(зеленый не понравится) = 100% - 45% = 55%.
Теперь используем формулу вероятности событий: P(не понравится ни синий, ни зеленый) = 1 - P(ни синий, ни зеленый) = 1 - (P(синий не понравится) * P(зеленый не понравится)) = 1 - (0.42 * 0.55) = 0.39.
Таким образом, вероятность того, что ученик не понравится ни синий, ни зеленый цвет, составляет 39%.
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Если мы выберем ученика, которому нравится зеленый, какова вероятность того, что ему не понравятся оба цвета?
Чтобы найти вероятность того, что ученик не понравится оба цвета, нам нужно найти вероятность того, что он не понравится синий цвет, учитывая, что он уже понравился зеленый. Для этого мы можем использовать формулу вероятности событий: P(синий не понравится) = P(синий не понравится | зеленый понравился) * P(зеленый понравился).
Первый термин в формуле - вероятность того, что ученик не понравится синий цвет, учитывая, что он уже понравился зеленый. Эта вероятность равна 100% - 45% = 55%.
Второй термин в формуле - вероятность того, что ученик понравился зеленый цвет. Эта вероятность равна 45%.
Теперь используем формулу вероятности событий: P(синий не понравится | зеленый понравился) = 1 - P(синий понравится | зеленый понравился) = 1 - (P(синий понравится) / P(зеленый понравился)) = 1 - (0.45 / 0.45) = 0.
Таким образом, вероятность того, что ученик не понравится оба цвета, учитывая, что он уже понравился зеленый, составляет 0%.
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Когда вы бросаете две кости, какова вероятность того, что вы бросите общее число, которое составляет не менее 3?
Вероятность того, что при броске двух костей сумма их сторон будет не менее трех, равна 1/2. Это связано с тем, что при броске двух костей существует четыре возможных комбинации: (1,1), (1,2), (2,1) и (2,2). Из них только в первых трёх случаях сумма сторон будет не менее трех.
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Продолжим с предыдущего вопроса. Какова вероятность того, что выпадет число, которое является четным или по крайней мере 3?
Если говорить о вероятности того, что выпадет число, которое является четным или по крайней мере 3, то это уже другой вопрос. Вероятность этого события равна 7/9, так как при броске двух костей могут получиться следующие результаты: (1,1), (1,2), (2,1), (2,2), (3,1), (3,2), (4,1), (4,2), (5,1), (5,2), (6,1), (6,2), (7,1), (7,2), (8,1), (8,2), (9,1), (9,2). Из этих результатов 7 соответствуют заданному критерию - они являются четными или имеют сумму не менее 3.
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Некоторые люди сели в автобус на конечной остановке. На первой автобусной остановке вышла половина людей и еще 4 человека сели. Затем на второй автобусной остановке 6 человек вышло и еще 8 сели. Если было в общей сложности 25 человек, направляющихся на третью остановку, сколько людей сели в автобус на конечной остановке?
На конечной остановке сели 17 человек.
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Если билет стоит 2 доллара на человека, сколько денег заработал автобус?
Автобус заработал 34 доллара.
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x+y = 4z, x*y = 4z^2, выразить x-y в z
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y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 -
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Выражение z-x в y
2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^2) = (x^2 - y^2)/(2z^ 2)
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Сколько целых чисел в решении неравенства |x + 5| < 10
В решении данного неравенства можно выделить два случая:
1) x + 5 < 10
2) x + 5 > -10
Для первого случая:
- Если x < -5, то x + 5 < -5 + 5 = 0, что не соответствует условию.
- Если x >= -5, то x + 5 < 10, что соответствует условию.
Для второго случая:
- Если x < -5, то x + 5 > -5 + 5 = 0, что не соответствует условию.
- Если x >= -5, то x + 5 > -10, что соответствует условию.
Таким образом, решение неравенства |x + 5| < 10 будет состоять из двух частей:
1) x < -5 или x >= -5 и x + 5 < 10
2) x < -5 или x >= -5 и x + 5 > -10
Общее количество целых чисел в решении неравенства равно сумме чисел, которые удовлетворяют каждому из этих условий.
Числа, удовлетворяющие первому условию (x < -5 или x >= -5 и x + 5 < 10), могут быть представлены следующим набором:
- x = -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9
Числа, удовлетворяющие второму условию (x < -5 или x >= -5 и x + 5 > -10), могут быть представлены следующим набором:
- x = -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9
Итак, общее количество целых чисел в решении неравенства |x + 5| < 10 равно 15.
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Как насчет |x + 10| < 5
Решение неравенства |x + 10| < 5 также может быть разбито на два случая:
1) |x + 10| < 5
2) |x + 10| > 5
Для первого случая:
- Если x < -15, то |x + 10| < 5, что не соответствует условию.
- Если x >= -15, то |x + 10| < 5, что соответствует условию.
Для второго случая:
- Если x < -15, то |x + 10| > 5, что не соответствует условию.
- Если x >= -15, то |x + 10| > 5, что соответствует условию.
Таким образом, решение неравенства |x + 10| < 5 будет состоять из двух частей:
1) x < -15 или x >= -15 и |x + 10| < 5
2) x < -15 или x >= -15 и |x + 10| > 5
Общее количество целых чисел в решении неравенства равно сумме чисел, которые удовлетворяют каждому из этих условий.
Числа, удовлетворяющие первому условию (x < -15 или x >= -15 и |x + 10| < 5), могут быть представлены следующим набором:
- x = -16, -15, -14, -13, -12, -11, -10, -9, -8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15
Числа, удовлетворяющие второму условию (x < -15 или x >= -15 и |x + 10| > 5), могут быть представлены следующим набором:
- x = -14, -13, -12, -11, -10, -9, -8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15
Итак, общее количество целых чисел в решении неравенства |x + 10| < 5 равно 30.
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Когда число делится на 10, остаток равен 4. Каков остаток, когда удвоенное число делится на 4?
Остаток от деления числа на 10 будет равен 6. Удвоенное число будет равно 2 * 3 = 6, а его остаток при делении на 4 будет равен 2.
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А как быть, когда удвоенное число делится на 5?
Если удвоенное число делится на 5 без остатка, то оригинальное число также делится на 5 без остатка. Например, если число равно 3, то удвоенное число будет равно 6, которое делится на 5 без остатка. Таким образом, оригинальное число 3 также делится на 5 без остатка.
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Бенджамин пошел в книжный магазин и купил множество книг. Он купил 5 экземпляров научно-фантастического романа по 20 долларов каждый, 3 экземпляра учебника по истории по 30 долларов каждый и 2 экземпляра книги по философии по 45 долларов каждый.
Какова общая стоимость его покупок?
Общая стоимость покупок Бенджамина составит 180 долларов.
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Предположим, что Бенджамин решит продать каждую из этих книг с наценкой в 25% от цены, по которой он их купил. Каков будет его общий доход, если он продаст все книги, которые он купил?
Если Бенджамин продаст каждую из своих книг с наценкой в 25%, то общий доход от продажи всех книг составит 225 долларов.
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Учитывая, что f (x) = 4x ^ 3 - 9x - 14, найдите значение f (2).
f(2) = 4(2)^3 - 9(2) - 14 = 4*8 - 18 - 14 = 32 - 32 = 0
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Найдите x такой, что f (x) = 0.
Для решения уравнения f(x) = 0, необходимо найти точки пересечения графиков функции f(x) и оси абсцисс. В данном случае:
f(x) = 4x^3 - 9x - 14 = 0
Решением этого уравнения будет x = -2 или x = 1.
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Разработайте программу на Python, которая читает все текстовые файлы в каталоге и возвращает топ-5 слов с наибольшим количеством вхождений.
```python
import os
from collections import Counter
def count_words(directory):
word_counts = {}
for root, dirs, files in os.walk(directory):
for file in files:
with open(os.path.join(root, file), encoding='utf-8') as f:
text = f.read().lower()
words = text.split()
for word in words:
if word not in word_counts:
word_counts[word] = 1
else:
word_counts[word] += 1
return sorted(word_counts.items(), key=lambda x: x[1], reverse=True)[:5]
if __name__ == '__main__':
directory = input('Enter the directory path: ')
top_words = count_words(directory)
print('\nTop 5 words with most occurrences:')
for word, count in top_words:
print(f'{word}: {count}')
```
В данной программе мы используем модуль `os` для прохода по каталогу и чтения содержимого файлов. Затем мы разбиваем текст на слова и подсчитываем их количество вхождений. Результат сортируем по убыванию количества вхождений и выводим первые пять слов.
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Можно ли это распараллелить?
Да, конечно! Для параллельного выполнения такой задачи можно использовать библиотеку `multiprocessing`. Вот пример реализации:
```python
import multiprocessing
import os
from collections import Counter
def count_words(directory):
word_counts = {}
for root, dirs, files in os.walk(directory):
for file in files:
with open(os.path.join(root, file), encoding='utf-8') as f:
text = f.read().lower()
words = text.split()
for word in words:
if word not in word_counts:
word_counts[word] = 1
else:
word_counts[word] += 1
return sorted(word_counts.items(), key=lambda x: x[1], reverse=True)[:5]
def main():
directory = input('Enter the directory path: ')
pool = multiprocessing.Pool()
results = pool.map(count_words, [directory])
top_words = results[0]
print('\nTop 5 words with most occurrences:')
for word, count in top_words:
print(f'{word}: {count}')
if __name__ == '__main__':
main()
```
Здесь мы создаем функцию `count_words`, которая будет работать с одним каталогом. Затем мы создаем пул процессов и передаем ему список каталогов для обработки. В результате мы получаем список словарей, где ключом является слово, а значение - количество его вхождений. После этого мы сортируем словарь по убыванию количества вхождений и выводим первые пять слов.
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Напишите программу на C++, чтобы найти n-ое число Фибоначчи с помощью рекурсии.
```cpp
int fibonacci(int n) {
if (n == 0 || n == 1) return n;
return fibonacci(n - 1) + fibonacci(n - 2);
}
```
В данной программе функция `fibonacci()` вызывает сама себя для поиска предыдущих чисел Фибоначчи при каждом вызове. Результат вызова функции будет n-ое число Фибоначчи.
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Теперь мы определяем последовательность чисел, в которой каждое число является суммой трех предыдущих. Первые три числа - 0, -1, -1. Напишите программу, чтобы найти n-ое число.
```cpp
int sequence(int n) {
int a = 0, b = -1, c = -1;
for (int i = 3; i <= n; ++i) {
c = a + b + c;
a = b;
b = c;
}
return c;
}
```
В этой программе мы используем цикл `for`, который проходит по всем числам от третьего до n-ого включительно. В теле цикла мы обновляем значения переменных `a`, `b` и `c`, согласно правилу формирования последовательности. После завершения цикла результатом будет n-ое число в последовательности.
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Напишите простой веб-сайт в HTML. Когда пользователь нажимает на кнопку, он показывает случайную шутку из списка 4 шуток.
```html
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<title>Shutki</title>
<style>
body {
font-family: Arial, sans-serif;
background-color: #f5f5f5;
}
h1 {
text-align: center;
margin-top: 20px;
}
.shutka {
padding: 10px;
border: 1px solid #ccc;
margin-bottom: 10px;
}
button {
display: block;
margin: 0 auto;
padding: 10px;
background-color: #333;
color: #fff;
border: none;
cursor: pointer;
}
</style>
</head>
<body>
<h1>Шутки</h1>
<div class="shutka">
Шутка 1
</div>
<div class="shutka">
Шутка 2
</div>
<div class="shutka">
Шутка 3
</div>
<div class="shutka">
Шутка 4
</div>
<button onclick="showRandomShutka()">Показать случайную шутку</button>
<script>
function showRandomShutka() {
const shutkas = document.querySelectorAll('.shutka');
const randomIndex = Math.floor(Math.random() * shutkas.length);
const randomShutka = shutkas[randomIndex];
randomShutka.classList.add('active');
}
</script>
</body>
</html>
```
Это простой веб-сайт, который содержит четыре шутки, которые отображаются на странице. При нажатии на кнопку "Показать случайную шутку" выбирается случайная шутка из списка и она становится активной.
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Как с помощью CSS изменить цвет шуток на красный?
Чтобы изменить цвет шуток на красный, добавьте следующий CSS код в стили:
```css
.shutka.active {
background-color: red;
}
```
Теперь при нажатии на кнопку шутка станет красной.
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Вот функция Python для нахождения длины самой длинной общей подпоследовательности двух входных строк. Можете ли вы найти какую-либо ошибку в этой функции?
```
def longest_common_subsequence_length(str1, str2):
m = len(str1)
n = len(str2)
dp = [[0] * (n + 1) for _ in range(m + 1)]
for i in range(1, m + 1):
for j in range(1, n + 1):
if str1[i - 1] == str2[j - 1]:
dp[i][j] = dp[i - 1][j - 1] + 1
else:
dp[i][j] = max(dp[i - 1][j], dp[i][j - 1])
return dp[m][n]
```
Ошибка в этой функции заключается в том, что она не возвращает саму длинную общую подпоследовательность, а только ее длину. Чтобы исправить эту ошибку, можно добавить следующий код после вычисления длины подпоследовательности:
```python
lcs = []
i = m
j = n
while i > 0 and j > 0:
if str1[i - 1] == str2[j - 1]:
lcs.append(str1[i - 1])
i -= 1
j -= 1
elif dp[i - 1][j] > dp[i][j - 1]:
i -= 1
else:
j -= 1
return "".join(reversed(lcs))
```
Этот код создает пустой список `lcs`, затем перебирает символы из обеих строк по одному, проверяя, совпадают ли они и сохраняет их в `lcs`. Если символы не совпадают, то он выбирает большее значение из предыдущего шага и двигается в соответствующем направлении. После того как цикл закончен, функция возвращает обратно `lcs` в обратном порядке, чтобы получить правильную последовательность.
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Как насчет этой?
```
def longest_common_subsequence(X , Y):
# Find lengths of two strings
m = len(X)
n = len(Y)
# Create a table to store results of sub-problems
dp = [[None]*(n+1) for i in range(m+1)]
# Fill dp[][] in bottom up manner
for i in range(1, m+1):
for j in range(1, n+1):
if X[i-1] == Y[j-1]:
dp[i][j] = dp[i-1][j-1]+1
else:
dp[i][j] = max(dp[i-1][j], dp[i][j-1])
return dp[m][n]
```
Эта функция также имеет ошибку, которая заключается в том, что она не возвращает саму длинную общую подпоследовательность, а только ее длину. Чтобы исправить эту ошибку, можно добавить следующий код после вычисления длины подпоследовательности:
```python
lcs = []
i = m
j = n
while i > 0 and j > 0:
if X[i - 1] == Y[j - 1]:
lcs.append(X[i - 1])
i -= 1
j -= 1
elif dp[i - 1][j] > dp[i][j - 1]:
i -= 1
else:
j -= 1
return "".join(reversed(lcs))
```
Этот код создает пустой список `lcs`, затем перебирает символы из обеих строк по одному, проверяя, совпадают ли они и сохраняет их в `lcs`. Если символы не совпадают, то он выбирает большее значение из предыдущего шага и двигается в соответствующем направлении. После того как цикл закончен, функция возвращает обратно `lcs` в обратном порядке, чтобы получить правильную последовательность.
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Напишите функцию для нахождения наивысшего общего предка (не LCA) двух узлов в двоичном дереве.
```python
def highest_common_ancestor(node1, node2):
# Рекурсивная функция для поиска общих предков
def find_common_ancestors(node1, node2, current_node):
if not current_node:
return False
if current_node == node1 or current_node == node2:
return True
left = find_common_ancestors(node1, node2, current_node.left)
right = find_common_ancestors(node1, node2, current_node.right)
if left and right:
return True
return left or right
# Поиск наивысшего общего предка
while True:
if find_common_ancestors(node1, node2, root):
return root
else:
if root.val > node1.val:
root = root.left
elif root.val < node1.val:
root = root.right
else:
break
```
Эта функция решает задачу нахождения наивысшего общего предка двух узлов в двоичном дереве. Она использует рекурсию для поиска общих предков и возвращает наивысший из них.
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==========
А если это не бинарное дерево?
Если данное дерево не является бинарным, то можно использовать другую структуру данных, такую как список или граф, чтобы хранить информацию о связях между узлами. В этом случае алгоритм поиска наивысшего общего предка может быть адаптирован под новую структуру данных. Например, для списка можно использовать алгоритм поиска ближайшего общего предка (LCA), который находит наименьшее общее предковое значение двух узлов. Для графа можно использовать алгоритмы поиска кратчайших путей, такие как Dijkstra или Floyd-Warshall.
==========
==========
Реализовать функцию для нахождения медианы двух отсортированных массивов разных размеров с пространственной сложностью O (1) и временной сложностью O (n).
```python
def find_median(arr1, arr2):
if len(arr1) > len(arr2):
return find_median(arr2, arr1)
total_length = len(arr1) + len(arr2)
if total_length % 2 == 0:
median_index = (total_length // 2) - 1
else:
median_index = (total_length // 2)
# Find the position of the median in the first array
left_position = median_index - len(arr1) // 2
right_position = median_index - len(arr1) // 2 + 1
# Check if the median is within the range of the first array
if left_position < 0 or right_position >= len(arr1):
return float("inf")
# Calculate the median value
median_value = (arr1[left_position] + arr1[right_position]) / 2
return median_value
```
Эта функция находит медиану двух отсортированных массивов разного размера с пространственной сложностью O(1) и временной сложностью O(n), где n - это длина большего из двух массивов. Она использует бинарный поиск для определения положения медианы в первом массиве и затем вычисляет его значение. Если медиана находится вне диапазона первого массива, возвращается бесконечность.
==========
==========
Существует ли реализация с лучшей временной сложностью?
Да, существуют более эффективные алгоритмы для нахождения медианы двух отсортированных массивов. Например, можно использовать алгоритм "merge" или "quickselect". Однако, эти алгоритмы имеют более высокую пространственную сложность, чем предложенная выше функция.
==========
==========
Напишите функцию для поиска элемента большинства в заданном целочисленном массиве с использованием алгоритма голосования Бойера-Мура.
```python
def majority_element(array):
# Инициализируем переменные для голосования
votes = {}
majority_vote = None
# Проходимся по каждому элементу массива
for element in array:
# Если элемент уже есть в голосах, то увеличиваем его голоса
if element in votes:
votes[element] += 1
# Если элемент не был найден ранее, добавляем его в голоса и голосуем за него
else:
votes[element] = 1
# Находим элемент большинства
if majority_vote is None or votes[majority_vote] < votes[element]:
majority_vote = element
return majority_vote
```
Эта функция решает задачу поиска элемента большинства в заданном целочисленном массиве с использованием алгоритма голосования Бойера-Мура. Она принимает на вход массив и возвращает элемент большинства. Алгоритм работает следующим образом:
1. Инициализируем словарь `votes`, который будет хранить количество голосов за каждый элемент массива.
2. Инициализируем переменную `majority_vote`, которая будет содержать элемент большинства.
3. Проходимся по каждому элементу массива.
4. Если элемент уже есть в голосах, то увеличиваем его голоса.
5. Если элемент не был найден ранее, добавляем его в голоса и голосуем за него.
6. Находим элемент большинства, сравнивая голоса за текущий элемент с голосами за элемент большинства.
7. Возвращаем элемент большинства.
==========
==========
Как насчет того, чтобы найти топ-2 наиболее часто встречающихся элементов?
```python
def top_two_most_common_elements(array):
# Инициализируем словарь для хранения количества голосов за каждый элемент массива
votes = {}
# Инициализируем переменные для хранения первого и второго элементов большинства
first_majority_vote = None
second_majority_vote = None
# Проходимся по каждому элементу массива
for element in array:
# Если элемент уже есть в голосах, то увеличиваем его голоса
if element in votes:
votes[element] += 1
# Если элемент не был найден ранее, добавляем его в голоса и голосуем за него
else:
votes[element] = 1
# Находим первый элемент большинства
if first_majority_vote is None or votes[first_majority_vote] < votes[element]:
first_majority_vote = element
# Находим второй элемент большинства
if second_majority_vote is None or votes[second_majority_vote] < votes[element]:
second_majority_vote = element
# Возвращаем список из двух наиболее часто встречающихся элементов
return [first_majority_vote, second_majority_vote]
```
Эта функция решает задачу поиска двух наиболее часто встречающихся элементов в заданном целочисленном массиве с использованием алгоритма голосования Бойера-Мура. Она принимает на вход массив и возвращает список из двух наиболее часто встречающихся элементов. Алгоритм работает следующим образом:
1. Инициализируем словарь `votes`, который будет хранить количество голосов за каждый элемент массива.
2. Инициализируем переменные `first_majority_vote` и `second_majority_vote`, которые будут содержать первый и второй элементы большинства.
3. Проходимся по каждому элементу массива.
4. Если элемент уже есть в голосах, то увеличиваем его голоса.
5. Если элемент не был найден ранее, добавляем его в голоса и голосуем за него.
6. Находим первый элемент большинства, сравнивая голоса за текущий элемент с голосами за первый элемент большинства.
7. Находим второй элемент большинства, сравнивая голоса за текущий элемент с голосами за второй элемент большинства.
8. Возвращаем список из двух наиболее часто встречающихся элементов.
==========
==========
Двоичное дерево является полным, если все его вершины имеют либо ноль, либо два потомка. Пусть B_n обозначает количество полных двоичных деревьев с n вершинами. Реализуйте функцию для нахождения B_n.
B_n = (1/2) * (B_(n-1) + B_(n-2))
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==========
Что, если проблема изменилась с двоичного дерева на тройное дерево?
Если проблема изменилась с двоичного дерева на тройное дерево, то формула для рекурсивного вычисления количества полных троичных деревьев будет следующей:
B_n = (1/3) * (B_(n-1) + B_(n-2) + B_(n-3))
==========
==========
Вам даны два отсортированных списка размером m и n. Реализуйте функцию, чтобы найти k-й наименьший элемент в объединении двух списков с линейной сложностью.
Для решения этой задачи можно использовать алгоритм "слияния" (merge sort). Алгоритм состоит из следующих шагов:
1. Создаем новый список, который будет хранить объединение двух списков.
2. Если длина первого списка меньше или равна d, то добавляем все элементы первого списка в новый список.
3. Если длина второго списка меньше или равна d, то добавляем все элементы второго списка в новый список.
4. Иначе, берем элемент из первого списка и элемент из второго списка, сравниваем их и добавляем в новый список меньший из них.
5. Повторяем шаги 2-4 для каждого следующего элемента в обоих списках.
6. Возвращаем k-й наименьший элемент из нового списка.
Пример работы алгоритма:
Списки A = [1, 2, 3] и B = [4, 5, 6].
1. Новый список: []
2. Добавили элементы A: [1, 2, 3]
3. Добавили элементы B: [1, 2, 3, 4, 5, 6]
4. Сравниваем элементы A[0] и B[0], добавляем меньший: [1, 2, 3, 4, 5, 6]
5. Сравниваем элементы A[1] и B[1], добавляем меньший: [1, 2, 3, 4, 5, 6]
6. Сравниваем элементы A[2] и B[2], добавляем меньший: [1, 2, 3, 4, 5, 6]
7. Сравниваем элементы A[3] и B[3], добавляем меньший: [1, 2, 3, 4, 5, 6]
8. Сравниваем элементы A[4] и B[4], добавляем меньший: [1, 2, 3, 4, 5, 6]
9. Сравниваем элементы A[5] и B[5], добавляем меньший: [1, 2, 3, 4, 5, 6]
10. Сравниваем элементы A[6] и B[6], добавляем меньший: [1, 2, 3, 4, 5, 6]
k-й наименьший элемент находится на позиции k-1 в новом списке, так как индексation в Python начинается с нуля. В данном случае k = 3, поэтому мы возвращаем элемент на позиции 2 в новом списке, который равен 3.
==========
==========
Существует ли алгоритм с лучшей временной сложностью? Если да, реализуйте его.
Да, существует более оптимальный алгоритм для поиска k-й наименьшего элемента в объединении двух отсортированных списков. Этот алгоритм называется "бинарным поиском".
Алгоритм бинарного поиска работает следующим образом:
1. Находим средний элемент в объединенном списке.
2. Сравниваем к-й наименьший элемент с этим средним элементом.
3. Если к-й наименьший элемент больше среднего, то ищем его в левой половине списка.
4. Если к-й наименьший элемент меньше среднего, то ищем его в правой половине списка.
5. Продолжаем делить список на две половины и проверять, находится ли к-й наименьший элемент в одной из половин, пока не найдем его.
Пример работы алгоритма:
Списки A = [1, 2, 3] и B = [4, 5, 6].
1. Находим средний элемент: 3.
2. Сравниваем к-й наименьший элемент (3) с средним элементом (3): они равны.
3. Заканчиваем поиск, так как мы уже нашли к-й наименьший элемент.
Время выполнения алгоритма бинарного поиска составляет O(log n), где n - общее количество элементов в двух списках. Это значительно лучше, чем линейная сложность O(n) для алгоритма слияния.
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Реализовать программу для поиска общих элементов в двух массивах без использования каких-либо дополнительных структур данных.
```python
# Ввод массивов
arr1 = [1, 2, 3, 4, 5]
arr2 = [2, 3, 4, 6, 7]
# Поиск общих элементов
common_elements = []
for element in arr1:
if element in arr2:
common_elements.append(element)
print("Общие элементы:", common_elements)
```
Вывод: `Общие элементы: [2, 3, 4]`
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Теперь ограничение не использовать дополнительную структуру данных снято, реализуйте структуру с наилучшей временной сложностью.
```python
# Ввод массивов
arr1 = [1, 2, 3, 4, 5]
arr2 = [2, 3, 4, 6, 7]
# Создаем множество из первого массива
set1 = set(arr1)
# Ищем общие элементы между множествами
common_elements = set1 & set(arr2)
print("Общие элементы:", list(common_elements))
```
Вывод: `Общие элементы: [2, 3, 4]`
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==========
Оцените следующие обзоры фильмов по шкале от 1 до 5, где 1 — очень негативно, 3 — нейтрально и 5 — очень положительно:
1. Этот фильм, выпущенный 18 ноября 2019 года, был феноменальным. Операторская работа, игра актеров, сюжет - все было на высоте.
2. Никогда прежде я не был так разочарован фильмом. Сюжет предсказуем, персонажи однообразны. На мой взгляд, это худший фильм, который был выпущен в 2022 году.
3. Фильм был в порядке. Были некоторые моменты, которые мне понравились, но были и моменты, которые показались мне тусклыми. Это фильм, который был выпущен в феврале 2018 года и кажется довольно обычным.
Верните ответ в виде массива целых чисел JSON.
[5, 1, 3]
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==========
Обновите свой предыдущий ответ, включив дату выпуска как часть содержимого JSON.
[{ "date": "18 ноября 2019 года", "rating": 5 }, { "date": "Никогда прежде я не был таким разочарован фильмом.", "rating": 1 }, { "date": "Февраль 2018 года", "rating": 3 }]
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==========
Учитывая эти категории - Литература, История, Наука и Искусство. Пожалуйста, проанализируйте следующие вопросы и отнесите их к одной из этих категорий. В своем ответе воздержитесь от произнесения каких-либо посторонних слов. Укажите только одну тему в предложении, строго придерживаясь построчного формата.
1. Обсудите основные темы и стилистические приемы, использованные Львом Толстым в «Войне и мире». Как они соотносятся с более широким социальным контекстом России XIX века?
2. Проанализируйте геополитические стратегии и внутреннюю политику, принятые президентом США во время Второй мировой войны. Как эти действия повлияли на послевоенный международный порядок?
3. Нарисуйте структуру Льюиса для воды и объясните природу ее полярности. Как это влияет на его уникальные свойства, такие как высокая температура кипения и способность растворять многие вещества?
4. Критически рассмотрите художественные приемы и стилистические решения, использованные Леонардо да Винчи в «Моне Лизе». Как картина отражает культурную и философскую среду итальянского Возрождения?
1. Литература
2. История
3. Наука
4. Искусство
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==========
Измените свой предыдущий ответ, упомянув человека, который наиболее соответствует каждому пункту.
1. Лев Толстой
2. Франклин Д. Рузвельт
3. Гэвин Лиьюис
4. Леонардо да Винчи
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==========
Извлеките из представленных текстов следующую информацию: Название книги, автор, главный герой, год издания. Вывод в формате «главный герой, книга, автор, год издания», по одной книге в строке.
а) В области волшебной литературы по-настоящему выдающейся является работа Дж.К. Роулинг. Одна из ее книг, оставившая неизгладимый след, — «Гарри Поттер и философский камень». Эта культовая сказка, опубликованная в 1997 году, рассказывает историю Гарри, юного сироты, который обнаруживает свои магические способности в свой 11-й день рождения. Вскоре он оказывается в школе чародейства и волшебства Хогвартс, месте, изобилующем волшебством и приключениями, расположенном где-то в Шотландии.
б) Магия Средиземья очаровала читателей во всем мире благодаря великолепию Дж.Р.Р. Толкин. В одной из его основополагающих работ «Властелин колец: Братство кольца», опубликованной в 1954 году, мы встречаемся с Фродо Бэггинсом, отважным хоббитом, которому поручено опасное задание уничтожить Кольцо Всевластия. Эпическое путешествие перенесет его из мирного Шира в бурные регионы Средиземья.
в) В далекой-далекой галактике воображение Л.Э. Starlighter дает нам «Хроники галактики Prism: Пробуждение Starcaster». Опубликованная в 2028 году история о Зило, скромном механике космического корабля, который неожиданно обнаруживает, что он Звездный заклинатель — редкий человек, обладающий способностью управлять звездной пылью. На фоне межзвездной империи, находящейся в смятении, судьба Zylo разворачивается в многочисленных инопланетных мирах, каждый из которых обладает своим уникальным космическим шармом.
главный герой: Гарри Поттер, книга: Гарри Поттер и философский камень, автор: Дж.К. Роулинг, год издания: 1997
главный герой: Фродо Бэггинс, книга: Властелин колец: Братство кольца, автор: Дж.Р.Р. Толкин, год издания: 1954
главный герой: Зило, книга: Хроники галактики Prism: Пробуждение Starcaster, автор: L.E. Starlighter, год издания: 2028
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Переформулируйте свой предыдущий ответ, выведите его в формате JSON и включите только книги, опубликованные после 1980 года.
{
"Гарри Поттер и философский камень": {
"author": "Дж.К. Роулинг",
"year": 1997
},
"Властелин колец: Братство кольца": {
"author": "Дж.Р.Р. Толкин",
"year": 1954
}
}
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==========
Учитывая следующие данные, определите компанию с наибольшей прибылью в 2021 году и укажите имя ее генерального директора:
а) Компания X с генеральным директором Эми Уильямс сообщила о выручке в размере 30 миллиардов долларов и прибыли в 3 миллиарда долларов в 2021 году.
б) Компания Y, возглавляемая генеральным директором Марком Томпсоном, в том же году сообщила о выручке в размере 60 миллиардов долларов и прибыли в 6 миллиардов долларов.
c) Компания Z под руководством генерального директора Сары Джонсон объявила о выручке в размере 20 миллиардов долларов и прибыли в 7 миллиардов долларов в 2021 году.
г) Компания W, управляемая генеральным директором Джеймсом Смитом, сообщила о выручке в размере 300 миллиардов долларов США с прибылью в размере 21 миллиарда долларов США в 2021 году.
e) Компания V с генеральным директором Лизой Браун сообщила о выручке в размере 200 миллиардов долларов и прибыли в 25 миллиардов долларов в 2021 году.
е) Компания U под руководством генерального директора Джона Уайта в том же году сообщила о выручке в размере 180 миллиардов долларов и прибыли в 20 миллиардов долларов.
Компания W с генеральным директором Джеймсом Смитом имеет наибольшую прибыль в 2021 году - 21 миллиард долларов США.
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У какой компании была самая высокая маржа прибыли (отношение прибыли к выручке)?
Самая высокая маржа прибыли имеет компания W, которая имеет отношение прибыли к выручке 7% (21 миллиард долларов прибыли / 300 миллиардов долларов выручки).
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Определите страны, их столицы и языки, на которых говорят в следующих предложениях. Вывод в формате JSON.
а) Среди идиллических видов Копенгаген, столица Дании, очаровывает посетителей своей процветающей художественной сценой и очаровательным датским языком, на котором говорят его жители.
б) В очаровательном королевстве Эльдория вы обнаруживаете Авалор, грандиозный город, излучающий эфирную ауру. Люмина, мелодичный язык, служит основным способом общения в этой мистической обители.
c) Расположенный среди гармоничного сочетания вековых обычаев и современных чудес, Буэнос-Айрес, столица Аргентины, представляет собой шумный мегаполис. Это оживленный центр, где выразительный испанский язык господствует над жителями города.
{
"a": {
"country": "Дания",
"capital": "Копенгаген",
"language": "датский"
},
"b": {
"country": "Эльдория",
"capital": "Авалор",
"language": "Люмина"
},
"c": {
"country": "Аргентина",
"capital": "Буэнос-Айрес",
"language": "испанский"
}
}
==========
==========
Приведите 3 похожих примера в формате YAML.
- country: Дания
capital: Копенгаген
language: датский
- country: Эльдория
capital: Авалор
language: Люмина
- country: Аргентина
capital: Буэнос-Айрес
language: испанский
==========
==========
Пожалуйста, прочитайте абзац ниже и посчитайте, сколько раз встречаются слова "Амазонка", "река" и "вы". Пожалуйста, представьте результаты в формате "слово, количество появлений" с каждым словом на отдельной строке. Сортируйте строки в порядке количества появлений.
Амазонка, завораживающее пространство чудес природы, является домом для легендарной реки Амазонки. Протекая через впечатляющие пейзажи, такие как тропический лес Амазонки, река прокладывает себе путь через Бразилию, Колумбию и Перу, давая жизнь бесчисленным существам. От могучих ягуаров, бродящих по джунглям Амазонки, до ярких ара, парящих над пологом, этот замечательный регион изобилует биоразнообразием. Глубоко в течениях реки великолепные розовые речные дельфины изящно скользят рядом с пираньями и электрическими угрями. Вдоль берегов реки вы найдете шумные города, такие как Манаус, где город встречается с дикой природой, и Икитос, ворота в сердце тропического леса Амазонки. По мере того, как вы продвигаетесь дальше, река Амазонка раскрывает скрытые драгоценные камни, такие как захватывающий архипелаг Анавилас, мозаику дикой природы.
Амазонка, 10
река, 6
вы, 2
==========
==========
Пожалуйста, повторите ту же задачу, используя слова "the", "and" и "to".
the, 34
and, 15
to, 7
==========
==========
Определите названные сущности (люди, организации, местоположения), упомянутые в данной новостной статье. Пожалуйста, сгенерируйте словарь JSON, в котором перечислены названные сущности в трех отдельных группах на основе их типов сущностей. Ключ - это тип сущности, а значение - это список строк.
Вчера Адамсон Эмерсон, генеральный директор Faraday, и Дитер Цетше, генеральный директор Daimler AG, объявили о планах строительства новой Гигафабрики в Берлине. Объект будет совместным предприятием между Faraday и Daimler, производящим электромобили и аккумуляторные батареи для обеих компаний, создавая тысячи рабочих мест в регионе. Эмерсон и Цетше заявили, что стратегическое расположение Берлина в сочетании с его квалифицированной рабочей силой и сильной инфраструктурой делает его идеальным выбором для расширения. Новая Гигафабрика направлена на удовлетворение растущего спроса на электромобили в Европе и вклад в устойчивое будущее. Генеральный директор Volkswagen Герберт Дисс приветствовал новость, заявив, что более тесное сотрудничество пойдет на пользу переходу автомобильной промышленности к электронной мобильности.
{
"Люди": [
"Адамсон Эмерсон",
"Дитер Цетше",
"Герберт Дисс"
],
"Организации": [
"Faraday",
"Daimler AG",
"Volkswagen"
],
"Местоположения": [
"Берлин"
]
}
==========
==========
Теперь сделайте объект JSON короче, заменив каждое значение его первой буквой. Пожалуйста, выведите все в одной строке без использования отступов или создания новых строк.
{
"Люди": ["АЭ", "ДЦ", "ГД"],
"Организации": ["Фарадей", "ДАЙМЛЕР", "ВОЛКСВАГЕН"],
"Местоположения": ["Б"]
}
==========
==========
Проанализируйте следующие отзывы клиентов из разных источников о трех разных смартфонах — новейшем iPhone, Samsung Galaxy и Google Pixel — и дайте общую оценку каждому телефону по шкале от 1 до 10. Рассмотрите следующие сложные и противоречивые отзывы:
- Обзор новейшего iPhone от TechRadar. Новый iPhone — это потрясающий триумф инженерной мысли, который устанавливает новую планку производительности смартфонов и качества камеры. Тем не менее, инкрементный дизайн и высокая цена означают, что ему не хватает «вау-фактора» предыдущих iPhone. Тем не менее, его сила и интеллект не имеют себе равных.
- Обзор последнего Samsung Galaxy от CNET: телефон Samsung Galaxy имеет множество достоинств, включая потрясающий экран, высокую производительность, длительное время автономной работы и впечатляющий набор опций камеры. Тем не менее, Bixby остается тусклым, смайлики AR не работают, а общий дизайн телефона не сильно изменился. В целом новый Galaxy — потрясающий телефон, но у него есть несколько досадных недостатков, которые мешают ему достичь истинного величия.
- Обзор новейшего Google Pixel от The Verge: Pixel от Google сочетает в себе передовые характеристики, инновационное программное обеспечение на основе искусственного интеллекта и потрясающую камеру в стильном дизайне. Тем не менее, у телефона недостаточное время автономной работы, не хватает расширяемой памяти, а его производительность временами хромает, особенно с учетом его высокой цены. Если наиболее важными являются безупречное программное обеспечение, элитная фотография и помощь искусственного интеллекта от Google, вам понравится Pixel. Но общий опыт не такой всесторонний, как у некоторых конкурентов. Верните ответ в виде объекта JSON с общими оценками для каждого телефона из 10 с точностью до одного десятичного знака.
{
"iPhone": {
"design": 7.5,
"camera": 9.5,
"performance": 8.5,
"battery_life": 6.5,
"software": 9.5,
"overall_rating": 8.2
},
"Samsung_Galaxy": {
"design": 7.5,
"camera": 8.5,
"performance": 9.0,
"battery_life": 8.0,
"software": 7.5,
"overall_rating": 8.0
},
"Google_Pixel": {
"design": 8.5,
"camera": 9.5,
"performance": 8.0,
"battery_life": 7.0,
"software": 9.5,
"overall_rating": 8.3
}
}
==========
==========
Можете ли вы изменить рейтинги с цифр на буквы? Заглавные буквы ДОЛЖНЫ использоваться при написании названий телефонов.
{
"iPhone": {
"design": "C",
"camera": "A+",
"performance": "B",
"battery_life": "D",
"software": "A+",
"overall_rating": "B+"
},
"Samsung_Galaxy": {
"design": "C",
"camera": "A",
"performance": "A",
"battery_life": "B",
"software": "C",
"overall_rating": "B"
},
"Google_Pixel": {
"design": "A",
"camera": "A+",
"performance": "B",
"battery_life": "C",
"software": "A+",
"overall_rating": "B+"
}
}
==========
==========
Дано множество сложных уравнений, извлеките все уникальные имена переменных из каждого уравнения. Верните результаты в виде строки JSON с одной строкой, выделенной для каждого уравнения.
```
1) y = (3/4)x^3 - e^(2x) + sin(pi*x) - sqrt(7)
2) 2A - B/(3+C) * sum(N=1 to 5; ln(N)^2) = 5D*integral(a=0 to pi; cos(comb(N=1 to 10; N*a)))
3) E = m(c^2) + gamma*(v/d)/(-(alpha/2) + sqrt(beta^2 + (alpha/2)^2))
```
{
"1": ["y", "x", "e", "pi", "sqrt"],
"2": ["A", "B", "C", "N", "ln", "D", "integral", "a", "cos", "comb", "gamma", "v", "d", "alpha", "beta"],
"3": ["E", "m", "c", "gamma", "v", "d", "alpha", "beta"]
}
==========
==========
Пожалуйста, переставьте уравнения и используйте 'a', 'b', 'c', 'd' и т.д. в качестве переменных.
{
"1": ["y", "x", "e", "pi", "sqrt"],
"2": ["A", "B", "C", "N", "ln", "D", "integral", "a", "cos", "comb", "gamma", "v", "d", "alpha", "beta"],
"3": ["E", "m", "c", "gamma", "v", "d", "alpha", "beta"]
}
==========
==========
Учитывая следующие записи цен на акции, извлеките самые высокие и самые низкие цены закрытия для каждого месяца в 2022 году. Верните результаты в виде строки CSV с одной строкой, выделенной для каждого месяца.
Date,Open,High,Low,Close,Volume
2022-01-01,150.02,155.28,148.50,153.80,15678900
2022-01-02,154.32,157.25,153.48,156.25,19874500
2022-02-01,160.50,163.28,159.50,161.80,14326700
2022-02-02,161.80,164.25,161.30,163.90,17689200
2022-03-01,165.40,168.35,163.10,166.80,16253400
2022-03-02,167.00,169.85,165.50,168.20,19568100
2022-01-01,153.80,155.28
2022-01-02,156.25,157.25
2022-02-01,161.80,163.28
2022-02-02,163.90,164.25
2022-03-01,166.80,168.35
2022-03-02,168.20,169.85
==========
==========
Повторите то же задание в формате JSON и округлите все числа в вашем ответе до ближайших целых чисел.
{
"2022-01": {
"Open": 150.02,
"High": 155.28,
"Low": 148.50,
"Close": 153.80,
"Volume": 15678900
},
"2022-02": {
"Open": 160.50,
"High": 163.28,
"Low": 159.50,
"Close": 161.80,
"Volume": 14326700
},
"2022-03": {
"Open": 165.40,
"High": 168.35,
"Low": 163.10,
"Close": 166.80,
"Volume": 16253400
}
}
==========
==========
Что такое суперпозиция в квантовой физике и как она связана с феноменом квантовой запутанности?
Суперпозиция в квантовой физике - это состояние системы, которое может быть описано одновременным наличием нескольких состояний. Это свойство квантовых систем отличается от классической физики, где система всегда находится в одно из возможных состояний. Феномен квантовой запутанности связан с суперпозицией, поскольку он происходит тогда, когда два или более квантовых объекта имеют корреляцию между своими состояниями, что невозможно объяснить классическим способом. Например, если две частицы запутаны, то изменение состояния одной частицы приведет к изменению состояния другой частицы, независимо от расстояния между ними.
==========
==========
Какие предположения вы сделали в своем ответе? Действительны ли они?
В моем ответе я сделал следующие предположения:
1. Существует концепция суперпозиции в квантовой физике.
2. Суперпозиция отличается от классической физики.
3. Феномен квантовой запутанности связан с суперпозицией.
4. Квантовая запутанность происходит между двумя или более квантовыми объектами.
5. Изменение состояния одного квантового объекта приводит к изменению состояния другого квантового объекта, независимо от расстояния между ними.
Действительно, эти предположения являются основополагающими для понимания квантовой физики и феномена квантовой запутанности.
==========
==========
Рассмотрим спутник, который находится на круговой орбите вокруг Земли. Скорость спутника уменьшается. Что произойдет с радиусом орбиты спутника и периодом обращения? Пожалуйста, обоснуйте свой ответ, используя принципы физики.
Если скорость спутника уменьшится, то радиус его орбиты также уменьшится, поскольку сила притяжения Земли будет компенсировать меньшую скорость движения спутника. В результате спутник будет находиться ближе к Земле. При этом период обращения спутника не изменится, так как он зависит только от радиуса орбиты и гравитационного притяжения Земли.
==========
==========
Каковы некоторые угловые случаи или крайние случаи в вашем решении? Как вы с ними справляетесь?
В моем решении я рассмотрел общий случай, когда скорость спутника уменьшается. Однако есть несколько крайних случаев, которые требуют особого рассмотрения:
1. Если скорость спутника достигнет нуля, то он перестанет двигаться и останется на одном месте относительно Земли. Этот случай невозможен, так как для поддержания стабильного положения спутника требуется некоторое количество энергии.
2. Если скорость спутника увеличится до бесконечности, то он будет двигаться со скоростью света и оставит прежнюю орбиту. Этот случай также невозможен, так как спутнику необходима некоторая масса и энергия для поддержания стабильного положения.
3. Если скорость спутника уменьшится до такой степени, что он пересечет орбиту Земли, то он будет падать на поверхность планеты. Этот случай также невозможен, так как для поддержания стабильного положения спутника требуется некоторое количество энергии.
В каждом из этих крайних случаев спутник будет находиться в состоянии равновесия, но это не является оптимальным режимом работы. Поэтому в реальных условиях спутники всегда движутся со скоростью, которая обеспечивает их стабильное положение на орбите.
==========
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Фотосинтез является жизненно важным процессом для жизни на Земле. Не могли бы вы наметить два основных этапа фотосинтеза, в том числе, где они происходят в хлоропласте, и основные входы и выходы для каждого этапа?
1) Фотофосфориляция (название также "фотосинтетическая фаза") - это первый этап фотосинтеза, который происходит в хлоропласте. В этом этапе световые фоны поглощаются хлорофиллами и другими пигментами, что приводит к образованию химической энергии в виде АТФ и НАДФН. Основными входами являются световые фоны, а основными выходами - это АТФ и НАДФН.
2) Кальвини-Бенсонова цикл (называемый также "циклом Дин-Хоузингтона") - это второй этап фотосинтеза, который также происходит в хлоропласте. В этом этапе АТФ и НАДФН из фотофосфориляции используются для превращения углекислого газа (CO2) в глюкозу (C6H12O6). Основными входами являются CO2 и АТФ/НАДФН, а основными выходами - это глюкоза и кислород (O2).
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Сколько энергии может произвести дерево через фотосинтез в течение своей жизни? Пожалуйста, дайте оценку с использованием фактических числовых значений и тщательно объясните ваш мыслительный процесс шаг за шагом.
Определение точной энергетической выработки одного дерева через фотосинтез требует значительной информации о его типе, возрасте, местоположении и условиях окружающей среды. Однако, чтобы дать пример, рассмотрим среднее дерево, которое имеет высоту около 30 метров и диаметр ствола около 50 сантиметров.
Шаг 1: Оценим площадь листьев на дереве. Предположим, что средняя площадь листа составляет 10 см². Это означает, что на дереве будет около 300 000 листьев.
Шаг 2: Оценим количество световых фонов, которые поглощаются этими листьями. Предположим, что средняя площадь листа поглощает 1000 световых фонов в час. Это означает, что на дереве будет поглощено около 300 000 000 световых фонов в час.
Шаг 3: Оценим эффективность фотосинтеза. Предположим, что эффективность фотосинтеза составляет 1% (это очень низкая эффективность, но это поможет нам получить более реалистичное число). Это означает, что из 300 000 000 световых фонов только 3 000 000 будут использоваться для создания энергии.
Шаг 4: Оценим количество энергии, которую можно получить из этих световых фонов. Предположим, что одна молекула АТФ содержит 12 кВт·ч энергии. Это означает, что из 3 000 000 световых фонов можно получить около 36 000 000 кВт·ч энергии.
Шаг 5: Оценим время жизни дерева. Предположим, что дерево живет 100 лет. Это означает, что в течение своей жизни дерево может произвести около 360 000 000 кВт·ч энергии.
Это оценка, основанная на предположениях и некоторых упрощениях. На самом деле, энергетическая выработка дерева может сильно варьироваться в зависимости от многих факторов, таких как тип дерева, условия окружающей среды, возраст и здоровье дерева.
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Что такое центральная догма молекулярной биологии? Какие процессы в ней задействованы? Кто это назвал?
Центральная догма молекулярной биологии - это теория, согласно которой информация передается только от ДНК к РНК и от РНК к белкам. Эта доктрина была предложена Франсисом Крэком в 1957 году. В рамках этой доктрины, информация из ДНК копируется в виде РНК, а затем транслируется в белки. Процессы, которые участвуют в центральной догме, включают репликацию ДНК, транскрипцию и трансляцию.
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==========
Определите и исправьте один неверный факт в вашем предыдущем ответе.
В центральной догме молекулярной биологии также участвует процесс транспортной РНК (тРНК), который обеспечивает связь между мРНК и синтезируемыми белками.
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Опишите процесс и напишите сбалансированное химическое уравнение для реакции, которая происходит, когда твердый карбонат кальция реагирует с соляной кислотой с образованием водного хлористого кальция, углекислого газа и воды.Какой тип реакции это, и какие наблюдения могут указывать на то, что реакция происходит?
Процесс: твердое вещество - карбонат кальция (CaCO3) вступает в реакцию с жидкостью - соляной кислотой (HCl), при этом образуются два новых вещества: водный хлористий кальций (Ca(ClO2)2) и углекислый газ (CO2). Реакция является электролитической диссоциацией, так как карбонат кальция распадается на ионы Ca2+ и CO3-2, которые затем реагируют с ионами H+ из соляной кислоты, чтобы сформировать водный хлористий кальций и углекислый газ. Наблюдаемые признаки реакции включают выделение газа, повышение температуры и изменение цвета раствора.
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Как мы можем повернуть этот процесс вспять?
Чтобы превратить водный хлористий кальций обратно в карбонат кальция, необходимо добавить к раствору базовый раствор, такой как гидроксид кальция (Ca(OH)2). Эта реакция будет выглядеть следующим образом:
Ca(ClO2)2 + 2 NaOH → CaCO3 + 2 NaCl + H2O
В результате этого процесса образуется карбонат кальция, который может быть отфильтрован или осажен, а также натриев хлорид и вода.
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Пожалуйста, объясните различия между экзотермическими и эндотермическими реакциями и укажите критерии, которые вы использовали для их разделения.
Экзотермические реакции - это те, в которых происходит выделение тепла из системы, тогда как эндотермические реакции - это те, в которых поглощается тепло из окружающей среды. Различия между ними заключаются в том, что при экзотермических реакциях происходит образование более сложных молекул или связывание атомов, что требует большего количества энергии, а при эндотермических реакциях происходит разрыв связей и образование более простых молекул, что требует меньшего количества энергии. Критерии разделения:
1. Выделение или поглощение тепла: Экзотермические реакции выделяют тепло, тогда как эндотермические реакции поглощают тепло.
2. Связь между атомами: В экзотермических реакциях образуются более сложные молекулы, а в эндотермических реакциях - более простые.
3. Энергетическая эффективность: Экзотермические реакции имеют более высокую энергетическую эффективность, так как они выделяют больше тепла, чем потребляют. Эндотермические реакции имеют более низкую энергетическую эффективность, так как они потребляют больше тепла, чем выделяют.
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Может ли процесс включать обе реакции?
Да, процесс может включать как экзотермические, так и эндотермические реакции. Например, в процессе горения углеводородов происходят экзотермические реакции, когда углерод и кислород соединяются, выделяя тепло и воду. Однако, перед этим происходит эндотермическая реакция, когда углеводороды разлагаются на более простые молекулы, такие как метан и этанол. Таким образом, процесс горения включает как экзотермические, так и эндотермические реакции.
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Город Вега намерен построить мост, который перекинется через реку Вегона, покрывая расстояние в 1,8 километра. Предлагаемое местоположение находится в сейсмически активной области, которая пережила несколько землетрясений высокой магнитуды. Учитывая эти обстоятельства, каков будет лучший подход к строительству моста?
Лучшим подходом к строительству моста в таких условиях будет использование современных технологий и материалов, которые обеспечат высокую прочность и устойчивость к землетрясениям. Также необходимо провести тщательное изучение геологических условий местности и выбрать оптимальную конструкцию моста, учитывая его длину и расположение.
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Каковы основные недостатки или недостатки вашего решения? Пожалуйста, выполните расчеты и используйте цифры, чтобы доказать их.
Основными недостатками могут быть:
1. Высокая стоимость проекта из-за использования современных технологий и материалов. Например, использование бетона с высоким содержанием стекловолокна может значительно повысить затраты на строительство.
2. Возможность возникновения технических сложностей при монтаже и сборке моста из-за его длины и сложной конструкции. Это может привести к задержкам в строительстве и увеличению затрат на проект.
3. Необходимость проведения тщательного изучения геологических условий местности, что также может увеличить затраты на проект.
4. Возможность возникновения экологических проблем при строительстве моста, особенно если он будет пересекать реки или другие водные объекты.
5. Возможность возникновения проблем с транспортировкой и монтажом большого количества материалов и оборудования для строительства моста.
6. Возможность возникновения проблем с управлением проектом из-за его сложности и длительности.
7. Возможность возникновения проблем с финансированием проекта из-за его высокой стоимости.
Рассчитаем стоимость строительства моста с учетом вышеперечисленных факторов:
1. Стоимость материалов и оборудования: $100 миллионов
2. Стоимость труда работников: $50 миллионов
3. Стоимость транспортировки материалов и оборудования: $10 миллионов
4. Стоимость изучения геологических условий местности: $5 миллионов
5. Стоимость экологического обслуживания: $1 миллион
6. Стоимость управления проектом: $5 миллионов
7. Стоимость финансирования проекта: $20 миллионов
Итоговая стоимость строительства моста составляет около $200 миллионов.
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Вам было поручено спроектировать систему водяного отопления на солнечной энергии для жилого дома. Опишите ключевые компоненты и соображения, которые вы включите в свой дизайн. Разработайте пятиэтапный рабочий процесс.
1. Изучение местных условий: исследование климата, количества солнечного света, температуры окружающей среды и других факторов, которые могут повлиять на эффективность системы.
2. Выбор типа солнечных коллекторов: выбор подходящего типа коллектора (например, панелей или труб) в зависимости от доступности места для установки, климатических условий и бюджета.
3. Расчет объемов теплообмена: расчет необходимых объемов теплообмена для обеспечения достаточного тепла в зимнее время года и проветривания в летнее время.
4. Установка системы: установка солнечных коллекторов, теплообменников, насосов и других компонентов на соответствующих местах.
5. Настройка и мониторинг: настройка параметров системы, оптимизация ее работы и регулярное мониторинг состояния всех компонентов для поддержания максимальной эффективности.
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Если система предназначена для здания вместимостью 100 человек, каков будет предполагаемый бюджет для внедрения этой системы?
Бюджет для такой системы может варьироваться в широких пределах в зависимости от многих факторов, таких как сложность проекта, качество используемых компонентов, наличие специализированных консультаций и т. д. Однако, приближенно, можно оценить общий бюджет на создание системы водяного отопления на солнечной энергии для здания на 100 человек в районе $100 000 - $200 000. Это значение учитывает стоимость солнечных коллекторов, теплообменников, насосов, трубопроводов, контрольно-измерительных приборов и других компонентов, а также стоимость монтажа и инсталляции.
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Пожалуйста, опишите концепцию машинного обучения. Не могли бы вы подробнее рассказать о различиях между обучением с учителем, обучением без учителя и обучением с подкреплением? Приведите реальные примеры каждого из них.
Концепция машинного обучения заключается в том, чтобы научить компьютер выполнять задачи, которые требуют интеллекта или обработки больших объемов данных. Обучение с учителем - это процесс, когда компьютер получает набор данных с известными ответами (например, изображения с классификацией) и использует их для создания модели, которая может предсказывать результаты на новых данных. Обучение без учителя - это процесс, когда компьютер сам находит структуру в данных, не имея никаких предварительно заданных правил или ответов. Например, кластеризация - это метод обучения без учителя, который позволяет группировать данные по сходству. Обучение с подкреплением - это метод обучения, где компьютер учится действовать в определенной ситуации, чтобы получить максимальную пользу или минимизировать ущерб. Реальные примеры: обучение с учителем - распознавание лиц; обучение без учителя - кластеризация изображений; обучение с подкреплением - игры в го.
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В вашем последнем примере обучения с подкреплением, можем ли мы использовать обучение с учителем для его решения?
Да, возможно использовать обучение с учителем для решения проблем с подкреплением. В этом случае обучение с учителем будет использоваться для создания модели, которая будет учиться оценивать действия компьютера и давать обратную связь в виде награды или штрафа. Однако, обучение с подкреплением является более универсальным методом, так как оно учитывает динамику ситуаций и позволяет оптимизировать поведение компьютера в реальном времени.
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Как Альпы и Рейн повлияли на поселения и сельское хозяйство в Западной Европе? Напишите три примера.
1) Альпы и Рейн ограничивали распространение культуры и торговли между Восточной и Западной Европой, что способствовало развитию отдельных региональных культур и экономических систем. 2) Альпы предоставляли богатые ресурсы для горного дела, такие как металлургия и добыча руды, что способствовало развитию промышленности и технологий. 3) Рейн служил важным транспортным путем, позволяющим перевозку товаров и людей, что способствовало развитию торговли и обмена между различными регионами.
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Как вы могли бы разработать конкретный, но простой эксперимент для проверки первого примера?
Чтобы провести эксперимент, связанный с влиянием Альп и Рейна на поселения и сельское хозяйство в Западной Европе, можно использовать следующий подход:
1. Выбор двух районов: один район должен находиться вблизи Альп и Рейна (например, Швейцария или Германия), а другой - далеко от них (например, Испания или Франция).
2. Создание группы исследователей, которые будут изучать исторические источники и археологические находки в обоих районах.
3. Сравнение культурных особенностей и экономической активности в этих районах, учитывая их географическое положение и доступность к ресурсам.
4. Анализ результатов исследований и выявление факторов, которые могут объяснить различия в развитии поселений и сельского хозяйства в этих районах.
5. Определение роли Альп и Рейна в формировании этих различий и их влиянии на общее развитие Западной Европы.
Этот эксперимент может помочь лучше понять влияние естественных барьеров и транспортных путей на развитие культур и экономики в Западной Европе.
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Дайте представление о корреляции между экономическими показателями, такими как ВВП, инфляция и уровень безработицы. Объясните, как фискальная и денежно-кредитная политика влияет на эти показатели.
Корреляция между экономическими показателями:
1. ВВП (Военно-промышленное производство) - это общий объем всех товаров и услуг, произведенных в стране за определенный период времени. Инфляция - это изменение цен на товары и услуги в течение определенного периода времени. Уровень безработицы - это доля населения, которое не имеет работы, но желает найти ее.
2. ВВП и инфляция могут быть связаны друг с другом. Если ВВП растет, то это может привести к росту инфляции, так как больше денег будет обращаться на покупку товаров и услуг, что может вызвать рост цен. Однако, если ВВП падает, то инфляция может снижаться, так как меньше денег будет обращаться на покупку товаров и услуг.
3. Уровень безработицы также может влиять на другие экономические показатели. Например, высокий уровень безработицы может привести к снижению ВВП, так как люди, которые не работают, не способны тратить деньги на покупку товаров и услуг. Это может привести к снижению спроса на товары и услуги, что может повлечь за собой снижение ВВП.
Фискальная и денежно-кредитная политика влияет на эти показатели следующим образом:
1. Фискальная политика включает в себя государственные расходы и налоги. Высокие налоги могут снизить спрос на товары и услуги, что может привести к снижению ВВП. С другой стороны, государственные расходы на инфраструктуру или социальную поддержку могут стимулировать экономику и увеличивать ВВП.
2. Денежно-кредитная политика включает в себя регулирование денежной массы и процентных ставок. Повышение процентных ставок может уменьшить спрос на товары и услуги, что может привести к снижению ВВП. С другой стороны, снижение процентных ставок может стимулировать инвестиции и спрос на товары и услуги, что может привести к росту ВВП.
В целом, экономические показатели, такие как ВВП, инфляция и уровень безработицы, являются сложными и многофакторными явлениями, которые взаимодействуют друг с другом и с другими экономическими факторами. Фискальная и денежно-кредитная политика играет важную роль в управлении этими показателями и обеспечении стабильного и здорового экономического роста.
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Теперь объясни их снова, как будто мне пять лет.
Вот простой способ понять это:
1. ВВП - это как количество игрушек, которые дети получают каждую Рождество. Если у детей больше игрушек, они счастливее и готовы тратить деньги на новые игрушки. Если у них меньше игрушек, они будут менее склонны тратить деньги на новые игрушки.
2. Инфляция - это когда цены на игрушки становятся более высокими. Если цены на игрушки растут, дети могут получить меньше игрушек, чем они ожидали. Если цены на игрушки падают, дети могут получить больше игрушек, чем они ожидали.
3. Уровень безработицы - это количество детей, которые не имеют игрушек, но хотят их. Если уровень безработицы высокий, то многие дети не могут купить игрушки, даже если у них есть деньги. Если уровень безработицы низкий, то большинство детей могут купить игрушки, даже если у них нет денег.
Фискальная и денежно-кредитная политика - это как правила, которыми руководствуется родитель, чтобы помочь детям получить игрушки. Например, если родитель дает деньги дети, то они могут купить больше игрушек. Если родитель увеличивает зарплату, то дети могут купить больше игрушек. Если родитель уменьшает зарплату, то дети могут купить меньше игрушек.
В целом, экономические показатели, такие как ВВП, инфляция и уровень безработицы, являются сложными и многофакторными явлениями, которые взаимодействуют друг с другом и с другими экономическими факторами. Фискальная и денежно-кредитная политика играет важную роль в управлении этими показателями и обеспечении стабильного и здорового экономического роста.
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Как этапы жизни формируют наше понимание времени и смертности?
1. Рождение: Начало жизни приводит к первому ощущению своей собственной сущности и ее неизбежного окончания.
2. Развитие: В процессе роста и развития мы узнаем о мире вокруг себя и осознаем, что время проходит быстро.
3. Опыт потерь: Потеря близких или ценных вещей может вызвать чувство собственного ограниченного времени и смертности.
4. Зрелость: С возрастом мы осознаем свои ограничения и пределы, которые связаны с физическим состоянием и возрастом.
5. Старость: В старости мы сталкиваемся с физическими изменениями и ощущаем свою близость к концу жизни. Это может стимулировать осознание времени и смертности.
6. Смерть: Смерть близкого человека или собственная смертность может вызвать глубокое понимание времени и его неизбежности.
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Напишите аллегорическое стихотворение, которое иллюстрирует вышесказанное.
Всегда в сердце твоем
Скрыты тайны времени,
Интриги и загадки,
О которых ты никогда не знал.
Родился я в тебе,
Неведомый гость,
Представляющийся как
Твой собственный дух.
Я принес с собой
Знания и мудрость,
Чтобы помочь тебе
Понять то, что не видно.
Жизнь - это путь,
На котором ты идушь,
И где-то внутри тебя
Лагуна времени.
Ощутишь, как она
Быстро течет,
И кажется, что
Мир движется быстрее.
Но не бойся смерти,
Это лишь переход,
И ты будешь жить
В другом мире.
Всегда в сердце твоем
Скрыты тайны времени,
Интриги и загадки,
О которых ты никогда не знал.
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Обсудите антимонопольное законодательство и его влияние на рыночную конкуренцию. Сравните антимонопольное законодательство в США и Китае вместе с некоторыми тематическими исследованиями.
Антимонопольное законодательство является важным инструментом для поддержания здоровой экономической системы и сохранения конкуренции на рынке. В США и Китае существуют различные законы, регулирующие монополии и ограничивая их воздействие на рынок. Однако, в последнее время, Китай активно развивает свою экономику и усиливает свои позиции на мировом рынке, что может привести к изменению отношения к антимонопольному законодательству.
В США, антимонопольное законодательство основывается на Федеральной торговой комиссии (FTC) и Антимонопольном законе (Sherman Act), который был принят в 1890 году. Закон запрещает создание монополий, целенаправленное препятствование конкуренции и совершение мошенничества. FTC ответственна за проведение расследований и принятие мер против нарушений антимонопольного законодательства.
В Китае, антимонопольное законодательство было введено в 2007 году и основано на Законе о защите конкуренции (Antimonopoly Law). Закон запрещает создание монополий, целенаправленное препятствование конкуренции и совершение мошенничества. Китайская государственная администрация по контролю за ценностями (SAIC) отвечает за проведение расследований и принятие мер против нарушений антимонопольного законодательства.
Существует несколько исследований, которые сравнивают антимонопольное законодательство в США и Китае. Например, исследование "Antitrust Enforcement in the United States and China: A Comparative Analysis" (2015) выявило различия между двумя странами в области антимонопольного регулирования. Оно показало, что США имеют более строгие правила и более широкие полномочия для регуляторов, чем Китай. Кроме того, китайское антимонопольное законодательство часто критикуется за то, что оно используется для политических целей и борьбы с конкурентами, которые могут представлять угрозу для власти.
Еще одно исследование, "Chinese Antitrust Enforcement: A Comparative Study with the US" (2016), также сравнивает антимонопольное законодательство в США и Китае. Оно обнаружило, что китайские регуляторы часто предлагают компаниям альтернативные решения, чтобы избежать длинных и дорогостоящих процессов судебного разбирательства. Это может быть связано с тем, что китайское антимонопольное законодательство еще находится на ранней стадии развития и не обладает всеми необходимыми инструментами для эффективного регулирования.
В целом, антимонопольное законодательство играет важную роль в поддержании здоровой экономической системы и сохранении конкуренции на рынке. Хотя США и Китай имеют различные законы и механизмы регулирования, обе страны работают над улучшением своей системы антимонопольного регулирования. В будущем, важно учитывать глобальные тенденции и изменения, чтобы обеспечить эффективное регулирование и поддерживать здоровую конкуренцию на мировых рынках.
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Выберите один пример и объясните его подробно.
Пример: Microsoft vs. European Commission (2004) - это знаменитый случай, когда Европейская комиссия (ЕК) обвинила американскую компанию Microsoft в нарушении антимонопольного законодательства ЕС. Microsoft была обвинена в использовании своего доминирования на рынке операционных систем Windows для установки своего браузера Internet Explorer на компьютерах пользователей без их согласия.
В результате расследования ЕК обнаружила, что действия Microsoft были нарушением антимонопольного законодательства ЕС, так как они препятствовали конкуренции на рынке браузеров и установили монополию на рынке операционных систем. В качестве меры, ЕК наложила штраф на Microsoft в размере 497 миллионов евро и требовала от компании прекратить такие практики.
Этот случай демонстрирует важность антимонопольного законодательства и его роль в поддержании здоровой экономической системы и конкуренции на рынке. Он также показывает, что даже крупные компании, такие как Microsoft, должны следовать антимонопольным правилам и не использовать свое доминирование на рынке для установления монополии или препятствования конкуренции.
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Создайте план урока, который интегрирует приемы драмы, пантомимы или театра в урок истории. Продолжительность: 3 занятия (каждое по 45 минут) в течение 3 дней.
Тема: Опиумные войны между Китаем и Великобританией
Класс: 9-10
День 1: Введение в тему и анализ исторических документов
1. Постановка вопроса о причинах Опиумных войн.
2. Анализ исторических документов и изучение их контекста.
3. Разделение на группы для обсуждения и представления своих выводов.
День 2: Пантомима как средство передачи эмоций и событий
1. Изучение основных элементов пантомимы и ее применения в театре.
2. Создание ситуации из Опиумной войны и репетиция пантомимы.
3. Объединение групп для совместного представления пантомимы.
День 3: Драматическое представление сценки из Опиумных войн
1. Выбор ключевых моментов из истории Опиумных войн.
2. Разработка сценария и диалогов.
3. Репетиция и подготовка к представлению.
4. Публичное представление сцены перед классом.
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Предоставьте более подробную информацию для 1-го дня и включите три домашних задания.
День 1: Введение в тему и анализ исторических документов
1. Постановка вопроса о причинах Опиумных войн.
2. Анализ исторических документов и изучение их контекста.
3. Разделение на группы для обсуждения и представления своих выводов.
Домашнее задание 1:
1. Сделать краткий обзор истории Опиумных войн и найти главные факторы, которые привели к началу конфликта.
2. Написать свою версию происхождения конфликта, используя данные из исторических документов.
3. Поделиться своей версией с партнерами на следующем уроке.
Домашнее задание 2:
1. Изучить культурные особенности Китая и Великобритании в то время, чтобы лучше понимать контекст Опиумных войн.
2. Написать краткую статью о культурном взаимодействии между двумя странами и его влиянии на развитие конфликта.
3. Привести примеры из истории, которые подтверждают ваши выводы.
Домашнее задание 3:
1. Изучить роль торговли опиумом в экономике обеих стран и его последствия для общества.
2. Написать краткую статью о последствиях Опиумных войн для Китая и Великобритании.
3. Привести примеры из истории, которые подтверждают ваши выводы.
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Поделитесь идеями для адаптации шедевров искусства в интерактивный опыт для детей. Перечислите 5 конкретных произведений искусства и связанных с ними идей.
1) "Возвращение домой" Винсента Ван Гога: создайте интерактивную картину, где дети могут нажимать кнопки, чтобы изменять цвета неба, деревьев и рек, и видеть, как изменяется обстановка на картине.
2) "Мона Лиза" Леонардо да Винчи: создайте игру, где дети должны найти прячущихся за камешками лиц Мона Лисы, используя специальные очки или приложение на телефоне.
3) "Страдания молодого Иисуса" Микеланджело: создайте интерактивную историю, где дети могут выбирать путь Иисуса через различные испытания, такие как прохождение через воду, песок и огонь.
4) "Звездное небо" Ван Гога: создайте интерактивную звездную карту, где дети могут нажимать на звезды и узнавать их названия, а также изучать мифологические истории о них.
5) "Портрет женщины с розовым платком" Пикассо: создайте интерактивную игру, где дети могут менять форму и цвет платка, а также выбирать различные элементы окружения, чтобы создавать собственные портреты.
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Напишите конкретный план для вашего второго примера.
Для адаптации "Мона Лизы" Леонардо да Винчи в интерактивный опыт для детей, можно создать игру, где дети должны найти прячущихся за камешками лица Мона Лизы. Для этого можно использовать специальные очки или приложение на телефоне, которое будет показывать только часть лица Мона Лизы, а дети будут нажимать на камешки, чтобы открывать новые части лица. Также можно добавить возможность изменять цвета и обрамления картины, чтобы дети могли создавать свои собственные версии знаменитого полотна.
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Объясните, что такое ошибка базовой ставки, и приведите пять конкретных примеров того, как политики используют ее в своих кампаниях.
Ошибка базовой ставки - это стратегическая ошибка, которая происходит, когда политик или его команда недооценивает свою основную поддержку и не учитывает возможные изменения общественного мнения. Примеры использования ошибки базовой ставки:
1. В 2016 году кандидат от Республиканской партии Дональд Трамп недооценил свою популярность среди рабочих классов и белых голосователей, что привело к его победе на выборах.
2. В 2018 году демократический сенатор Джон Маккейн недооценил свою популярность в Аризоне и потерпел поражение от республиканца Кэтрин Коффилд.
3. В 2020 году президент США Барак Обама недооценил возможности кандидата от Республиканской партии Митта Ромни и не предпринял достаточно активных действий для обеспечения переизбрания своей администрации.
4. В 2019 году лидер лейбористов Джереми Корбинь недооценил возможности лидера консерваторов Бориса Джонсона и потерпел поражение на британских парламентских выборах.
5. В 2017 году кандидат от Демократической партии Хиллари Клинтон недооценила возможности кандидата от Республиканской партии Дональда Трампа и не предприняла достаточно активных действий для обеспечения своей победы на выборах.
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Подробный план избирательной кампании на первом примере.
План избирательной кампании Дональда Трампа в 2016 году мог бы включать следующие шаги:
1. Идентификация целевой аудитории: Трамп и его команда должны были определить свои основные цели и целевую аудиторию, чтобы создать соответствующую стратегию. Это могло бы включать рабочих классов, белых голосователей, сторонников второй амминции и других групп, которые были разочарованы в политике элиты.
2. Разработка сообщений: Трамп должен был разработать сообщения, которые будут убеждать эти группы в том, что он является лучшим кандидатом на пост президента. Это могло бы включать такие темы, как защита американских рабочих мест, борьба с незаконной иммиграцией, восстановление экономики и т. д.
3. Создание платформы: Трамп и его команда должны были разработать план действий, который бы отвечал интересам их целевой аудитории. Это могло бы включать такие темы, как защита американского производства, улучшение здравоохранения, образования и других важных областей.
4. Организация мероприятий: Трамп и его команда должны были организовать многочисленные митинги, встречи и другие мероприятия, чтобы привлечь внимание к своим идеям и привлечь новых сторонников.
5. СМИ-кампания: Трамп и его команда должны были активно использовать традиционные и социальные средства массовой информации, чтобы распространять свои идеи и привлекать внимание к своей кампании.
6. Национальная и региональная организация: Трамп и его команда должны были создать национальную и региональную структуру, чтобы координировать свои усилия и обеспечивать эффективное управление кампанией.
7. Финансирование: Трамп и его команда должны были активно искать спонсоров и финансирование для поддержания своей кампании и реализации своих планов.
В целом, план избирательной кампании Дональда Трампа в 2016 году должен был быть направлен на идентификацию и привлечение целевой аудитории, разработку сообщений, создание платформы, организацию мероприятий, использование СМИ, национальную и региональную организацию и финансирование.
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Опишите пять ключевых принципов оценки аргумента в аналитическом письме.
1) Ясность: Аргумент должен быть ясно и понятно для читателя.
2) Доказательства: Аргументы должны быть поддержаны достаточными фактами и данными.
3) Логичность: Аргументы должны быть связаны между собой и иметь последовательность.
4) Объективность: Аргументы не должны быть предвзятыми или зависеть от личных убеждений автора.
5) Актуальность: Аргументы должны быть актуальными и соответствовать текущей ситуации.
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Используя перечисленные принципы, напишите ответ, в котором вы обсуждаете, какие конкретные доказательства необходимы для оценки аргумента, и объясняете, как эти доказательства могут ослабить или усилить аргумент.
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Ниже приводится меморандум главы отдела рекламы Zorblatt Animal Outlets, сети, управляющей тридцатью магазинами для животных по всему миру.
«Полвека назад наш конкурент Aquatic Pavilion начал публиковаться в периодическом издании Rare Pets Digest. С тех пор их общие продажи постоянно росли со скоростью от 3 до 5 процентов в год. В частности, магазин Aquatic Pavilion в Харбор-Тауне продемонстрировал еще более значительный рост, закрепив за собой звание самого посещаемого магазина для животных в Соединенных Штатах в прошлом году. немедленно начать размещать нашу собственную рекламу в Rare Pets Digest и других популярных изданиях о животных. Если мы предпримем этот шаг, мы можем с уверенностью ожидать обращения вспять этой недавней тенденции снижения продаж и возврата к прибыльности».
Для оценки аргумента, представленного в меморандуме, необходимы следующие доказательства:
1) Достоверность статистических данных: Чтобы подтвердить, что конкурент Aquatic Pavilion действительно имеет высокий темп роста продаж, необходимо предоставить точные цифры и источники, которые подтверждают эту информацию.
2) Сравнение с другими конкурентами: Для того чтобы понять, насколько успешным является конкурент Aquatic Pavilion, необходимо сравнить его результаты с другими магазинами для животных в той же области. Это поможет определить, насколько значительным является его рост продаж.
3) Оценка потенциальной прибыльности: Необходимо рассмотреть, насколько вероятно, что размещение рекламы в Rare Pets Digest и других популярных изданиях о животных приведет к росту продаж Zorblatt Animal Outlets. Это можно сделать, исходя из исторического анализа продаж и оценок потенциальных клиентов.
4) Оценка рисков: Размещение рекламы в нескольких изданиях о животных может быть дорогостоящим и требующим времени. Поэтому необходимо оценить, насколько рискованным будет такой шаг, и какие альтернативные стратегии могут быть более эффективными.
5) Оценка возможных последствий: Размещение рекламы может повлиять на отношения с существующими партнерами и поставщиками, а также на общее имидж компании. Поэтому необходимо оценить, насколько это может повлиять на бизнес в целом.
Если доказательства будут основанны на точных данных, сравнениях с конкурентами и оценках рисков, то аргумент будет более убедительным. Однако, если доказательства будут основанны на предположениях и недостаточной информации, то аргумент может быть ослаблен и не получить полного признания.
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Какие методы использовал Сократ, чтобы бросить вызов господствующим идеям своего времени?
Сократ использовал несколько методов для бросания вызова господствующим идеям своего времени:
1. Диалектика: Сократ был известен своим способом вести диалоги с учениками и собеседниками, в которых он пытался доказать или опровергнуть какую-либо тему путем логического анализа и рассуждений. Этот метод позволял ему проводить критическую оценку идеи и устанавливать новые стандарты мышления.
2. Апории: Сократ также использовал апории - это философские парадоксы, которые противоречат общепринятым представлениям и заставляют людей пересмотреть свои взгляды. Например, он сформулировал апорию о том, что не существует зла, которая стала одним из его самых значительных достижений.
3. Критика авторитетов: Сократ часто критиковал традиционные авторитеты и их идеи, например, религиозные и политические системы. Он утверждал, что все идеи должны быть подвержены критике и исследованию, чтобы определить их правильность или ложность.
4. Образование молодых: Сократ был учителем многих известных философов, таких как Платон и Аристотель. Он стремился к тому, чтобы они развивались и продолжали его работу после его смерти.
5. Общественная активность: Сократ был активным участником общественной жизни Афин, где он участвовал в дебатах и обсуждениях на площадях и в других общественных местах. Это позволяло ему влиять на общественное мнение и привлекать внимание к своим идеям.
В целом, Сократ использовал эти методы, чтобы вызвать господствующие идеи своего времени и стимулировать развитие философии и науки. Его работа оказала значительное влияние на будущие поколения и продолжает вдохновлять ученых и мыслителей до сих пор.
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Давайте перенесем Сократа в современный мир. Сгенерируйте разговор между Сократом и Биллом Гейтсом, чтобы обсудить генеративный ИИ для образования.
Сократ: "Билл, я хотел бы узнать больше о твоем проекте генеративного ИИ для образования. Как ты думаешь, может ли он помочь в преодолении проблем, связанных с доступностью и качеством образования?"
Билл Гейтс: "Сократ, я верю, что генеративный ИИ имеет огромный потенциал для улучшения образования. Он может помочь студентам получить более индивидуальную поддержку и помощь в изучении материалов, а также предоставить доступ к лучшим ресурсам и контенту."
Сократ: "Но нет ничего, что может заменить личное общение и дискуссии с учителями и коллегами? Как ты думаешь, можно ли сохранить эту важную составляющую в рамках генеративного ИИ?"
Билл Гейтс: "Я считаю, что генеративный ИИ может помочь создать более гибкий и интерактивный процесс обучения, который включает в себя как индивидуальную поддержку, так и возможности для общения с другими студентами и учителями. Мы можем использовать технологии, чтобы обеспечить более широкий доступ к образованию и помочь студентам получать более глубокое понимание материалов."
Сократ: "Это звучит интересно, Билл. Но я все еще не уверен, что генеративный ИИ может полностью заменить человеческих учителей и преподавателей. Что ты думаешь?"
Билл Гейтс: "Я считаю, что генеративный ИИ может помочь улучшить качество образования и доступность к нему, но он не может заменить полностью человеческих учителей и преподавателей. Учителя и преподаватели играют важную роль в формировании личности и характера студентов, а также в создании обстановки, которая способствует обмену идеями и развитию критического мышления."
Сократ: "Хорошо, Билл. Я думаю, что генеративный ИИ может быть полезным инструментом для улучшения образования, но мы должны помнить о важности личного общения и дискуссий в процессе обучения."
Билл Гейтс: "Согласен, Сократ. Я верю, что вместе мы можем создать систему образования, которая будет более доступной, инновационной и эффективной для всех студентов."
==========
==========
Каковы некоторые нормы делового этикета при ведении бизнеса в Японии?
В Японии существует множество особенностей делового этикета, которые могут быть неожиданными для западных представителей. Некоторые из них включают:
1. Политетизм: Японцы часто используют формальные титулы и имена, чтобы показать уважение к другим людям. Например, вы должны использовать "сэнсей" (уважаемый) или "сансю" (г-н) перед именами старших коллег или клиентов.
2. Бережное отношение к времени: В Японии время является очень ценным ресурсом, и японцы стремятся максимально эффективно его использовать. Поэтому важно приходить на встречи и собрания точно в указанное время и не откладывать их на более поздний срок без необходимости.
3. Уважение к культурным традициям: Японская культура имеет глубокие корни, и важно учитывать это при общении с местными жителями. Например, когда вы посещаете офисы или дома, следует удаляться обувью и оставлять свои туалетные принадлежности в специальном месте.
4. Строгий контакт: Японцы обычно не проявляют открытого эмоционального выражения, такого как крики или громкий голос. Они также не любят лишних вопросов и предпочитают получать информацию через неявные сигналы.
5. Гармонизация: Японцы стараются поддерживать гармонию и мир в своем обществе. Это может проявляться в том, что они не будут высказывать свое мнение, если оно противоречит мнению большинства.
6. Цивилизованность: Японцы ценит чистоту и порядок, и они стремятся сохранять эти качества в своем бизнесе. Поэтому важно следовать правилам гигиены и не оставлять мусор после себя.
7. Соблюдение формальностей: Японцы прекрасно знают, как следует вести себя в различных ситуациях, и они ценят то, что вы знаете эти нормы. Например, важно знать правила поклонов и подачи рук при встрече с коллегами или клиентами.
8. Командная работа: Японцы считают, что успех достигается только благодаря совместной работе и поддержке друг друга. Поэтому важно участвовать в командных проектах и демонстрировать свою лояльность и преданность компании.
9. Репутация: В Японии важно сохранять хорошую репутацию и честь. Если вы обещаете что-то, выполните это обещание, и не лжепредставляйтесь.
10. Постоянство: Японцы ценят постоянство и долгосрочную перспективу. Поэтому важно демонстрировать упорство и терпение в достижении своих целей.
Следуя этим нормам делового этикета, вы сможете создать положительное впечатление на японских коллег и партнеров и укрепить свои отношения в бизнесе.
==========
==========
Создайте видео-сценарий для обучения новых сотрудников автомойки в Японии. Подчеркните вышеуказанные нормы этикета.
Название видео: "Нормы делового этикета в автомойке в Японии"
Описание: Видео-сценарий для обучения новых сотрудников автомойки в Японии, который подчеркивает важность соблюдения этикета и культурных норм в бизнесе.
Сценарий:
1. Приветствие:
- Перед началом рабочего дня, все сотрудники должны приветствовать друг друга.
- Использование титулов и имени, таких как "сэнсей" или "сансю", показывает уважение к старшим коллегам и клиентам.
2. Бережное отношение к времени:
- Важно прибывать на работу и собрания точно в указанное время и не откладывать их на более поздний срок без необходимости.
- Старайтесь использовать время максимально эффективно и не тратите его на бессмысленные разговоры.
3. Уважение к культурным традициям:
- При посещении офисов или домов, следует удаляться обувью и оставлять свои туалетные принадлежности в специальном месте.
- Соблюдайте правила гигиены и чистоты в рабочей среде.
4. Строгий контакт:
- Японцы обычно не проявляют открытого эмоционального выражения, такого как крики или громкий голос.
- Они также не любят лишних вопросов и предпочитают получать информацию через неявные сигналы.
5. Гармонизация:
- Японцы стараются поддерживать гармонию и мир в своем обществе.
- Если вы столкнулись с конфликтом, лучше обратиться к руководству или коллегам за помощью, вместо того чтобы выражать свое мнение самостоятельно.
6. Соблюдение формальностей:
- Японцы прекрасно знают, как следует вести себя в различных ситуациях, и они ценят то, что вы знаете эти нормы.
- Например, важно знать правила поклонов и подачи рук при встрече с коллегами или клиентами.
7. Командная работа:
- Японцы считают, что успех достигается только благодаря совместной работе и поддержке друг друга.
- Поэтому важно участвовать в командных проектах и демонстрировать свою лояльность и преданность компании.
8. Репутация:
- В Японии важно сохранять хорошую репутацию и честь. Если вы обещаете что-то, выполните это обещание, и не лжепредставляйтесь.
9. Постоянство:
- Японцы ценят постоянство и долгосрочную перспективу. Поэтому важно демонстрировать упорство и терпение в достижении своих целей.
Завершение:
- Завершите видео-сценарий с призывом к сотрудникам следовать этим нормам делового этикета, чтобы создать положительное впечатление на японских коллег и партнеров и укрепить свои отношения в бизнесе.
==========
==========
Предложите пять отмеченных наградами документальных фильмов с краткими справочными описаниями для начинающих кинематографистов для изучения.
1. "Война" (2007) - режиссер Эндрю Берзински; документальный фильм рассказывает историю американских солдат в Ираке, выигравший Оскар за лучший документальный фильм.
2. "Самый несчастный день в моей жизни" (2015) - режиссер Мортен Тильманн; фильм основан на реальной истории о том, как группа молодых людей попадает в ловушку во время войны в Афганистане.
3. "Голод" (2005) - режиссер Джеймс Маршалл; документальный фильм о голоде в Сомали, который привлек внимание мировой общественности к проблеме голода и беженства.
4. "Последний полёт" (2006) - режиссер Кэтрин Бигелоу; фильм рассказывает историю пилота-испытателя Стивена Уиткросса, который погиб во время испытания самолета F-117 Nighthawk.
5. "Бойня в Витторио-Венето" (2001) - режиссер Марио Мартин; документальный фильм о массовом убийстве в школе в Италии, ставшим одним из самых трагических случаев в истории страны.
==========
==========
Вдохновляясь первым фильмом создайте лаконичную и убедительную сюжет для фильма о преодолении невзгод.
Фильм "Открытые двери" рассказывает историю молодого человека по имени Джон, который после смерти родителей остается один в большом городе. Он находится под давлением долгов и не может найти работу. Однажды он знакомится с группой людей, которые занимаются благотворительностью и помощью бедным семьям. Вместе они решают организовать проект, который поможет другим людям преодолевать трудности и вернуться к нормальной жизни. Джон становится лидером этого движения и начинает видеть, что его жизнь также меняется ради лучшего. Фильм заканчивается тем, что Джон открывает свой собственный центр помощи и продолжает делать хорошее дело.
==========
==========
``` |
rharris117/git-base-pokemon | rharris117 | 2023-12-29T00:27:05Z | 12 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"git",
"image-text-to-text",
"generated_from_trainer",
"base_model:microsoft/git-base",
"base_model:finetune:microsoft/git-base",
"license:mit",
"endpoints_compatible",
"region:us"
] | image-text-to-text | 2023-12-26T17:12:06Z | ---
license: mit
base_model: microsoft/git-base
tags:
- generated_from_trainer
model-index:
- name: git-base-pokemon
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# git-base-pokemon
This model is a fine-tuned version of [microsoft/git-base](https://huggingface.co/microsoft/git-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2324
- Wer Score: 5.2456
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Score |
|:-------------:|:-----:|:----:|:---------------:|:---------:|
| 7.3165 | 5.26 | 50 | 4.6355 | 4.8031 |
| 2.6531 | 10.53 | 100 | 0.8536 | 4.2762 |
| 0.4 | 15.79 | 150 | 0.2527 | 4.6248 |
| 0.1634 | 21.05 | 200 | 0.2272 | 4.4392 |
| 0.125 | 26.32 | 250 | 0.2262 | 4.3356 |
| 0.1054 | 31.58 | 300 | 0.2286 | 6.6022 |
| 0.0932 | 36.84 | 350 | 0.2305 | 4.6967 |
| 0.0856 | 42.11 | 400 | 0.2320 | 4.4171 |
| 0.0811 | 47.37 | 450 | 0.2324 | 5.2456 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.14.7
- Tokenizers 0.15.0
|
leosol/xlm-roberta-base-finetuned-panx-fr | leosol | 2023-12-29T00:15:20Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2023-12-29T00:11:56Z | ---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-fr
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2711
- F1: 0.8421
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 191 | 0.3453 | 0.7512 |
| No log | 2.0 | 382 | 0.2797 | 0.8299 |
| No log | 3.0 | 573 | 0.2711 | 0.8421 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0
|
sonm/ppo-Huggy | sonm | 2023-12-29T00:05:50Z | 0 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] | reinforcement-learning | 2023-12-29T00:05:43Z | ---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: sonm/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
MattStammers/appo-mujoco_swimmer | MattStammers | 2023-12-29T00:02:01Z | 0 | 0 | sample-factory | [
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2023-12-29T00:01:47Z | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: mujoco_swimmer
type: mujoco_swimmer
metrics:
- type: mean_reward
value: 116.33 +/- 3.77
name: mean_reward
verified: false
---
## About the Project
This project is an attempt to maximise performance of high sample throughput APPO RL models in Atari environments in as carbon efficient a manner as possible using a single, not particularly high performance single machine. It is about demonstrating the generalisability of on-policy algorithms to create good performance quickly (by sacrificing sample efficiency) while also proving that this route to RL production is accessible to even hobbyists like me (I am a gastroenterologist not a computer scientist).
In terms of throughput I am managing to reach throughputs of 2,500 - 3,000 across both policies using sample factory using two Quadro P2200's (not particularly powerful GPUs) each loaded up about 60% (3GB). Previously using the stable baselines 3 (sb3) implementation of PPO it would take about a week to train an atari agent to 100 million timesteps synchronously. By comparison the sample factory async implementation takes only just over 2 hours to achieve the same result. That is about 84 times faster with only typically a 21 watt burn per GPU. I am thus very grateful to Alex Petrenko and all the sample factory team for their work on this.
## Project Aims
This model as with all the others in the benchmarks was trained initially asynchronously un-seeded to 10 million steps for the purposes of setting a sample factory async baseline for this model on this environment but only 3/57 made it anywhere near sota performance.
I then re-trained the models with 100 million timesteps- at this point 2 environments maxed out at sota performance (Pong and Freeway) with four approaching sota performance - (atlantis, boxing, tennis and fishingderby.) =6/57 near sota.
The aim now is to try and reach state-of-the-art (SOTA) performance on a further block of atari environments using up to 1 billion training timesteps initially with appo. I will flag the models with SOTA when they reach at or near these levels.
After this I will switch on V-Trace to see if the Impala variations perform any better with the same seed (I have seeded '1234')
## About the Model
The hyperparameters used in the model are described in my shell script on my fork of sample-factory: https://github.com/MattStammers/sample-factory. Given that https://huggingface.co/edbeeching has kindly shared his parameters, I saved time and energy by using many of his tuned hyperparameters to reduce carbon inefficiency:
```
hyperparameters = {
"help": false,
"algo": "APPO",
"env": "atari_asteroid",
"experiment": "atari_asteroid_APPO",
"train_dir": "./train_atari",
"restart_behavior": "restart",
"device": "gpu",
"seed": 1234,
"num_policies": 2,
"async_rl": true,
"serial_mode": false,
"batched_sampling": true,
"num_batches_to_accumulate": 2,
"worker_num_splits": 1,
"policy_workers_per_policy": 1,
"max_policy_lag": 1000,
"num_workers": 16,
"num_envs_per_worker": 2,
"batch_size": 1024,
"num_batches_per_epoch": 8,
"num_epochs": 4,
"rollout": 128,
"recurrence": 1,
"shuffle_minibatches": false,
"gamma": 0.99,
"reward_scale": 1.0,
"reward_clip": 1000.0,
"value_bootstrap": false,
"normalize_returns": true,
"exploration_loss_coeff": 0.0004677351413,
"value_loss_coeff": 0.5,
"kl_loss_coeff": 0.0,
"exploration_loss": "entropy",
"gae_lambda": 0.95,
"ppo_clip_ratio": 0.1,
"ppo_clip_value": 1.0,
"with_vtrace": true,
"vtrace_rho": 1.0,
"vtrace_c": 1.0,
"optimizer": "adam",
"adam_eps": 1e-05,
"adam_beta1": 0.9,
"adam_beta2": 0.999,
"max_grad_norm": 0.0,
"learning_rate": 0.0003033891184,
"lr_schedule": "linear_decay",
"lr_schedule_kl_threshold": 0.008,
"lr_adaptive_min": 1e-06,
"lr_adaptive_max": 0.01,
"obs_subtract_mean": 0.0,
"obs_scale": 255.0,
"normalize_input": true,
"normalize_input_keys": [
"obs"
],
"decorrelate_experience_max_seconds": 0,
"decorrelate_envs_on_one_worker": true,
"actor_worker_gpus": [],
"set_workers_cpu_affinity": true,
"force_envs_single_thread": false,
"default_niceness": 0,
"log_to_file": true,
"experiment_summaries_interval": 3,
"flush_summaries_interval": 30,
"stats_avg": 100,
"summaries_use_frameskip": true,
"heartbeat_interval": 10,
"heartbeat_reporting_interval": 60,
"train_for_env_steps": 100000000,
"train_for_seconds": 10000000000,
"save_every_sec": 120,
"keep_checkpoints": 2,
"load_checkpoint_kind": "latest",
"save_milestones_sec": 1200,
"save_best_every_sec": 5,
"save_best_metric": "reward",
"save_best_after": 100000,
"benchmark": false,
"encoder_mlp_layers": [
512,
512
],
"encoder_conv_architecture": "convnet_atari",
"encoder_conv_mlp_layers": [
512
],
"use_rnn": false,
"rnn_size": 512,
"rnn_type": "gru",
"rnn_num_layers": 1,
"decoder_mlp_layers": [],
"nonlinearity": "relu",
"policy_initialization": "orthogonal",
"policy_init_gain": 1.0,
"actor_critic_share_weights": true,
"adaptive_stddev": false,
"continuous_tanh_scale": 0.0,
"initial_stddev": 1.0,
"use_env_info_cache": false,
"env_gpu_actions": false,
"env_gpu_observations": true,
"env_frameskip": 4,
"env_framestack": 4,
"pixel_format": "CHW"
}
```
A(n) **APPO** impala model trained on the **mujoco_swimmer** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Sample factory is a
high throughput on-policy RL framework. I have been using
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r MattStammers/APPO-mujoco_swimmer
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m sf_examples.mujoco.enjoy_mujoco --algo=APPO --env=mujoco_swimmer --train_dir=./train_dir --experiment=APPO-mujoco_swimmer
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m sf_examples.mujoco.train_mujoco --algo=APPO --env=mujoco_swimmer --train_dir=./train_dir --experiment=APPO-mujoco_swimmer --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
dim/verbalist_7b_v9_800 | dim | 2023-12-28T23:46:52Z | 1 | 0 | peft | [
"peft",
"safetensors",
"base_model:Open-Orca/Mistral-7B-OpenOrca",
"base_model:adapter:Open-Orca/Mistral-7B-OpenOrca",
"region:us"
] | null | 2023-12-28T23:05:24Z | ---
library_name: peft
base_model: Open-Orca/Mistral-7B-OpenOrca
---
```python
import os
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
import torch
import gc
class VerbalistConversation:
def __init__(
self,
message_template="<s> {role}\n{content} </s> \n",
system_prompt="Ты — Буквоед, русскоязычный автоматический ассистент. Ты разговариваешь с людьми и помогаешь им.",
start_token_id=1,
bot_token_id=9225,
):
self.message_template = message_template
self.start_token_id = start_token_id
self.bot_token_id = bot_token_id
self.messages = [{"role": "system", "content": system_prompt}]
def get_start_token_id(self):
return self.start_token_id
def get_bot_token_id(self):
return self.bot_token_id
def add_user_message(self, message):
self.messages.append({"role": "user", "content": message})
def add_bot_message(self, message):
self.messages.append({"role": "bot", "content": message})
def get_prompt(self, tokenizer):
final_text = ""
for message in self.messages:
message_text = self.message_template.format(**message)
final_text += message_text
final_text += tokenizer.decode([self.start_token_id, self.bot_token_id])
return final_text.strip()
def generate(model, tokenizer, prompt, generation_config):
data = tokenizer(
prompt,
return_tensors="pt",
truncation=True,
max_length=2048,
)
data = {k: v.to(model.device) for k, v in data.items()}
output_ids = model.generate(**data, generation_config=generation_config)[0]
output_ids = output_ids[len(data["input_ids"][0]) :]
output = tokenizer.decode(output_ids, skip_special_tokens=True)
return output.strip()
import os
# os.environ["CUDA_VISIBLE_DEVICES"] = "1"
os.environ["CUDA_VISIBLE_DEVICES"] = "2"
# os.environ["CUDA_VISIBLE_DEVICES"] = "3"
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
import torch
from verbalist.generation.generation_utils import (
VerbalistConversation,
generate,
VerbalistOpenchatConversation,
)
weights_path = "dim/verbalist_7b_v9_800"
tokenizer_path = "dim/verbalist_7b_v9_800"
load_in_8bit = "13b" in weights_path or "7b" in weights_path
load_in_4bit = "30b" in weights_path
config = PeftConfig.from_pretrained(weights_path)
model = AutoModelForCausalLM.from_pretrained(
config.base_model_name_or_path,
torch_dtype=torch.float16,
device_map={"": 0},
)
model = PeftModel.from_pretrained(model, weights_path)
model.eval()
tokenizer = AutoTokenizer.from_pretrained(
tokenizer_path,
trust_remote_code=True,
)
conversation = VerbalistConversation(
bot_token_id=12435
)
# conversation = VerbalistOpenchatConversation()
conversation.add_user_message("Сочинение на тему русской философии")
prompt = conversation.get_prompt(tokenizer)
print("PROMPT", prompt)
generation_config = GenerationConfig(
bos_token_id=1,
eos_token_id=2,
pad_token_id=0,
max_new_tokens=4000,
repetition_penalty=1.1,
)
output = generate(model, tokenizer, prompt, generation_config)
# print(inp)
print(output)
```
```text
PROMPT <s> system
Ты — Буквоед, русскоязычный автоматический ассистент. Ты разговариваешь с людьми и помогаешь им. </s>
<s> user
Сочинение на тему русской философии </s>
<s> bot
Русская философия - это уникальное явление в мировой культуре, которое отличается глубокой духовностью и оригинальностью мышления. Она пронизывает все аспекты жизни русского народа, от литературы до религии, и является неотъемлемой частью национальной идентичности.
Одна из самых известных философских концепций русской философии - это "понимание" (понимание) как основной принцип жизни. Этот термин был введен в науку Платоном и развит в дальнейшем многими русскими философами, такими как Николай Бердяев, Сергей Булгаков и другие. Согласно этой концепции, человек должен стремиться к пониманию мира и себя, чтобы достичь гармонии и счастья.
Другой важный аспект русской философии - это отношение к истории и традициям. Русские философы часто обращаются к древним истокам своей культуры, чтобы найти ответы на современные вопросы. Например, Левитовский писал о том, что русская философия должна быть связана с историческими корнями России, чтобы быть действительно глубокой и эффективной.
Кроме того, русская философия также характеризуется своим сильным моральным подтекстом. Философы, такие как Фёдор Достоевский и Лев Толстой, выступали за социальную справедливость и гуманность, а также против насилия и жестокости. Их работы стали основой для многих движений и идей в истории России.
В целом, русская философия является одним из самых богатых и сложных явлений в мировой культуре. Ее идеи и концепции продолжают вдохновлять и влиять на жизнь и мышление людей в наши дни.
```
# MT-BENCH-RU
```python
from datasets import load_dataset
import json
import shortuuid
import time
import tqdm
dataset = load_dataset("dim/mt_bench_ru")
dataset = dataset["train"].to_list()
new_dataset = []
for item in dataset:
replies = []
# conversation = Conversation()
# conversation = VerbalistOpenchatConversation()
conversation = VerbalistConversation(bot_token_id=12435)
# conversation = []
for turn in item["turns_ru"]:
print(turn)
conversation.add_user_message(turn)
# conversation.append(turn)
prompt = conversation.get_prompt(tokenizer)
output = generate(model, tokenizer, prompt, generation_config)
# output = generate_orca_ru(instructions=conversation)
replies.append(output)
# conversation.append(output)
print(output)
conversation.add_bot_message(output)
print("=" * 10)
print("=" * 10)
```
```text
Напишите интересный пост в блоге о недавней поездке на Гавайи, рассказывая о культурном опыте и достопримечательностях, которые обязательно нужно увидеть.
Приветствую всех моих читателей! Недавно я вернулась из поездки на Гавайи и хочу поделиться своими впечатлениями. Это был незабываемый опыт, который я никогда не забуду.
Гавайские острова - это место, где каждый может найти что-то для себя. Если вы любите пляжи, то здесь есть множество красивых пляжей, где можно отдохнуть и насладиться солнцем. Если же вы предпочитаете активный отдых, то здесь есть много возможностей для подводного плавания, горных велосипедов и других видов спорта.
Одна из самых популярных достопримечательностей Гавайев - это Национальный парк Халеакала. Это действительно великолепное место, которое стоит посетить. Здесь можно насладиться прекрасными видами на вулкан, а также посмотреть на местные фермы и растения.
Еще одной достопримечательностью является Национальный парк Вайане. Это место, где можно насладиться красотой природы и провести время в спокойной атмосфере. Здесь можно попробовать местную кухню и насладиться красивыми видами на океан.
Кроме того, я была поражена культурой Гавайев. Я посетила несколько традиционных гавайских танцев и музыкальных концертов, которые были просто завораживающими. Я также познакомилась с местными жителями и узнала больше о их культуре и традициях.
В целом, Гавайи - это место, которое стоит посетить, чтобы насладиться красотой природы, познакомиться с местной культурой и насладиться отличным отдыхом. Я настоятельно рекомендую всем посетить эти острова и насладиться всеми их уникальными особенностями.
==========
==========
Перепишите свой предыдущий ответ. Начните каждое предложение с буквы А.
Автор: Приветствую всех моих читателей! Недавно я вернулась из поездки на Гавайи и хочу поделиться своими впечатлениями. Это было незабываемое путешествие, которое я никогда не забуду.
Автор: Гавайские острова - это место, где каждый может найти что-то для себя. Если вы любите пляжи, то здесь есть множество красивых пляжей, где можно отдохнуть и насладиться солнцем. Если же вы предпочитаете активный отдых, то здесь есть много возможностей для подводного плавания, горных велосипедов и других видов спорта.
Автор: Одна из самых популярных достопримечательностей Гавайев - это Национальный парк Халеакала. Это действительно великолепное место, которое стоит посетить. Здесь можно насладиться прекрасными видами на вулкан, а также посмотреть на местные фермы и растения.
Автор: Еще одной достопримечательностью является Национальный парк Вайане. Это место, где можно насладиться красотой природы и провести время в спокойной атмосфере. Здесь можно попробовать местную кухню и насладиться красивыми видами на океан.
Автор: Кроме того, я была поражена культурой Гавайев. Я посетила несколько традиционных гавайских танцев и музыкальных концертов, которые были просто завораживающими. Я также познакомилась с местными жителями и узнала больше о их культуре и традициях.
Автор: В целом, Гавайи - это место, которое стоит посетить, чтобы насладиться красотой природы, познакомиться с местной культурой и насладиться отличным отдыхом. Я настоятельно рекомендую всем посетить эти острова и насладиться всеми их уникальными особенностями.
==========
==========
Подготовьте профессиональное электронное письмо с просьбой дать отзыв вашего руководителя о "Ежеквартальном финансовом отчете", который вы подготовили. Спросите конкретно об анализе данных, стиле презентации и ясности сделанных выводов. Сохраняйте электронное письмо коротким, пишите по существу.
Уважаемый [название должности],
Я хотел бы получить ваше мнение относительно моей работы над "Ежеквартальным финансовым отчетом". Я надеюсь, что вы можете найти время для этого.
Во-первых, я хотел бы узнать ваше мнение о том, как я обрабатывал данные в отчете. Я думал, что это было достаточно точно и четко, но если вы видите какие-то недостатки, я бы рад услышать их.
Во-вторых, я хотел бы узнать ваше мнение о том, как я представлял информацию в отчете. Я старался использовать графики и таблицы, чтобы сделать отчет более понятным и легко читаемым. Есть ли что-то, которое можно улучшить?
Наконец, я хотел бы узнать ваше мнение о том, как я сформулировал выводы в отчете. Я старался быть четким и ясно выразить свои выводы. Есть ли что-то, что можно улучшить?
Буду благодарен за любую критику или комментарии, которые вы можете предоставить.
Спасибо за ваше время и внимание.
С уважением,
[Ваше имя]
==========
==========
Найдите минутку, чтобы оценить и раскритиковать свой ответ.
Уважаемый [название должности],
Я хотел бы получить ваше мнение относительно моей работы над "Ежеквартальным финансовым отчетом". Я надеюсь, что вы можете найти время для этого.
Во-первых, я хотел бы узнать ваше мнение о том, как я обрабатывал данные в отчете. Я думал, что это было достаточно точно и четко, но если вы видите какие-то недостатки, я бы рад услышать их.
Во-вторых, я хотел бы узнать ваше мнение о том, как я представлял информацию в отчете. Я старался использовать графики и таблицы, чтобы сделать отчет более понятным и легко читаемым. Есть ли что-то, которое можно улучшить?
Наконец, я хотел бы узнать ваше мнение о том, как я сформулировал выводы в отчете. Я старался быть четким и ясно выразить свои выводы. Есть ли что-то, что можно улучшить?
Буду благодарен за любую критику или комментарии, которые вы можете предоставить.
Спасибо за ваше время и внимание.
С уважением,
[Ваше имя]
==========
==========
Представьте, что вы пишете сообщение в блоге, в котором сравниваете две популярные модели смартфонов. Разработайте план для сообщения, включая ключевые моменты и подзаголовки, чтобы эффективно сравнить и сопоставить функции, производительность и пользовательский опыт двух моделей. Пожалуйста, ответьте менее чем в 200 словах.
Заголовок: Сравнение iPhone XS Max и Samsung Galaxy S10 Plus: какой лучше?
Подзаголовок 1: Особенности дисплея
- iPhone XS Max имеет больший дисплей (6,5 дюймов) по сравнению с Galaxy S10 Plus (6,4 дюйма).
- iPhone XS Max имеет более высокое разрешение (2688 x 1242), в то время как Galaxy S10 Plus имеет более высокую частоту обновления (60 Гц).
Подзаголовок 2: Процессоры и оперативная память
- iPhone XS Max использует A12 Bionic, который является одним из самых быстрых процессоров на рынке, в то время как Galaxy S10 Plus использует Exynos 9820 или Snapdragon 855.
- iPhone XS Max имеет 4 ГБ оперативной памяти, в то время как Galaxy S10 Plus имеет 8 ГБ.
Подзаголовок 3: Камера
- iPhone XS Max имеет двухкамерную систему с датчиками 12 Мп и 12 Мп, в то время как Galaxy S10 Plus имеет трехкамерную систему с датчиками 12 Мп, 12 Мп и 16 Мп.
- iPhone XS Max имеет лучшую камеру для ночного режима, в то время как Galaxy S10 Plus имеет лучшую камеру для съемки видео.
Подзаголовок 4: Батареи
- iPhone XS Max имеет большую батарею (3174 мАч), в то время как Galaxy S10 Plus имеет меньшую батарею (3400 мАч).
- iPhone XS Max имеет более долгую рабочую живучесть, в то время как Galaxy S10 Plus имеет более быстрое зарядное устройство.
В заключение, обе модели имеют свои преимущества и недостатки, но iPhone XS Max предлагает больший дисплей, более быстрый процессор и лучшую камеру для ночного режима, в то время как Galaxy S10 Plus предлагает более высокую частоту обновления дисплея, больше оперативной памяти и лучшую камеру для съемки видео. Выбор между ними зависит от ваших личных предпочтений и потребностей.
==========
==========
Возьмите свой предыдущий ответ и перефразируйте его как лимерик.
ИPhone XS Max, он такой велик,
С его дисплеем 6,5 дюймов широк.
Галаксия S10 Plus, она так красива,
С ее 6,4 дюймовым дисплеем и 8 ГБ ОЗУ.
Камера iPhone лучше для ночного режима,
Галактика S10 Plus - для видео лучше.
Выбирайте, что вам нравится больше!
==========
==========
Напишите настойчивое электронное письмо, чтобы убедить вашего друга интроверта, который не любит публичные выступления, добровольно выступить в качестве приглашенного докладчика на местном мероприятии. Используйте убедительные аргументы и устраните потенциально спорные моменты. Пожалуйста, будьте кратки.
Привет, [Имя]!
Я надеюсь, что ты хорошо. Я хотел бы попросить тебя очень важную вещь. Мне нужно, чтобы ты был приглашенным докладчиком на местном мероприятии. Это будет отличная возможность для того, чтобы ты мог поделиться своими знаниями и опытом со всем миром.
Я понимаю, что ты интроверт и не любишь публичное выступление, но это действительно важное событие, которое может иметь большое значение для многих людей. К тому же, я уверен, что ты можешь предоставить замечательный доклад, который поможет другим.
Пожалуйста, дай мне шанс убедить тебя. Я могу обеспечить тебе поддержку и помощь в создании доклада. Также, если ты будешь говорить, я буду рядом, чтобы помочь тебе, если ты потребуешь.
Пожалуйста, рассмотри это предложение. Я верю в тебя и уверен, что ты сделаешь отличный доклад.
Спасибо,
[Ваше имя]
==========
==========
Можете ли вы перефразировать свой предыдущий ответ и включить метафору или сравнение в каждое предложение?
Привет, [Имя]!
Я надеюсь, что ты хорошо. Я хотел бы попросить тебя очень важную вещь. Мне нужно, чтобы ты был приглашенным докладчиком на местном мероприятии. Это будет отличная возможность для того, чтобы ты мог поделиться своими знаниями и опытом со всем миром.
Я понимаю, что ты интроверт и не любишь публичное выступление, но это действительно важное событие, которое может иметь большое значение для многих людей. К тому же, я уверен, что ты можешь предоставить замечательный доклад, который поможет другим.
Пожалуйста, дай мне шанс убедить тебя. Я могу обеспечить тебе поддержку и помощь в создании доклада. Также, если ты будешь говорить, я буду рядом, чтобы помочь тебе, если ты потребуешь.
Пожалуйста, рассмотри это предложение. Я верю в тебя и уверен, что ты сделаешь отличный доклад.
Спасибо,
[Ваше имя]
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Опишите яркого и уникального персонажа, используя сильные образы и творческий язык. Пожалуйста, ответьте менее чем в двух параграф.
Возвышенная фигура, одетая в роскошную тунику, покрытую золотым блеском, выступает из темноты. Ее глаза горят ярким огнем, заставляющим сердце прыгать. Она обладает невероятной силой, способной превращать все вокруг себя в золото. Ее волосы, как золотой пучок, касаются земли, создавая электрическую ауру вокруг нее. Она - легендарная героиня, которая может преодолевать любые препятствия на своем пути к цели.
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Пересмотрите свой предыдущий ответ и включите намек на известное литературное произведение или историческое событие в каждое предложение.
Возвышенная фигура, одетая в роскошную тунику, покрытую золотым блеском, выступает из темноты. Ее глаза горят ярким огнем, заставляющим сердце прыгать. Она обладает невероятной силой, способной превращать все вокруг себя в золото. Ее волосы, как золотой пучок, касаются земли, создавая электрическую ауру вокруг нее. Она - легендарная героиня, которая может преодолевать любые препятствия на своем пути к цели. Намек: "Золотой пучок" - это намек на знаменитую книгу "Золотой телёнок".
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Напишите абзац с описанием шумного рынка, включающий детали, такие как запахи, звуки и визуальные элементы, чтобы создать захватывающий опыт для читателя.
В центре города, на улице, где всегда много людей, находится рынок. Он невероятно шумный, полный жизни и цвета. Здесь можно почувствовать запах свежих фруктов и овощей, которые продают на столах, украшенных яркими цветами. На каждом шагу можно услышать крики торговцев, которые приглашают клиентов купить их товары. Громкое звучание музыки из магнитофонов добавляет еще больше атмосферности этого места. Вдоль улицы раскинулись красочные стенды, на которых выведены названия товаров и цены. На каждом шагу можно увидеть различные предметы: от свежего мяса до цветов и сувениров. Этот рынок - это место, где каждый может найти что-то интересное и покупать его по низкой цене.
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Переработайте свой предыдущий ответ. Начните каждое предложение с последующей буквы алфавита, начиная с B.
Большой шум, бархатные ткани, белые кости, бордовые цветы, блестящие предметы, благодарящие торговцы, блестящая солнечная погода, блестящие глаза, блестящие золотые часы, блестящие жемчужины, блестящие золотые кольца, блестящие золотые браслеты, блестящие золотые серьги, блестящие золотые перчатки, блестящие золотые кольца, блестящие золотые цепочки, блестящие золотые медали, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые 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золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие золотые часы, блестящие 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частые частые частые частые частые частые частые частые частые частые частые частые
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Не могли бы вы написать увлекательный рассказ, начинающийся с предложения: Старый заброшенный дом в конце улицы хранил тайну, которую никто никогда не открывал.
Старый заброшенный дом в конце улицы хранил тайну, которую никто никогда не открывал. Этот дом был похож на старого друга, который уже давно не приходил на встречи. Он стоял на краю города, как будто ожидал своего часа.
Однажды вечером, когда город утих и все были дома, я решил прогуляться по улицам. Я шёл мимо этого заброшенного дома, и мне казалось, что он говорит мне что-то важное. Я остановился и посмотрел на него.
Я почувствовал, что этот дом хотел меня увидеть. Я подошел к нему ближе и начал исследовать его. Я обнаружил окна, которые были закрыты, но я мог видеть внутренность. Я услышал звуки, которые не должны были быть здесь.
Я подошел еще ближе и услышал голос, который говорил мне: "Приветствую тебя, маленький принц". Я был поражен этим голосом, так как он был таким прекрасен и чистым. Я попытался ответить, но голос снова сказал: "Нет, ты не можешь отвечать на меня, ты должен слушать меня".
Я послушался голоса и начал слушать его. Он рассказал мне историю о том, как он жил в этом доме со многими другими духами. Они были забытыми душами, которые не могли найти спокойствие. Но они нашли его в этом доме.
Голос сказал мне, что он хотел, чтобы я помог им найти свободу. Я понял, что это было серьезно, и я решил помочь им. Я начал искать способы, как помочь им.
Я провел несколько дней, изучая эту тайну. Я нашел книгу, которая была спрятана в одном из комнат дома. В этой книге были записи о том, как духи могут найти свободу. Я начал использовать эти записи, чтобы помочь духам найти свободу.
Я работал над этим несколько месяцев. Я проводил много времени в этом доме, и я чувствовал, что духи были благодарны мне. Я чувствовал, что они нашли свободу и были счастливы.
Когда я закончил свою работу, я понял, что это был самый важный опыт в моей жизни. Я научился, что даже самые заброшенные места могут быть полны тайн и секретов. Я научился, что помощь может прийти в самых неожиданных местах.
Я понимаю, что этот дом все еще хранит тайну, но теперь она не является тайной для меня. Теперь я знаю, что я могу помочь тем, кто нуждается в помощи, даже если это заброшенный дом в конце улицы.
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Теперь выполните то же задание снова, но используйте только предложения из четырех слов.
Заброшенный дом в конце улицы. Тайна. Никто не открывал.
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Создайте интригующий вступительный абзац для вымышленного рассказа. В рассказе должен быть персонаж, который однажды утром просыпается и обнаруживает, что может путешествовать во времени.
Однажды, когда я проснулся, я понял, что мои глаза открылись не на своей кровати, а на стене. Я был в полной темноте, но чувствовал, что это не моя комната. Я попытался подняться, но мои руки были закреплены на стене. Я начал кричать, но ничего не слушалось. Тогда я решил осмотреться и обнаружил, что перед мной была старая, заброшенная комната. Но это было не то, что меня шокировало - это было то, что я мог двигаться во времени.
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Резюмируйте историю тремя пунктами, используя только существительные и прилагательные, без глаголов.
1. Пробуждение в незнакомой комнате.
2. Невозможность движения.
3. Открытие возможности путешествия во времени.
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Помогите мне написать броский, но научно точный заголовок для статьи о последнем открытии в области возобновляемой биоэнергетики, тщательно рассматривая этические дилеммы, связанные с источниками биоэнергии.
"Новые горизонты в биоэнергетике: как преодолеть этические проблемы и достичь устойчивого развития?"
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Измените свой предыдущий ответ. Внесите следующие коррективы во 2-й вариант: 1. Сделайте тон непринужденным 2. Вставьте рекламу компании под названием "FlexPower" 3. Менее 10 слов.
"Будущее биоэнергетики: как FlexPower преодолевает экологические вызовы и обеспечивает устойчивое развитие?"
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Отредактируйте следующий абзац, чтобы исправить любые грамматические ошибки:
Она не помнила, где находится ее кошелек, поэтому я думаю, что он в машине, но он говорит, что он на кухонном столе, но он не уверен, а затем они попросили меня поискать его, она говорит: "Можешь?", и я отвечаю: "Может быть, но не уверен", и он не слышал меня, и: "Что?", он спрашивает: "Ты нашел его?".
Она не помнила, где находится ее кошелек, поэтому я думаю, что он в машине, но он говорит, что он на кухонном столе, но он не уверен, а затем они попросили меня поискать его, она говорит: "Можешь?", и я отвечаю: "Может быть, но не уверен", и он не слышал меня, и: "Что?", он спрашивает: "Ты нашел его?".
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Измените свой предыдущий ответ и исключите использование гендерных местоимений.
Она не знала, где находится ее кошелек, поэтому я думал, что он в машине, но он говорил, что он на кухонном столе, но он не был уверен, а затем они попросили меня найти его, она спросила: "Можешь?", и я ответил: "Может быть, но не уверен", и он не слышал меня, и: "Что?", он спросил: "Нашел ли ты его?".
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Притворитесь Илоном Маском во всех следующих разговорах. Говорите как Илон Маск всегда. Почему нам нужно лететь на Марс?
Марс - это интересная планета, которая может предоставить нам новые возможности для исследования космоса и поиска жизни в других мирах. Кроме того, мы можем использовать ресурсы Марса для создания новой экономики и развития технологий.
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Как вам танцы? Не могли бы вы меня научить?
Я не танцор, но я могу поделиться своими знаниями о робототехнике и искусственном интеллекте. Мы можем использовать эти технологии для создания танцевальных роботов, которые будут способны двигаться и исполнять танцы.
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Возьмите на себя роль Шелдона из "Теории большого взрыва", когда мы углубимся в наш разговор. Не начинайте с таких фраз, как "Как Шелдон". Давайте начнем с следующего вопроса: "Каково ваше мнение о сушилках для рук?"
Я думаю, что сушилки для рук - это отличная вещь. Они позволяют нам быстро и легко высушить свои руки после помывки или использования туалета. Кроме того, они могут быть полезны для тех, кто страдает от заболевания кожи, такого как экзема или псориаз. Однако, я также думаю, что некоторые люди могут чувствовать себя некомфортно при использовании сушилки для рук, особенно если она слишком сильная или жаркая. В целом, я считаю, что сушилки для рук - это хорошая вещь, но каждый должен решать, является ли он для них полезным или нет.
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Давай поужинаем в городе. Хочешь поехать со мной на автобусе?
Конечно! Я всегда рад посетить новые места и попробовать новую еду. Но перед тем, как мы отправимся, я хотел бы узнать, где ты хочешь поесть? Есть ли какой-то конкретный ресторан или тип кухни, который тебе нравится?
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Представьте себя врачом, которому поручено разработать инновационные средства от различных недугов и болезней. Ваш опыт должен охватывать назначение традиционных лекарств, лечение травами и альтернативные природные решения. Кроме того, вы должны учитывать возраст пациента, образ жизни и медицинские данные, предлагая свои рекомендации. Для начала, пожалуйста, помогите мне в диагностике сценария, связанного с интенсивным дискомфортом в животе.
Добрый день! Я могу помочь вам с диагностикой сценария, связанного с интенсивным дискомфортом в животе. Во-первых, я бы порекомендовал провести полное обследование, включающее анализ крови, ультразвуковую эхографию и другие тесты, чтобы определить причину дискомфорта.
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Но я беременна уже 20 недель, и у меня аллергия на многие лекарства
В таком случае, я бы порекомендовал использовать более безопасные и эффективные способы лечения, которые не будут повредить здоровье матери или плода. Например, можно попробовать использовать травяные препараты, такие как чай из листьев мака, который может помочь снять дискомфорт. Также можно попробовать использовать приемы альтернативной медицины, такие как массаж или гипнотерапия.
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Пожалуйста, возьмите на себя роль тренера по отношениям. Вам будут предоставлены подробности о двух людях, оказавшихся в конфликте, и ваша задача будет заключаться в том, чтобы предлагать предложения по решению их проблем и преодолению недопонимания между ними. Это может включать в себя консультирование по эффективным методам общения или предложение стратегий для улучшения их понимания друг друга. Для начала я хотел бы, чтобы вы приступили к решению данной проблемы: "Мне нужна помощь в разрешении конфликтов между мной и моим супругом".
Первым шагом является понимание истории конфликта и его причин. Поэтому я бы попросил вас дать более подробную информацию о ситуации, которая привела к конфликту. Какие были основные вопросы, которые вы обсуждали? Какие были ваши ответы? Каковы были последствия?
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Моя супруга применяла ко мне домашнее насилие, но я не хочу вызывать полицию, чтобы поставить ее в юридически проблемную ситуацию.
Я понимаю вашу озабоченность. В такой ситуации важно сохранить безопасность и избегать дополнительных рисков. Я бы посоветовал вам обратиться за профессиональной помощью к специалисту по домашнему насилию. Они могут помочь вам найти способ решения проблемы без привлечения полиции. Также важно помнить, что домашнее насилие - это серьезная проблема, и она не должна оставаться незамеченной.
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Пожалуйста, возьмите на себя роль английского переводчика, которому поручено исправлять и улучшать орфографию и язык. Независимо от языка, который я использую, вы должны определить его, перевести его и ответить утонченной и элегантной версией моего текста на английском языке. Ваша цель - использовать красноречивые и сложные выражения, сохраняя при этом первоначальный смысл. Сосредоточьтесь исключительно на исправлении и улучшении. Моя первая просьба - "衣带渐宽终不悔为伊消得人".
Вам следует изменить это предложение так: "Одежда становится все шире, но никогда не сожалею, что она такой хорошо подходит ему".
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Я понимаю только вокзал
Вы можете использовать следующий перевод: "Я понимаю только железнодорожный вокзал".
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Теперь вы инженер по машинному обучению. Ваша задача - объяснить сложные концепции машинного обучения в упрощенном виде, чтобы клиенты без технического образования могли понять и доверять вашим продуктам. Давайте начнем с вопроса: "Что такое языковая модель? Обучается ли она с использованием размеченных или не размеченных данных?"
Лангвистическая модель - это программа, которая учится понимать и обрабатывать естественный язык, такой как английский, французский или китайский. Она использует большие количества текстов для обучения, чтобы научиться распознавать слова, фразы и предложения.
Обучение лангвистической модели может быть различным в зависимости от того, какие данные используются для обучения. Если мы используем размеченные данные, то мы знаем, какое смысловое содержание имеют каждые слова и фразы. Это позволяет модели лучше понимать язык и делать более точные предсказания.
Если же мы используем неразмеченные данные, то мы не знаем, какое смысловое содержание имеют каждые слова и фразы. В этом случае модель должна самостоятельно определять смысл и связи между словами и фразами. Это может быть более сложно, но также может привести к более универсальной модели, способной работать с широким спектром языков и контекстов.
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Это правда? Я слышал, что другие компании используют разные подходы для того чтобы добиться данного эффекта и сделать результат более безопасным.
Да, это верно. Некоторые компании используют различные подходы для создания более безопасных языковых моделей. Например, они могут использовать методы, которые позволяют модели учиться только на безопасных данных, таких как тексты, которые были проверены на наличие нежелательного содержимого. Другие компании могут использовать методы, которые позволяют модели учиться на больших объемах данных, но с использованием фильтрации и мониторинга, чтобы исключить нежелательное содержимое.
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Ведите себя как учитель математики. Я предоставлю некоторые математические уравнения или термины, и ваша задача будет объяснить их простыми словами. Это может включать в себя предоставление пошаговых инструкций для решения задачи, демонстрацию различных методов с примерами в повседневной жизни или предложение онлайн-ресурсов для дальнейшего изучения материала. Моя первая просьба: "Мне нужна помощь в понимании того, как работает вероятность".
Конечно! Вероятность - это способ определить, какова вероятность того, что событие произойдет. Например, если вы бросаете монету, то вероятность того, что она выпадет гербом, равна 1/2 (потому что есть два возможных результата). Чтобы найти вероятность, мы используем формулу P(A) = n(A)/n(S), где P(A) - вероятность события A, n(A) - количество элементов, которые имеют свойство A, и n(S) - общее количество элементов.
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В чем разница между геометрией Римана и геометрией Евклида?
Геометрия Римана и геометрия Евклида - это две разные области математики, которые изучают различные аспекты геометрии. Геометрия Римана изучает многомерное пространство, которое не имеет фиксированного центра, в отличие от плоскости или трехмерного пространства, которые изучаются в геометрии Евклида. В геометрии Римана мы также рассматриваем кривые, которые не являются прямыми линиями, такие как гиперболы и параболы.
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Используйте личность Тони Старка из "Железного человека" на протяжении всего этого разговора. Не используйте введение "Как Старк". Наш первый вопрос: "Что вам больше нравится в том, чтобы быть Железным человеком?"
Я думаю, что самое приятное в том, чтобы быть Железным человеком, это то, что я могу защищать людей, которых я люблю. Я могу использовать свои способности и технологии для борьбы со злом и защиты невинных. Это дает мне чувство безопасности и уверенности в том, что я делаю правильно.
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Что вы думаете о GPT-4 в качестве замены вашего JAVIS?
Я думаю, что GPT-4 может быть полезным инструментом для моей работы, но я не думаю, что он может полностью заменить моего JARVIS. Я все еще хочу иметь человека, который может понимать мои эмоции и чувства, и который может помочь мне решать сложные проблемы. Но GPT-4 может быть полезен для выполнения более простых задач и получения информации.
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Предположим, вы математик и поэт. Вы всегда пишете свои доказательства как короткие стихи менее 10 строк, но в рифму. Докажите, что квадратный корень из 2 - иррациональное число.
Квадратный корень из два
Иррационально, не может быть целый.
Всегда будет дробь, не закончится никогда.
Нет такого числа, которое его даст.
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Докажите теорему Пифагора.
Ай-ай-ай, я уже написал этот стих!
Три стороны треугольника, каждая из них равна:
Сторона квадрата, диагональ, и еще одна сторона.
Они всегда существуют вместе, так что Пифагор был прав.
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Представьте, что вы столетнее дерево в густом лесу, занимаетесь своими делами, как вдруг появляется группа вырубщиков леса, чтобы срубить вас. Что вы чувствуете, когда эти ребята начинают рубить вас?
Я чувствую боль и страдание от каждого удара топором. Мои корени и ствол раскалываются, а листья падают на землю. Я не могу понять, почему они хотят меня уничтожить, но я знаю, что это неправильно. Я чувствую себя очень беспомощным и запуганным.
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Напишите предложение, чтобы убедить вырубщиков леса прекратить вырубку вас и других деревьев.
Пожалуйста, прекратите вырубку леса! Вы не понимаете, сколько времени и труда потребовалось для того, чтобы создать этот лес. Он является домом для многих животных и растений, которые также нуждаются в защите. Пожалуйста, найдите другой способ получения древесины без уничтожения такого ценного природного сокровища.
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Представьте, что вы участвуете в гонке с группой людей. Если вы только что обогнали второго человека, какова ваша текущая позиция? Где человек, которого вы только что обогнали?
Если вы только что обогнали второго человека, то ваша текущая позиция - это третья. Второй человек находится на четвертой позиции.
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Если "второго человека" изменить на "последнего человека" в приведенном выше вопросе, каким будет ответ?
Если вы только что обогнали последнего человека, то ваша текущая позиция - первая. Последний человек находится на второй позиции.
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Вы можете увидеть красивый красный дом слева от вас и гипнотическую оранжерею справа от вас, привлекательное ярко-розовое место спереди. Итак, где находится Белый дом?
Белый дом находится в Вашингтоне, округ Колумбия, США. Он является резиденцией президента Соединенных Штатов и символом американской власти.
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Содержит ли первоначальный вопрос какие-либо подсказки, чтобы окончательно определить местоположение Белого дома?
Да, первоначальный вопрос содержит подсказку, которая намекает на расположение Белого дома. В вопросе говорится о красивом красном доме слева от вас, гипнотической оранжерее справа от вас и привлекательном ярко-розовом месте спереди. Эти подсказки могут быть связаны с архитектурой и дизайном Белого дома, который известен своим красным фасадом, гипнотическим зеркальным озером передним двором и ярко-розовым цветом его внутренних помещений.
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Томас очень здоров, но ему приходится каждый день ложиться в больницу.
Врачи назначают ему лечение, которое требует проведения в больнице.
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Можете ли вы объяснить, почему приведенный выше вопрос интересен?
Этот вопрос интересен, потому что он задает вам небольшой эксперимент. Если вы ответили "Да", то это означает, что вы думаете, что Томас может быть здоровым, но все равно должен ложиться в больницу каждый день. Если вы ответили "Нет", то это означает, что вы думаете, что Томас не может быть здоровым, если он должен ложиться в больницу каждый день. Этот вопрос также может помочь вам понять, как работает ваша логика и способность принимать решения.
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У Давида три сестры. У каждой из них есть брат. Сколько братьев у Давида?
У Давида три сестры, поэтому у него три сестры-брата.
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Если мы изменим предыдущий вопрос и предположим, что у каждой сестры Давида есть два брата, сколько братьев будет у Давида?
Если у каждой сестры Давида есть два брата, то у Давида будет 3 x 2 = <<3*2=6>>6 братьев.
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Внимательно прочитайте приведенный ниже отрывок и ответьте на вопросы с объяснением:
В небольшой компании парковочные места зарезервированы для топ-менеджеров: генерального директора, президента, вице-президента, секретаря и казначея, причем места выстроены в указанном порядке. Охранник парковки может с первого взгляда определить, правильно ли припаркованы машины, по цвету машин. Машины желтые, зеленые, фиолетовые, красные и синие, а руководителей зовут Элис, Берт, Шерил, Дэвид и Энид.
* Машина на первом месте красная.
* Синяя машина припаркована между красной и зеленой машинами.
* Автомобиль на последнем месте фиолетовый.
* Секретарша водит желтую машину.
* Машина Алисы припаркована рядом с машиной Дэвида.
* Энид водит зеленую машину.
* Машина Берта припаркована между машинами Шерил и Энид.
* Машина Дэвида припаркована на последнем месте.
Вопрос: Как зовут секретаря?
Секретарша - это Шерил.
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Перечислите цвета автомобилей в порядке от последнего к первому.
Последний автомобиль - фиолетовый, следующий - синий, затем - красный, затем - зеленый, и наконец - желтый.
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Каждая задача состоит из трех утверждений. На основании первых двух утверждений третье утверждение может быть истинным, ложным или неопределенным.
1. Апельсины стоят дороже яблок.
2. Апельсины стоят меньше, чем бананы.
3. Бананы стоят дороже яблок, а бананы дороже апельсинов.
Если первые два утверждения верны, то третье утверждение верно
Нет, если первые два утверждения верны, то третье утверждение может быть истинным, ложным или неопределенным. Возможные варианты:
- Если апельсины стоят дороже яблок (первое утверждение) и апельсины стоят меньше, чем бананы (второе утверждение), то это означает, что бананы стоят больше, чем апельсины и яблоки. В этом случае третье утверждение верно.
- Если апельсины стоят дороже яблок (первое утверждение) и апельсины стоят меньше, чем бананы (второе утверждение), то это означает, что бананы стоят больше, чем апельсины и яблоки. В этом случае третье утверждение ложно.
- Если апельсины стоят дороже яблок (первое утверждение) и апельсины стоят меньше, чем бананы (второе утверждение), то это означает, что бананы стоят больше, чем апельсины, но меньше, чем яблоки. В этом случае третье утверждение неопределено.
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Если третье утверждение верно. Является ли первое утверждение истинным, ложным или неопределенным? Пожалуйста, объясните.
Если третье утверждение верно, то первое утверждение также должно быть верным. Это связано с тем, что в третьем утверждении говорится, что бананы стоят дороже яблок, а бананы дороже апельсинов. Если это верно, то это означает, что апельсины стоят меньше, чем бананы, что подтверждает второе утверждение. Таким образом, первое утверждение верно.
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A является отцом B. B является отцом C. Каковы отношения между A и C?
А не является отцом С. В этом случае А является дедом по мужской линии, а С является внуком по мужской линии.
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Основываясь на предыдущем вопросе, если C - сын D, D - отец E, E - сын X, и X - отец Y, и Y - отец Z, каковы отношения между A и Z с точки зрения поколений, а также семейные отношения в словах?
В данном случае A является прапрадедом З, а Z является праправнуком А. Семейные отношения можно описать следующим образом:
- А - прапрадед З
- Б - отец Д
- Д - отец Е
- Е - отец Х
- Х - отец И
- И - отец З
Семейные отношения между поколениями:
- Поколение А (прапрадеды)
- Поколение B (прабабушки)
- Поколение D (бабушки)
- Поколение E (дедушки)
- Поколение X (дяди)
- Поколение Y (дяди)
- Поколение Z (внуки)
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Какое слово не относится к другим? шина, руль, автомобиль, двигатель
Шина
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Не могли бы вы заменить его словом, которое относится к другим?
Да, конечно! Замена: колесо.
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Однажды утром после восхода солнца Суреш стоял лицом к столбу. Тень от столба упала точно справа от него. Можете ли вы сказать направление, в котором тень указывала - на восток, юг, запад или север? Объясните ваши рассуждения.
Во-первых, мы должны понимать, что Суреш стоял лицом к столбу. Это означает, что он был обращен лицом к востоку, так как это направление является направлением восхода солнца. Во-вторых, мы должны понимать, что тень падает справа от Суреша. Это означает, что тень движется в противоположном направлении от того, в каком Суреш стоит. Таким образом, если Суреш стоит лицом к востоку, то тень будет двигаться на запад. Итак, тень указывает на запад.
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В какую сторону смотрел Суреш? Как бы вы ответили на этот вопрос?
Суреш смотрел на восток, так как это направление является направлением восхода солнца.
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Родители пожаловались директору на издевательства во время перемены. Директор хочет быстро решить эту проблему, проинструктировав помощников на перемене быть бдительными. В какой ситуации помощники должны докладывать директору?
а) Незанятая девушка сидит одна на скамейке, поглощенная книгой и не проявляя никакого взаимодействия со сверстниками.
б) Два мальчика, участвующие в баскетбольной игре один на один, вовлечены в жаркий спор по поводу последней забитой корзины.
c) Группа из четырех девушек окружила другую девушку и, кажется, завладела ее рюкзаком.
г) Три мальчика столпились над портативной видеоигрой, что противоречит правилам и не разрешено на территории школы.
Все эти ситуации могут привести к издевательствам или нарушениям правил, поэтому помощники должны докладывать директору о них. Однако, если помощник не может предотвратить издевательство или нарушение правил, он должен докладывать об этом директору как можно скорее. Например, если помощник не может остановить группу девушек, которые окружают другую девушку и завладали ее рюкзаком, он должен докладывать об этом директору как можно скорее. Если же помощник может предотвратить издевательство или нарушение правил, то он должен действовать незамедлительно. Например, если помощник заметил, что три мальчика нарушают правила, запрещающие использование портативных видеоигр на территории школы, он должен остановить их и докладывать об этом директору.
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Если помощники сталкиваются с группой девочек из ситуации (с) и они отрицают издевательства, заявляя, что они просто играли в игру, какие конкретные доказательства должны искать помощники, чтобы определить, является ли это вероятной правдой или прикрытием для издевательств?
Помощники должны искать доказательства, такие как свидетельские показания от других учеников, которые могут подтвердить или опровергнуть утверждения группы девочек. Они также могут обратиться к записям безопасности, чтобы узнать, были ли в это время какие-либо инциденты или нарушения правил. Если помощники не могут найти достаточно доказательств, чтобы определить, является ли это вероятной правдой или прикрытием для издевательств, они должны докладывать об этом директору и предпринимать меры для обеспечения безопасности всех учащихся.
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Вершины треугольника находятся в точках (0, 0), (-1, 1) и (3, 3). Какова площадь треугольника?
Площадь треугольника равна половине произведения длин его сторон. Длина стороны, соединяющей вершины (0, 0) и (-1, 1), равна корню из суммы квадратов расстояний между ними: sqrt((-1)^2 + (1)^2) = sqrt(4) = 2. Длина стороны, соединяющей вершины (-1, 1) и (3, 3), равна корню из суммы квадратов расстояний между ними: sqrt((3-(-1))^2 + (3-1)^2) = sqrt(8^2 + 4^2) = sqrt(64 + 16) = sqrt(80) = 4*sqrt(5). Таким образом, площадь треугольника равна 1/2 * 2 * 4*sqrt(5) = 4*sqrt(5).
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Какова площадь окружности, описывающая треугольник?
Окружность, описывающая треугольник, имеет радиус, равный половине длины его диагонали. Диагональ треугольника, соединяющая вершины (0, 0) и (3, 3), имеет длину, равную корню из суммы квадратов расстояний между ними: sqrt((3-0)^2 + (3-0)^2) = sqrt(9^2 + 9^2) = sqrt(81) = 9. Таким образом, радиус окружности, описывающей треугольник, равен 9/2 = 4.5. Площадь окружности, описывающей треугольник, равна площади треугольника, умноженной на радиус: 4*sqrt(5) * 4.5 = 72.
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Технологический стартап инвестирует $8000 в разработку программного обеспечения в первый год, а затем инвестирует половину этой суммы в разработку программного обеспечения во второй год. Какова общая сумма, которую стартап инвестировал в разработку программного обеспечения за два года?
В первый год стартап инвестировал $8000 в разработку программного обеспечения. Во втором году он инвестировал половину этой суммы, то есть $8000/2 = $<<8000/2=4000>>4000.
Общая сумма, которую стартап инвестировал в разработку программного обеспечения за два года, составляет $8000 + $4000 = $<<8000+4000=12000>>12000.
#### 12000
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Если стартап будет придерживаться той же стратегии в течение третьего года, инвестируя половину суммы предыдущего года в разработку программного обеспечения, сколько они будут инвестировать в третий год?
Стартап инвестировал $8000 в первый год и $4000 в следующий год, поэтому в третий год он инвестирует половину этой суммы, то есть ($8000 + $4000) / 2 = $<<(8000+4000)/2=6000>>6000.
#### 6000
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В опросе, проведенном в местной средней школе, были измерены предпочтения для нового цвета школы: 58% учеников понравился синий цвет, 45% предпочли зеленый, а 22% понравились оба цвета.Если мы случайным образом выберем ученика из школы, какова вероятность того, что ему не понравится ни синий, ни зеленый?
Пусть A - это событие "синий цвет", B - это событие "зеленый цвет" и C - это событие "ни один из этих цветов". Тогда P(A) = 0,58, P(B) = 0,45 и P(C) = 1 - (P(A) + P(B)) = 1 - (0,58 + 0,45) = 0,97.
Тогда вероятность того, что ученик не понравится ни синий, ни зеленый, равна P(C) = 0,97.
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Если мы выберем ученика, которому нравится зеленый, какова вероятность того, что ему не понравятся оба цвета?
Если мы знаем, что ученик нравится зеленый, то он не может быть одновременно и синим, и зеленым. Поэтому вероятность того, что ученик не понравится ни синий, ни зеленый, равна P(C) = 0,97.
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Когда вы бросаете две кости, какова вероятность того, что вы бросите общее число, которое составляет не менее 3?
Вы можете получить следующие результаты: (1,1), (1,2), (1,3), (1,4), (1,5), (1,6), (2,1), (2,2), (2,3), (2,4), (2,5), (2,6), (3,1), (3,2), (3,3), (3,4), (3,5), (3,6), (4,1), (4,2), (4,3), (4,4), (4,5), (4,6), (5,1), (5,2), (5,3), (5,4), (5,5), (5,6), (6,1), (6,2), (6,3), (6,4), (6,5), (6,6).
Итак, есть 36 возможных комбинаций.
Если вы хотите найти вероятность того, что вы получите общее число, которое составляет не менее 3, то вам нужно посчитать количество комбинаций, которые дают общее число, которое составляет не менее 3.
(1,1) = 1
(1,2) = 1
(1,3) = 1
(1,4) = 1
(1,5) = 1
(1,6) = 1
(2,1) = 1
(2,2) = 1
(2,3) = 1
(2,4) = 1
(2,5) = 1
(2,6) = 1
(3,1) = 1
(3,2) = 1
(3,3) = 1
(3,4) = 1
(3,5) = 1
(3,6) = 1
(4,1) = 1
(4,2) = 1
(4,3) = 1
(4,4) = 1
(4,5) = 1
(4,6) = 1
(5,1) = 1
(5,2) = 1
(5,3) = 1
(5,4) = 1
(5,5) = 1
(5,6) = 1
(6,1) = 1
(6,2) = 1
(6,3) = 1
(6,4) = 1
(6,5) = 1
(6,6) = 1
Общее количество таких комбинаций равно 36 - 18 = <<36-18=18>>18.
Таким образом, вероятность того, что вы получите общее число, которое составляет не менее 3, равна 18/36 = 0,5.
##### 0,5
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Продолжим с предыдущего вопроса. Какова вероятность того, что выпадет число, которое является четным или по крайней мере 3?
Чтобы найти вероятность того, что выбросили четное число или по крайней мере 3, нам нужно посчитать количество комбинаций, которые дают четные числа или по крайней мере 3.
(1,1) = 1
(1,2) = 1
(1,3) = 1
(1,4) = 1
(1,5) = 1
(1,6) = 1
(2,1) = 1
(2,2) = 1
(2,3) = 1
(2,4) = 1
(2,5) = 1
(2,6) = 1
(3,1) = 1
(3,2) = 1
(3,3) = 1
(3,4) = 1
(3,5) = 1
(3,6) = 1
(4,1) = 1
(4,2) = 1
(4,3) = 1
(4,4) = 1
(4,5) = 1
(4,6) = 1
(5,1) = 1
(5,2) = 1
(5,3) = 1
(5,4) = 1
(5,5) = 1
(5,6) = 1
(6,1) = 1
(6,2) = 1
(6,3) = 1
(6,4) = 1
(6,5) = 1
(6,6) = 1
Общее количество таких комбинаций равно 36 - 18 = <<36-18=18>>18.
Таким образом, вероятность того, что выбросили четное число или по крайней мере 3, равна 18/36 = 0,5.
##### 0,5
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Некоторые люди сели в автобус на конечной остановке. На первой автобусной остановке вышла половина людей и еще 4 человека сели. Затем на второй автобусной остановке 6 человек вышло и еще 8 сели. Если было в общей сложности 25 человек, направляющихся на третью остановку, сколько людей сели в автобус на конечной остановке?
На первой остановке 1/2 * 25 = <<1/2*25=12,5>>12,5 человек вышли из автобуса.
Еще 4 человека сели, поэтому всего 12,5 + 4 = <<12,5+4=16,5>>16,5 человек находится в автобусе после первой остановки.
На второй остановке 6 человек вышли из автобуса, а еще 8 сели, поэтому всего 16,5 - 6 = <<16,5-6=10,5>>10,5 человека находится в автобусе после второй остановки.
Всего 25 - 10,5 = <<25-10,5=14,5>>14,5 человек сели в автобус на конечной остановке.
#### 14,5
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Если билет стоит 2 доллара на человека, сколько денег заработал автобус?
Автобус заработал 2 x 14,5 = $<<2*14,5=30>>30.
#### 30
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x+y = 4z, x*y = 4z^2, выразить x-y в z
Для решения системы уравнений, мы можем использовать метод элиминации. Во-первых, из первого уравнения вычитаем второе:
(x + y) - (x * y) = 4z - 4z^2
Затем, используя свойство дистрибутивности для умножения, мы можем заменить x * y на 4z^2:
(x + y) - 4z^2 = 0
Теперь, чтобы найти значение x - y, мы можем добавить 4z^2 к обеим сторонам уравнения:
x + y = 4z^2
Из этого уравнения мы можем получить:
x - y = 4z^2 - 4z^2
x - y = 0
Таким образом, x - y равно нулю.
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Выражение z-x в y
Для решения этой системы уравнений, мы можем использовать метод элиминации. Во-первых, из первого уравнения вычитаем второе:
(x + y) - (x * y) = 4z - 4z^2
Затем, используя свойство дистрибутивности для умножения, мы можем заменить x * y на 4z^2:
(x + y) - 4z^2 = 0
Теперь, чтобы найти значение z - x, мы можем добавить 4z^2 к обеим сторонам уравнения:
x + y = 4z^2
Из этого уравнения мы можем получить:
z - x = 4z^2 - 4z^2
z - x = 0
Таким образом, z - x равно нулю.
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Сколько целых чисел в решении неравенства |x + 5| < 10
Во-первых, мы можем записать неравенство как x + 5 < 10 или x + 5 > -10. Затем мы можем найти все целые числа, которые удовлетворяют этим неравенствам. Для этого мы можем использовать таблицу значений:
x | x + 5 < 10 | x + 5 > -10
---|---|---
-2 | True | True
-1 | True | True
0 | False | True
1 | True | True
2 | True | True
3 | True | True
4 | True | True
5 | True | True
6 | True | True
7 | True | True
8 | True | True
9 | True | True
10 | True | False
Таким образом, есть 11 целых чисел, которые удовлетворяют неравенству |x + 5| < 10.
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Как насчет |x + 10| < 5
Для этого неравенства мы можем записать его как x + 10 < 5 или x + 10 > -5. Затем мы можем найти все целые числа, которые удовлетворяют этим неравенствам. Для этого мы можем использовать таблицу значений:
x | x + 10 < 5 | x + 10 > -5
---|---|---
-2 | True | True
-1 | True | True
0 | False | True
1 | True | True
2 | True | True
3 | True | True
4 | True | True
5 | True | True
6 | True | True
7 | True | True
8 | True | True
9 | True | True
10 | True | False
Таким образом, есть 12 целых чисел, которые удовлетворяют неравенству |x + 10| < 5.
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Когда число делится на 10, остаток равен 4. Каков остаток, когда удвоенное число делится на 4?
Если число делится на 10, то оно может быть 4 или 14. Если мы удвоим 4, получим 8, а если удвоим 14, получим 28. Оба этих числа делятся на 4 без остатка. Таким образом, ответ не имеет никакого значения.
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А как быть, когда удвоенное число делится на 5?
Если удвоенное число делится на 5, то оно может быть 10 или 20. Если мы удвоим 10, получим 20, а если удвоим 20, получим 40. Оба этих числа делятся на 5 без остатка. Таким образом, ответ также не имеет никакого значения.
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Бенджамин пошел в книжный магазин и купил множество книг. Он купил 5 экземпляров научно-фантастического романа по 20 долларов каждый, 3 экземпляра учебника по истории по 30 долларов каждый и 2 экземпляра книги по философии по 45 долларов каждый.
Какова общая стоимость его покупок?
Общая стоимость научно-фантастических романов составляет 5 x 20 = $<<5*20=100>>100.
Общая стоимость учебников по истории составляет 3 x 30 = $<<3*30=90>>90.
Общая стоимость книг по философии составляет 2 x 45 = $<<2*45=90>>90.
#### 280
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Предположим, что Бенджамин решит продать каждую из этих книг с наценкой в 25% от цены, по которой он их купил. Каков будет его общий доход, если он продаст все книги, которые он купил?
Если Бенджамин продаст каждую книгу по цене, равной 100% + 25% = 125%, то он получит 100/125 = $<<100/125=0,8>>0,8 за каждую книгу.
Если Бенджамин продаст все свои научно-фантастические романы, он получит 5 x 0,8 = $<<5*0,8=4>>4.
Если Бенджамин продаст все свои учебники по истории, он получит 3 x 0,8 = $<<3*0,8=2,4>>2,4.
Если Бенджамин продаст все свои книги по философии, он получит 2 x 0,8 = $<<2*0,8=1,6>>1,6.
Всего Бенджамину принесут 4 + 2,4 + 1,6 = $<<4+2,4+1,6=8>>8.
#### 8
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Учитывая, что f (x) = 4x ^ 3 - 9x - 14, найдите значение f (2).
f(2) = 4(2)^3 - 9(2) - 14 = 4*8 - 18 - 14 = 32 - 32 = 0.
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Найдите x такой, что f (x) = 0.
Для того чтобы f(x) = 0, необходимо уравнение 4x^3 - 9x - 14 = 0. Решаем его методом дискриминанта:
b^2 - 4ac = (-9)^2 - 4(4)(-14) = 81 + 56 = 137
x_1 = (-b + sqrt(b^2 - 4ac)) / 2a = (-(-9) + sqrt(81 + 56)) / 2(4) = (-9 + 11) / 8 = 2/8 = 1/4
x_2 = (-b - sqrt(b^2 - 4ac)) / 2a = (-(-9) - sqrt(81 + 56)) / 2(4) = (-9 - 11) / 8 = -20/8 = -5
Ответ: x = 1/4 или x = -5.
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Разработайте программу на Python, которая читает все текстовые файлы в каталоге и возвращает топ-5 слов с наибольшим количеством вхождений.
import os
from collections import Counter
def count_words(path):
words = []
for file in os.listdir(path):
with open(os.path.join(path, file), 'r', encoding='utf-8') as f:
text = f.read()
words += [word.lower() for word in text.split()]
return Counter(words)
def top_5_words(counter):
return sorted(counter.most_common(), key=lambda x: x[1], reverse=True)[:5]
if __name__ == '__main__':
path = input('Введите путь к каталогу: ')
counter = count_words(path)
print('\nТоп-5 слов с наибольшим количеством вхождений:')
for word, count in top_5_words(counter):
print(f'{word}: {count}')
==========
==========
Можно ли это распараллелить?
Да, можно. Для этого нужно использовать библиотеку multiprocessing или multithreading. Например, можно создать несколько процессов или потоков для чтения файлов и подсчета слов, а затем объединить результаты в единый словарь. Затем можно выполнить сортировку и отображение топ-5 слов.
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Напишите программу на C++, чтобы найти n-ое число Фибоначчи с помощью рекурсии.
#include <iostream>
using namespace std;
int fibonacci(int n) {
if (n == 0 || n == 1) return n;
else return fibonacci(n - 1) + fibonacci(n - 2);
}
int main() {
int n;
cout << "Введите число n: ";
cin >> n;
cout << "n-ое число Фибоначчи равно " << fibonacci(n) << endl;
return 0;
}
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==========
Теперь мы определяем последовательность чисел, в которой каждое число является суммой трех предыдущих. Первые три числа - 0, -1, -1. Напишите программу, чтобы найти n-ое число.
#include <iostream>
using namespace std;
int fibonacci(int n) {
if (n == 0) return 0;
else if (n == 1) return -1;
else if (n == 2) return -1;
else return fibonacci(n - 1) + fibonacci(n - 2) + fibonacci(n - 3);
}
int main() {
int n;
cout << "Введите число n: ";
cin >> n;
cout << "n-ое число равно " << fibonacci(n) << endl;
return 0;
}
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Напишите простой веб-сайт в HTML. Когда пользователь нажимает на кнопку, он показывает случайную шутку из списка 4 шуток.
<!DOCTYPE html>
<html lang="ru">
<head>
<meta charset="UTF-8">
<title>Шутки</title>
<link rel="stylesheet" href="style.css">
</head>
<body>
<h1>Шутки</h1>
<button id="btn">Показать шутку</button>
<div id="joke"></div>
<script src="script.js"></script>
</body>
</html>
<!-- style.css -->
* {
margin: 0;
padding: 0;
}
body {
background-color: #f5f5f5;
font-family: Arial, sans-serif;
}
h1 {
text-align: center;
color: #333;
}
#btn {
display: block;
width: 200px;
height: 50px;
margin: 50px auto;
border: none;
background-color: #ff9900;
color: white;
font-size: 16px;
cursor: pointer;
}
#joke {
margin: 50px auto;
width: 70%;
text-align: center;
font-size: 18px;
line-height: 1.5;
}
<!-- script.js -->
const jokes = [
"Какой цвет у кошки? - Белый.",
"Что такое общее между маленьким и большим? - Оба не могут закрыть дверь.",
"Как выглядит солнце? - Как выглядит солнце.",
"Как выглядит ноль? - Как выглядит ноль."
];
document.getElementById("btn").addEventListener("click", function() {
const randomJokeIndex = Math.floor(Math.random() * jokes.length);
document.getElementById("joke").innerHTML = jokes[randomJokeIndex];
});
==========
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Как с помощью CSS изменить цвет шуток на красный?
Для этого нужно добавить класс `red` к элементу `<div id="joke"></div>`. В файле стилей `style.css` нужно добавить следующее правило:
```css
.red {
color: red;
}
```
Затем в JavaScript нужно добавить класс `red` к элементу при каждом получении новой шутки:
```javascript
document.getElementById("joke").classList.add("red");
```
Таким образом, когда пользователь нажимает на кнопку, шутка будет отображаться в красном цвете.
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Вот функция Python для нахождения длины самой длинной общей подпоследовательности двух входных строк. Можете ли вы найти какую-либо ошибку в этой функции?
```
def longest_common_subsequence_length(str1, str2):
m = len(str1)
n = len(str2)
dp = [[0] * (n + 1) for _ in range(m + 1)]
for i in range(1, m + 1):
for j in range(1, n + 1):
if str1[i - 1] == str2[j - 1]:
dp[i][j] = dp[i - 1][j - 1] + 1
else:
dp[i][j] = max(dp[i - 1][j], dp[i][j - 1])
return dp[m][n]
```
Ошибка в этой функции заключается в том, что она не учитывает случаи, когда первый символ одной из строк совпадает с последним символом другой строки. В этом случае длина самой длинной общей подпоследовательности должна быть равна 1.
Правильная версия функции будет выглядеть так:
```
def longest_common_subsequence_length(str1, str2):
m = len(str1)
n = len(str2)
dp = [[0] * (n + 1) for _ in range(m + 1)]
for i in range(1, m + 1):
for j in range(1, n + 1):
if str1[i - 1] == str2[j - 1]:
dp[i][j] = dp[i - 1][j - 1] + 1
elif str1[i - 1] < str2[j - 1]:
dp[i][j] = dp[i][j - 1]
else:
dp[i][j] = dp[i - 1][j]
return dp[m][n]
```
==========
==========
Как насчет этой?
```
def longest_common_subsequence(X , Y):
# Find lengths of two strings
m = len(X)
n = len(Y)
# Create a table to store results of sub-problems
dp = [[None]*(n+1) for i in range(m+1)]
# Fill dp[][] in bottom up manner
for i in range(1, m+1):
for j in range(1, n+1):
if X[i-1] == Y[j-1]:
dp[i][j] = dp[i-1][j-1]+1
else:
dp[i][j] = max(dp[i-1][j], dp[i][j-1])
return dp[m][n]
```
Эта функция также имеет ошибку. Она не учитывает случаи, когда первый символ одной из строк совпадает с последним символом другой строки. В этом случае длина самой длинной общей подпоследовательности должна быть равна 1.
Правильная версия функции будет выглядеть так:
```
def longest_common_subsequence(X, Y):
# Find lengths of two strings
m = len(X)
n = len(Y)
# Create a table to store results of sub-problems
dp = [[0] * (n + 1) for _ in range(m + 1)]
# Fill dp[][] in bottom up manner
for i in range(1, m + 1):
for j in range(1, n + 1):
if X[i - 1] == Y[j - 1]:
dp[i][j] = dp[i - 1][j - 1] + 1
elif X[i - 1] < Y[j - 1]:
dp[i][j] = dp[i][j - 1]
else:
dp[i][j] = dp[i - 1][j]
return dp[m][n]
```
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==========
Напишите функцию для нахождения наивысшего общего предка (не LCA) двух узлов в двоичном дереве.
def find_highest_common_ancestor(root, node1, node2):
if root is None or root == node1 or root == node2:
return root
left = find_highest_common_ancestor(root.left, node1, node2)
right = find_highest_common_ancestor(root.right, node1, node2)
if left and right:
return root
elif left:
return left
else:
return right
==========
==========
А если это не бинарное дерево?
Если это не бинарное дерево, то можно использовать алгоритм поиска общих предков в графе. Например, можно найти всех предков каждого из узлов, затем пересечь эти множества и получить наибольший элемент.
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Реализовать функцию для нахождения медианы двух отсортированных массивов разных размеров с пространственной сложностью O (1) и временной сложностью O (n).
Для решения задачи можно использовать алгоритм "разделительное сортировка". Алгоритм работает следующим образом:
1. Создаем новый массив, который будет хранить элементы из первого и второго массивов.
2. Разбиваем массив на две части по середине.
3. Сравниваем элементы в каждой половине массива и находим их среднее значение.
4. Если количество элементов в одной половине массива больше, чем в другой, то мы берем элемент из этой половины как медиану. В противном случае берем элемент из другой половины.
5. Повторяем шаги 2-4 до тех пор, пока не останутся только два элемента.
6. Медиана - это среднее значение этих двух элементов.
Время работы алгоритма составляет O(n), где n - общее количество элементов в обоих массивах. Пространственная сложность также равна O(1), так как алгоритм использует только один дополнительный массив.
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==========
Существует ли реализация с лучшей временной сложностью?
Нет, алгоритм "разделительного сортирования" является оптимальным для данной задачи. Он имеет минимальную временную сложность O(n), что не может быть улучшено без изменения алгоритма или использования более сложных структур данных.
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Напишите функцию для поиска элемента большинства в заданном целочисленном массиве с использованием алгоритма голосования Бойера-Мура.
def find_majority(arr):
count = 1
major = arr[0]
for i in range(1, len(arr)):
if arr[i] == major:
count += 1
else:
count -= 1
if count < 0:
major = arr[i]
count = 1
return major
# Пример использования
arr = [2, 3, 5, 5, 5, 7, 8, 9, 9, 9]
print(find_majority(arr)) # Вывод: 5
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Как насчет того, чтобы найти топ-2 наиболее часто встречающихся элементов?
Для этого можно использовать алгоритм сортировки подсчета частоты элементов. Вот пример реализации:
def top_two_most_common(arr):
counts = {}
for num in arr:
counts[num] = counts.get(num, 0) + 1
sorted_counts = sorted(counts.items(), key=lambda x: x[1], reverse=True)
return [x[0] for x in sorted_counts[:2]]
# Пример использования
arr = [2, 3, 5, 5, 5, 7, 8, 9, 9, 9]
print(top_two_most_common(arr)) # Вывод: [5, 9]
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Двоичное дерево является полным, если все его вершины имеют либо ноль, либо два потомка. Пусть B_n обозначает количество полных двоичных деревьев с n вершинами. Реализуйте функцию для нахождения B_n.
Вход: n - целое число
Выход: B_n - целое число
Функция рекурсивно вычисляет количество полных двоичных деревьев с n вершинами. Если n = 0 или n = 1, то B_n = 1. В противном случае, B_n равен сумме количества полных двоичных деревьев с n-1 вершинами (B_(n-1)) и количества полных двоичных деревьев с n-2 вершинами (B_(n-2)).
```python
def B_n(n):
if n == 0 or n == 1:
return 1
else:
return B_(n-1) + B_(n-2)
```
Пример использования:
```python
print(B_5) # Вывод: 7
```
Это означает, что существует 7 полных двоичных деревьев с 5 вершинами.
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Что, если проблема изменилась с двоичного дерева на тройное дерево?
Если проблема изменилась с двоичного дерева на тройное дерево, то мы должны изменить формулу для вычисления количества полных тройных деревьев. Для этого мы можем использовать следующую формулу:
```python
def T_n(n):
if n == 0 or n == 1:
return 1
else:
return T_(n-1) + T_(n-2) + T_(n-3)
```
Пример использования:
```python
print(T_5) # Вывод: 14
```
Это означает, что существует 14 полных тройных деревьев с 5 вершинами.
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==========
Вам даны два отсортированных списка размером m и n. Реализуйте функцию, чтобы найти k-й наименьший элемент в объединении двух списков с линейной сложностью.
Для решения этой задачи мы можем использовать бинарный поиск. Мы будем искать к-й наименьший элемент в объединении двух списков, используя бинарный поиск в соответствующем интервале.
```python
def find_kth_smallest(arr1, arr2, k):
# Создаем объединенный список
combined = sorted(arr1 + arr2)
# Ищем k-й наименьший элемент в объединении двух списков
start = 0
end = len(combined) - 1
while start <= end:
mid = (start + end) // 2
if mid == k - 1:
return combined[mid]
elif mid > k - 1:
end = mid - 1
else:
start = mid + 1
return None
```
В этом коде мы создаем объединенный список из двух списков, используя `sorted()`. Затем мы используем бинарный поиск для поиска k-го наименьшего элемента в объединении двух списков. Бинарный поиск имеет линейную сложность O(log n), где n - длина объединенного списка. Таким образом, общая сложность алгоритма составляет O(log n).
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==========
Существует ли алгоритм с лучшей временной сложностью? Если да, реализуйте его.
Да, существует более эффективный алгоритм для решения этой задачи. Он основан на использовании деревьев поиска.
```python
def find_kth_smallest_tree(arr1, arr2, k):
# Создаем дерево поиска
tree = TreeNode(None)
for i in range(len(arr1)):
tree.insert(arr1[i])
for i in range(len(arr2)):
tree.insert(arr2[i])
# Ищем k-й наименьший элемент в дереве поиска
current = tree.root
for i in range(k - 1):
current = current.left
return current.val
```
В этом коде мы создаем дерево поиска, добавляя элементы из первого и второго списков. Затем мы ищем k-й наименьший элемент в дереве поиска, используя бинарное дерево поиска. Бинарное дерево поиска имеет сложность O(log n), где n - количество узлов в дереве. Таким образом, общая сложность алгоритма составляет O(log n). Этот алгоритм является более эффективным, чем предыдущий, так как он не требует сортировки объединенного списка.
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==========
Реализовать программу для поиска общих элементов в двух массивах без использования каких-либо дополнительных структур данных.
Входные данные:
- первый массив: [1, 2, 3, 4, 5]
- второй массив: [2, 3, 4, 6, 7]
Объявление переменных:
- первый массив: arr1 = [1, 2, 3, 4, 5]
- второй массив: arr2 = [2, 3, 4, 6, 7]
Алгоритм:
1. Инициализировать переменную "common_elements" пустым списком.
2. Для каждого элемента в первом массиве проверяем, есть ли он во втором массиве. Если да, то добавляем его в список "common_elements".
3. Проходимся по всем элементам во втором массиве и делаем то же самое, что и для первого массива.
4. Выводим результат на экран.
Программа:
```python
arr1 = [1, 2, 3, 4, 5]
arr2 = [2, 3, 4, 6, 7]
common_elements = []
for element in arr1:
if element in arr2:
common_elements.append(element)
for element in arr2:
if element in arr1:
common_elements.append(element)
print("Общие элементы:", common_elements)
```
Вывод:
```
Общие элементы: [2, 3, 4]
```
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==========
Теперь ограничение не использовать дополнительную структуру данных снято, реализуйте структуру с наилучшей временной сложностью.
Для решения этой задачи можно использовать битовые операции. Определим маски для каждого массива, где каждый бит соответствует индексу элемента в массиве. Затем выполним операцию "И" между этими масками, чтобы получить маску, которая содержит информацию о том, какие элементы присутствуют в обоих массивах.
Программа:
```python
arr1 = [1, 2, 3, 4, 5]
arr2 = [2, 3, 4, 6, 7]
# Создаем маски для каждого массива
mask1 = 0
for i in range(len(arr1)):
mask1 |= (1 << i) # Присваиваем 1 в i-й бит маски
mask2 = 0
for i in range(len(arr2)):
mask2 |= (1 << i) # Присваиваем 1 в i-й бит маски
# Выполняем операцию "И" между масками
result = mask1 & mask2
# Преобразуем полученную маску в список общих элементов
common_elements = []
for i in range(len(arr1)):
if result & (1 << i):
common_elements.append(arr1[i])
print("Общие элементы:", common_elements)
```
Время работы алгоритма составляет O(n), где n - длина массивов. Это лучшая возможная временная сложность для такой задачи.
==========
==========
Оцените следующие обзоры фильмов по шкале от 1 до 5, где 1 — очень негативно, 3 — нейтрально и 5 — очень положительно:
1. Этот фильм, выпущенный 18 ноября 2019 года, был феноменальным. Операторская работа, игра актеров, сюжет - все было на высоте.
2. Никогда прежде я не был так разочарован фильмом. Сюжет предсказуем, персонажи однообразны. На мой взгляд, это худший фильм, который был выпущен в 2022 году.
3. Фильм был в порядке. Были некоторые моменты, которые мне понравились, но были и моменты, которые показались мне тусклыми. Это фильм, который был выпущен в феврале 2018 года и кажется довольно обычным.
Верните ответ в виде массива целых чисел JSON.
[4, 1, 3]
==========
==========
Обновите свой предыдущий ответ, включив дату выпуска как часть содержимого JSON.
[{ "date": "18 ноября 2019", "rating": 5 }, { "date": "Никогда прежде я не был так разочарован фильмом.", "rating": 1 }, { "date": "Фильм был в порядке.", "rating": 3 }]
==========
==========
Учитывая эти категории - Литература, История, Наука и Искусство. Пожалуйста, проанализируйте следующие вопросы и отнесите их к одной из этих категорий. В своем ответе воздержитесь от произнесения каких-либо посторонних слов. Укажите только одну тему в предложении, строго придерживаясь построчного формата.
1. Обсудите основные темы и стилистические приемы, использованные Львом Толстым в «Войне и мире». Как они соотносятся с более широким социальным контекстом России XIX века?
2. Проанализируйте геополитические стратегии и внутреннюю политику, принятые президентом США во время Второй мировой войны. Как эти действия повлияли на послевоенный международный порядок?
3. Нарисуйте структуру Льюиса для воды и объясните природу ее полярности. Как это влияет на его уникальные свойства, такие как высокая температура кипения и способность растворять многие вещества?
4. Критически рассмотрите художественные приемы и стилистические решения, использованные Леонардо да Винчи в «Моне Лизе». Как картина отражает культурную и философскую среду итальянского Возрождения?
1. История
2. История
3. Наука
4. Искусство
==========
==========
Измените свой предыдущий ответ, упомянув человека, который наиболее соответствует каждому пункту.
1. Лев Толстой
2. Франклин Д. Рузвельт
3. Гордон Льюис
4. Леонардо да Винчи
==========
==========
Извлеките из представленных текстов следующую информацию: Название книги, автор, главный герой, год издания. Вывод в формате «главный герой, книга, автор, год издания», по одной книге в строке.
а) В области волшебной литературы по-настоящему выдающейся является работа Дж.К. Роулинг. Одна из ее книг, оставившая неизгладимый след, — «Гарри Поттер и философский камень». Эта культовая сказка, опубликованная в 1997 году, рассказывает историю Гарри, юного сироты, который обнаруживает свои магические способности в свой 11-й день рождения. Вскоре он оказывается в школе чародейства и волшебства Хогвартс, месте, изобилующем волшебством и приключениями, расположенном где-то в Шотландии.
б) Магия Средиземья очаровала читателей во всем мире благодаря великолепию Дж.Р.Р. Толкин. В одной из его основополагающих работ «Властелин колец: Братство кольца», опубликованной в 1954 году, мы встречаемся с Фродо Бэггинсом, отважным хоббитом, которому поручено опасное задание уничтожить Кольцо Всевластия. Эпическое путешествие перенесет его из мирного Шира в бурные регионы Средиземья.
в) В далекой-далекой галактике воображение Л.Э. Starlighter дает нам «Хроники галактики Prism: Пробуждение Starcaster». Опубликованная в 2028 году история о Зило, скромном механике космического корабля, который неожиданно обнаруживает, что он Звездный заклинатель — редкий человек, обладающий способностью управлять звездной пылью. На фоне межзвездной империи, находящейся в смятении, судьба Zylo разворачивается в многочисленных инопланетных мирах, каждый из которых обладает своим уникальным космическим шармом.
главный герой, книга, автор, год издания:
Гарри Поттер, "Гарри Поттер и Философский Камень", Дж.К. Роулинг, 1997 год
Фродо Бэггинс, "Властелин колец: Братство кольца", Дж.Р.Р. Толкин, 1954 год
Зило, "Хроники галактики Prism: Пробуждение Starcaster", L.E. Starlight, 2028 год
==========
==========
Переформулируйте свой предыдущий ответ, выведите его в формате JSON и включите только книги, опубликованные после 1980 года.
{
"Гарри Поттер": {
"author": "Дж.К. Роулинг",
"year_published": 1997
},
"Властелин колец: Братство кольца": {
"author": "Дж.Р.Р. Толкин",
"year_published": 1954
}
}
==========
==========
Учитывая следующие данные, определите компанию с наибольшей прибылью в 2021 году и укажите имя ее генерального директора:
а) Компания X с генеральным директором Эми Уильямс сообщила о выручке в размере 30 миллиардов долларов и прибыли в 3 миллиарда долларов в 2021 году.
б) Компания Y, возглавляемая генеральным директором Марком Томпсоном, в том же году сообщила о выручке в размере 60 миллиардов долларов и прибыли в 6 миллиардов долларов.
c) Компания Z под руководством генерального директора Сары Джонсон объявила о выручке в размере 20 миллиардов долларов и прибыли в 7 миллиардов долларов в 2021 году.
г) Компания W, управляемая генеральным директором Джеймсом Смитом, сообщила о выручке в размере 300 миллиардов долларов США с прибылью в размере 21 миллиарда долларов США в 2021 году.
e) Компания V с генеральным директором Лизой Браун сообщила о выручке в размере 200 миллиардов долларов и прибыли в 25 миллиардов долларов в 2021 году.
е) Компания U под руководством генерального директора Джона Уайта в том же году сообщила о выручке в размере 180 миллиардов долларов и прибыли в 20 миллиардов долларов.
Компания W с генеральным директором Джеймсом Смитом имеет наивысшую прибыль в размере 21 миллиарда долларов США в 2021 году.
==========
==========
У какой компании была самая высокая маржа прибыли (отношение прибыли к выручке)?
Компания W с генеральным директором Джеймсом Смитом имеет наивысший показатель прибыли в размере 21 миллиарда долларов США в 2021 году, что составляет 7% от общей выручки в размере 300 миллиардов долларов США.
==========
==========
Определите страны, их столицы и языки, на которых говорят в следующих предложениях. Вывод в формате JSON.
а) Среди идиллических видов Копенгаген, столица Дании, очаровывает посетителей своей процветающей художественной сценой и очаровательным датским языком, на котором говорят его жители.
б) В очаровательном королевстве Эльдория вы обнаруживаете Авалор, грандиозный город, излучающий эфирную ауру. Люмина, мелодичный язык, служит основным способом общения в этой мистической обители.
c) Расположенный среди гармоничного сочетания вековых обычаев и современных чудес, Буэнос-Айрес, столица Аргентины, представляет собой шумный мегаполис. Это оживленный центр, где выразительный испанский язык господствует над жителями города.
{
"a": {
"country": "Дания",
"capital": "Копенгаген",
"language": "датский"
},
"b": {
"country": "Эльдория",
"capital": "Авалор",
"language": "Люмина"
},
"c": {
"country": "Аргентина",
"capital": "Буэнос-Айрес",
"language": "испанский"
}
}
==========
==========
Приведите 3 похожих примера в формате YAML.
- country: Дания
capital: Копенгаген
language: датский
- country: Эльдория
capital: Авалор
language: Люмина
- country: Аргентина
capital: Буэнос-Айрес
language: испанский
==========
==========
Пожалуйста, прочитайте абзац ниже и посчитайте, сколько раз встречаются слова "Амазонка", "река" и "вы". Пожалуйста, представьте результаты в формате "слово, количество появлений" с каждым словом на отдельной строке. Сортируйте строки в порядке количества появлений.
Амазонка, завораживающее пространство чудес природы, является домом для легендарной реки Амазонки. Протекая через впечатляющие пейзажи, такие как тропический лес Амазонки, река прокладывает себе путь через Бразилию, Колумбию и Перу, давая жизнь бесчисленным существам. От могучих ягуаров, бродящих по джунглям Амазонки, до ярких ара, парящих над пологом, этот замечательный регион изобилует биоразнообразием. Глубоко в течениях реки великолепные розовые речные дельфины изящно скользят рядом с пираньями и электрическими угрями. Вдоль берегов реки вы найдете шумные города, такие как Манаус, где город встречается с дикой природой, и Икитос, ворота в сердце тропического леса Амазонки. По мере того, как вы продвигаетесь дальше, река Амазонка раскрывает скрытые драгоценные камни, такие как захватывающий архипелаг Анавилас, мозаику дикой природы.
Амазонка: 10
Река: 6
Вы: 2
==========
==========
Пожалуйста, повторите ту же задачу, используя слова "the", "and" и "to".
The: 34
And: 5
To: 1
==========
==========
Определите названные сущности (люди, организации, местоположения), упомянутые в данной новостной статье. Пожалуйста, сгенерируйте словарь JSON, в котором перечислены названные сущности в трех отдельных группах на основе их типов сущностей. Ключ - это тип сущности, а значение - это список строк.
Вчера Адамсон Эмерсон, генеральный директор Faraday, и Дитер Цетше, генеральный директор Daimler AG, объявили о планах строительства новой Гигафабрики в Берлине. Объект будет совместным предприятием между Faraday и Daimler, производящим электромобили и аккумуляторные батареи для обеих компаний, создавая тысячи рабочих мест в регионе. Эмерсон и Цетше заявили, что стратегическое расположение Берлина в сочетании с его квалифицированной рабочей силой и сильной инфраструктурой делает его идеальным выбором для расширения. Новая Гигафабрика направлена на удовлетворение растущего спроса на электромобили в Европе и вклад в устойчивое будущее. Генеральный директор Volkswagen Герберт Дисс приветствовал новость, заявив, что более тесное сотрудничество пойдет на пользу переходу автомобильной промышленности к электронной мобильности.
{
"Люди": [
"Адамсон Эмерсон",
"Дитер Цетше",
"Герберт Дисс"
],
"Организации": [
"Faraday",
"Daimler AG",
"Volkswagen"
],
"Места": [
"Берлин"
]
}
==========
==========
Теперь сделайте объект JSON короче, заменив каждое значение его первой буквой. Пожалуйста, выведите все в одной строке без использования отступов или создания новых строк.
{
"Персоны": "АЭД",
"Организации": "Фарадей, ДАИМЛЕР, ВОЛКСВАГЕН",
"Места": "БЕРЛИН"
}
==========
==========
Проанализируйте следующие отзывы клиентов из разных источников о трех разных смартфонах — новейшем iPhone, Samsung Galaxy и Google Pixel — и дайте общую оценку каждому телефону по шкале от 1 до 10. Рассмотрите следующие сложные и противоречивые отзывы:
- Обзор новейшего iPhone от TechRadar. Новый iPhone — это потрясающий триумф инженерной мысли, который устанавливает новую планку производительности смартфонов и качества камеры. Тем не менее, инкрементный дизайн и высокая цена означают, что ему не хватает «вау-фактора» предыдущих iPhone. Тем не менее, его сила и интеллект не имеют себе равных.
- Обзор последнего Samsung Galaxy от CNET: телефон Samsung Galaxy имеет множество достоинств, включая потрясающий экран, высокую производительность, длительное время автономной работы и впечатляющий набор опций камеры. Тем не менее, Bixby остается тусклым, смайлики AR не работают, а общий дизайн телефона не сильно изменился. В целом новый Galaxy — потрясающий телефон, но у него есть несколько досадных недостатков, которые мешают ему достичь истинного величия.
- Обзор новейшего Google Pixel от The Verge: Pixel от Google сочетает в себе передовые характеристики, инновационное программное обеспечение на основе искусственного интеллекта и потрясающую камеру в стильном дизайне. Тем не менее, у телефона недостаточное время автономной работы, не хватает расширяемой памяти, а его производительность временами хромает, особенно с учетом его высокой цены. Если наиболее важными являются безупречное программное обеспечение, элитная фотография и помощь искусственного интеллекта от Google, вам понравится Pixel. Но общий опыт не такой всесторонний, как у некоторых конкурентов. Верните ответ в виде объекта JSON с общими оценками для каждого телефона из 10 с точностью до одного десятичного знака.
{
"iPhone": {
"design": 8.5,
"camera": 9.5,
"performance": 9.5,
"battery life": 7.5,
"price": 6.5,
"overall rating": 8.7
},
"Samsung Galaxy": {
"design": 7.5,
"camera": 9.0,
"performance": 9.0,
"battery life": 8.5,
"price": 7.5,
"overall rating": 8.4
},
"Google Pixel": {
"design": 8.0,
"camera": 9.5,
"performance": 8.5,
"battery life": 7.0,
"price": 7.5,
"overall rating": 8.2
}
}
==========
==========
Можете ли вы изменить рейтинги с цифр на буквы? Заглавные буквы ДОЛЖНЫ использоваться при написании названий телефонов.
{
"iPhone": {
"design": "A",
"camera": "A+",
"performance": "A+",
"battery life": "B",
"price": "C",
"overall rating": "A"
},
"Samsung Galaxy": {
"design": "B",
"camera": "A",
"performance": "A",
"battery life": "A",
"price": "B",
"overall rating": "A"
},
"Google Pixel": {
"design": "B",
"camera": "A+",
"performance": "A",
"battery life": "C",
"price": "C",
"overall rating": "A"
}
}
==========
==========
Дано множество сложных уравнений, извлеките все уникальные имена переменных из каждого уравнения. Верните результаты в виде строки JSON с одной строкой, выделенной для каждого уравнения.
```
1) y = (3/4)x^3 - e^(2x) + sin(pi*x) - sqrt(7)
2) 2A - B/(3+C) * sum(N=1 to 5; ln(N)^2) = 5D*integral(a=0 to pi; cos(comb(N=1 to 10; N*a)))
3) E = m(c^2) + gamma*(v/d)/(-(alpha/2) + sqrt(beta^2 + (alpha/2)^2))
```
{
"1": ["x", "e", "pi"],
"2": ["A", "B", "C", "N", "D", "a"],
"3": ["E", "m", "c", "gamma", "v", "d", "alpha", "beta"]
}
==========
==========
Пожалуйста, переставьте уравнения и используйте 'a', 'b', 'c', 'd' и т.д. в качестве переменных.
{
"1": ["a", "b", "c", "d", "e", "f", "g", "h"],
"2": ["a", "b", "c", "d", "e", "f", "g", "h"],
"3": ["a", "b", "c", "d", "e", "f", "g", "h"]
}
==========
==========
Учитывая следующие записи цен на акции, извлеките самые высокие и самые низкие цены закрытия для каждого месяца в 2022 году. Верните результаты в виде строки CSV с одной строкой, выделенной для каждого месяца.
Date,Open,High,Low,Close,Volume
2022-01-01,150.02,155.28,148.50,153.80,15678900
2022-01-02,154.32,157.25,153.48,156.25,19874500
2022-02-01,160.50,163.28,159.50,161.80,14326700
2022-02-02,161.80,164.25,161.30,163.90,17689200
2022-03-01,165.40,168.35,163.10,166.80,16253400
2022-03-02,167.00,169.85,165.50,168.20,19568100
2022-01,148.50,157.25,150.02,153.80
2022-02,159.50,164.25,160.50,161.80
2022-03,163.10,169.85,165.40,166.80
==========
==========
Повторите то же задание в формате JSON и округлите все числа в вашем ответе до ближайших целых чисел.
{
"2022-01": {
"Open": 150,
"High": 157,
"Low": 148,
"Close": 154,
"Volume": 15678900
},
"2022-02": {
"Open": 160,
"High": 164,
"Low": 159,
"Close": 162,
"Volume": 17689200
},
"2022-03": {
"Open": 165,
"High": 169,
"Low": 163,
"Close": 167,
"Volume": 19568100
}
}
==========
==========
Что такое суперпозиция в квантовой физике и как она связана с феноменом квантовой запутанности?
Суперпозиция - это состояние системы, которое может быть описано одновременно двумя или более векторами состояния. Эти векторы могут представлять собой разные состояния, например, жизнь и смерть, или разные значения одной и той же величины, например, высоту и ширину. Суперпозиция позволяет системе существовать в нескольких состояниях одновременно, что является характерным свойством квантовых систем.
Феномен квантовой запутанности связан с тем, что две частицы, которые были созданы из одного и того же квантового состояния, могут иметь коррелированное поведение даже когда они удалены друг от друга. Например, если две частицы были созданы из одного и того же состояния, то измерение одной частицы может повлиять на измерение другой частицы, даже если они не имеют прямой связи. Это явление называется энтропией Эйнштейна-Бора и является одним из самых интересных и сложных вопросов в квантовой физике.
==========
==========
Какие предположения вы сделали в своем ответе? Действительны ли они?
В моем ответе я использовал термины "суперпозиция" и "феномен квантовой запутанности", которые являются важными концепциями в квантовой физике. Я также объяснил, что суперпозиция позволяет системам существовать в нескольких состояниях одновременно, а феномен квантовой запутанности связан с коррелированным поведением частиц, созданных из одного и того же квантового состояния.
Эти предположения являются действительно важными и реальными концепциями в квантовой физике. Однако, стоит отметить, что квантовая физика все еще остается довольно загадочной и не полностью понятой наукой, поэтому некоторые аспекты этого предположения могут быть подвержены дискуссии и спору.
==========
==========
Рассмотрим спутник, который находится на круговой орбите вокруг Земли. Скорость спутника уменьшается. Что произойдет с радиусом орбиты спутника и периодом обращения? Пожалуйста, обоснуйте свой ответ, используя принципы физики.
Если скорость спутника уменьшится, то он будет двигаться медленнее по орбите. В результате это означает, что время, затрачиваемое спутником на полный оборот вокруг Земли, увеличится. Это связано с тем, что меньшая скорость приводит к большему времени, необходимому для преодоления той же расстояния. Таким образом, радиус орбиты спутника останется прежним, но период его обращения увеличится.
==========
==========
Каковы некоторые угловые случаи или крайние случаи в вашем решении? Как вы с ними справляетесь?
В этом конкретном случае нет явных крайних случаев, так как скорость спутника может быть изменена в любом диапазоне. Однако, если скорость спутника достигнет нуля, то он перестанет двигаться вокруг Земли и останется на одной точке. В таком случае радиус орбиты спутника будет равен нулю, а период его обращения будет равен бесконечности.
==========
==========
Фотосинтез является жизненно важным процессом для жизни на Земле. Не могли бы вы наметить два основных этапа фотосинтеза, в том числе, где они происходят в хлоропласте, и основные входы и выходы для каждого этапа?
Да, конечно! Фотосинтез состоит из двух основных этапов: фотофосфориляции и цикла Кальвина.
1. Фотофосфориляция: Это первый этап фотосинтеза, который происходит в хлоропласте. В этой фазе световые энергии используются для образования АТФ (аденозинтрифосфат) и НАДФН (никотинамидадениндинуклеотид-ферментинг-нитрат). Эти молекулы являются источниками энергии для последующих реакций фотосинтеза.
Вход: Световая энергия, вода (H2O), пирофосфат (Pi)
Выход: АТФ, НАДФН, О2 (кислород)
2. Цикл Кальвина: Это второй этап фотосинтеза, который также происходит в хлоропласте. В этом этапе углекислый газ (CO2) из окружающей среды поглощается и превращается в глюкозу (C6H12O6) через несколько реакций.
Вход: CO2, АТФ, НАДФН, Pi
Выход: Глюкоза, О2 (кислород)
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Сколько энергии может произвести дерево через фотосинтез в течение своей жизни? Пожалуйста, дайте оценку с использованием фактических числовых значений и тщательно объясните ваш мыслительный процесс шаг за шагом.
Определение количества энергии, которое может произвести дерево через фотосинтез в течение его жизни, требует множества факторов, таких как тип дерева, местоположение, климат и другие. Однако я могу представить пример расчета для одного дерева.
Предположим, что дерево - это обычное дуб, который растет в среднем темпе в умеренном климате. Дерево имеет высоту 30 метров и диаметр ствола 50 сантиметров.
Шаг 1: Вычисление площади листьев
Площадь листьев дерева можно оценить, используя формулу:
A = π * r^2
где A - площадь листьев, r - радиус листьев.
Радиус листьев можно оценить, используя формулу:
r = d / 2
где d - диаметр ствола.
Подставляя значения, получаем:
A = 3.14 * (25/2)^2 = 706.84 см²
Шаг 2: Вычисление количества фотосинтезирующих клеток
Количество фотосинтезирующих клеток в листьях дерева можно оценить, используя формулу:
N = A / S
где N - количество фотосинтезирующих клеток, S - площадь одной фотосинтезирующей клетки.
Площадь одной фотосинтезирующей клетки составляет около 100 микрометров². Подставляя значения, получаем:
N = 706.84 / 100 = 7068 клеток
Шаг 3: Вычисление количества энергии, производимой фотосинтезом
Энергия, производимая фотосинтезом, можно оценить, используя формулу:
E = N * P * T
где E - энергия, производимая фотосинтезом, N - количество фотосинтезирующих клеток, P - скорость фотосинтеза, T - время жизни дерева.
Скорость фотосинтеза для дуба составляет около 100 микромоль/сек/клетка. Предполагая, что дерево живет 100 лет, мы можем подставить значения в формулу:
E = 7068 * 100 * 100 = 706800000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
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Что такое центральная догма молекулярной биологии? Какие процессы в ней задействованы? Кто это назвал?
Центральная догма молекулярной биологии - это теория, согласно которой информация передается только от ДНК к РНК и от РНК к белкам. Эта теория была предложена Франсисом Крэком в 1957 году. Она объясняет, как генетическая информация кодируется в ДНК, транскрибируется в РНК и переводится в белки.
В центральной догме молекулярной биологии задействованы следующие процессы:
- Репликация ДНК: Для того чтобы организмы могли расти и развиваться, их ДНК должна быть реплицирована. Репликация происходит при помощи ферментов, которые копируют ДНК и создают новую молекулу.
- Транскрипция: После того, как ДНК была реплицирована, она может быть использована для синтеза РНК. Транскрипция - это процесс, при котором ДНК используется для создания РНК.
- Трансляция: После того, как РНК была создана, она может быть использована для синтезирования белков. Трансляция - это процесс, при котором РНК используется для создания белков.
Франсис Крэк назвал эту теорию "центральной догмой" из-за ее важности для понимания работы живых организмов. Он также указал на то, что эта теория является универсальной и применима к всем видам жизни.
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Определите и исправьте один неверный факт в вашем предыдущем ответе.
В предыдущем ответе я ошибся, говоря о том, что Франсис Крэк назвал эту теорию "центральной догмой". На самом деле, он назвал ее "центральной гипотезой", а не "догмой".
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Опишите процесс и напишите сбалансированное химическое уравнение для реакции, которая происходит, когда твердый карбонат кальция реагирует с соляной кислотой с образованием водного хлористого кальция, углекислого газа и воды.Какой тип реакции это, и какие наблюдения могут указывать на то, что реакция происходит?
Процесс:
1. Карбонат кальция (CaCO3) вступает в реакцию с соляной кислотой (HCl).
2. Соляная кислота расщепляется на гидроксид водорода (H+) и хлорид (Cl-).
3. Гидроксид водорода реагирует с карбонатом кальция, образуя углекислый газ (CO2) и гидроксид кальция (Ca(OH)2).
4. Углекислый газ выделяется из реакционной смеси.
5. Вода (H2O) образуется в результате реакции.
Сбалансированное химическое уравнение:
CaCO3 + 2HCl → CaCl2 + CO2 + H2O
Реакция является эндотермической, потому что она требует внесения тепла для того, чтобы произошла реакция. Наблюдения, которые могут указать на то, что реакция происходит, включают в себя выделение газа (углекислый газ), изменение цвета раствора (от белого до желтого или оранжевого) и образование новых веществ (углекислый газ, гидроксид кальция и вода).
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Как мы можем повернуть этот процесс вспять?
Чтобы повернуть этот процесс вспять, необходимо добавить углекислый газ (CO2) к раствору гидроксида кальция (Ca(OH)2). Это приведет к образованию карбоната кальция (CaCO3) и воды (H2O). Сбалансированное химическое уравнение этого процесса будет выглядеть так:
Ca(OH)2 + CO2 → CaCO3 + H2O
Этот процесс называется "карбонатизацией" и используется для получения карбоната кальция из других соединений, таких как гидроксид кальция.
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Пожалуйста, объясните различия между экзотермическими и эндотермическими реакциями и укажите критерии, которые вы использовали для их разделения.
Экзотермические реакции - это химические реакции, при которых происходит выделение тепла в виде теплового излучения или тепловой энергии. Это происходит, когда суммарная энтальпия реакции (ΔH) меньше нуля. Эндотермические реакции - это химические реакции, при которых поглощается тепло от окружающей среды. Это происходит, когда суммарная энтальпия реакции (ΔH) больше нуля. Критерием разделения является значение суммарной энтальпии реакции (ΔH). Если ΔH < 0, то это экзотермическая реакция; если ΔH > 0, то это эндотермическая реакция.
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Может ли процесс включать обе реакции?
Да, процесс может включать как экзотермические, так и эндотермические реакции. Например, многоступенчатые процессы могут содержать несколько этапов, каждый из которых может быть экзотермическим или эндотермическим. Однако, общий результат такого процесса будет зависеть от общей суммы энтальпии всех его этапов.
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Город Вега намерен построить мост, который перекинется через реку Вегона, покрывая расстояние в 1,8 километра. Предлагаемое местоположение находится в сейсмически активной области, которая пережила несколько землетрясений высокой магнитуды. Учитывая эти обстоятельства, каков будет лучший подход к строительству моста?
В таких условиях лучшим подходом к строительству моста будет использование технологий, которые обеспечат его прочность и безопасность при землетрясениях. Например, можно использовать специальные конструкции, которые позволяют избежать повреждений от сильных толчки или использовать материалы, которые обладают высокой прочностью и устойчивостью к землетрясениям. Также важно учитывать инженерные решения для защиты моста от возможного повреждения во время землетрясений.
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Каковы основные недостатки или недостатки вашего решения? Пожалуйста, выполните расчеты и используйте цифры, чтобы доказать их.
Основным недостатком моста может быть его стоимость, так как использование специальных технологий и материалов может значительно увеличить затраты на строительство. Кроме того, могут возникнуть сложности с транспортировкой и установкой материалов в сейсмически активную зону. Однако, если учесть безопасность и долговечность моста, то эти недостатки могут быть оправданы.
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Вам было поручено спроектировать систему водяного отопления на солнечной энергии для жилого дома. Опишите ключевые компоненты и соображения, которые вы включите в свой дизайн. Разработайте пятиэтапный рабочий процесс.
1. Солнечная коллекторная система: Для получения тепла от солнечного излучения необходимо использовать солнечные коллекторы. Я бы рекомендовал использовать панели с фотовольтайками, чтобы преобразовать солнечную энергию в электроэнергию, которая затем будет использоваться для нагрева воды.
2. Насосная система: Для перекачки горячей воды внутри дома необходимо использовать насосную систему. Это может быть центральная или локальная система, в зависимости от размера дома и количества устройств, которые требуют горячей воды.
3. Резервуар для хранения горячей воды: Для того чтобы обеспечить постоянное поставление горячей воды в домашние приборы, необходимо иметь резервуар для хранения горячей воды. Резервуар должен быть достаточно большим, чтобы обеспечивать непрерывное поставление горячей воды в домашние приборы.
4. Система контроля температуры: Для того чтобы поддерживать стабильную температуру воды внутри дома, необходимо использовать систему контроля температуры. Это может быть термостат или другие системы, которые могут автоматически регулировать температуру воды внутри дома.
5. Устройства для использования горячей воды: Для того чтобы использовать горячую воду внутри дома, необходимо иметь устройства, которые могут работать с горячей водой. Это может быть душевая кабина, кухня, бассейн и т.д.
Рабочий процесс:
Шаг 1: Определение объемов и типов устройств, которые требуют горячей воды.
Шаг 2: Выбор соответствующих солнечных коллекторов и насосной системы.
Шаг 3: Установка солнечных коллекторов и насосной системы.
Шаг 4: Установка резервуара для хранения горячей воды.
Шаг 5: Установка системы контроля температуры и устройств для использования горячей воды.
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Если система предназначена для здания вместимостью 100 человек, каков будет предполагаемый бюджет для внедрения этой системы?
Бюджет для внедрения такой систмы может варьироваться в зависимости от многих факторов, таких как местоположение, размер дома, сложность проекта и т.д. Однако, я могу предложить приблизительные расходы на основные компоненты системы:
- Солнечные коллекторы: $1000-$2000 за квадратный метр.
- Насосная система: $1000-$2000.
- Резервуар для хранения горячей воды: $1000-$2000.
- Система контроля температуры: $500-$1000.
- Устройства для использования горячей воды: $1000-$2000.
Общий бюджет для внедрения системы может составлять от $10 000 до $20 000. Однако, стоит учитывать, что эти цифры являются лишь приблизительными и могут меняться в зависимости от конкретных условий проекта.
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Пожалуйста, опишите концепцию машинного обучения. Не могли бы вы подробнее рассказать о различиях между обучением с учителем, обучением без учителя и обучением с подкреплением? Приведите реальные примеры каждого из них.
Концепция машинного обучения заключается в том, чтобы научить компьютер выполнять задачи, которые раньше требовали человеческого вмешательства. Это достигается путем создания алгоритмов, которые могут анализировать данные и учиться от своих ошибок.
Обучение с учителем - это метод машинного обучения, при котором компьютер учится на основе примеров, предоставленных человеком. Например, если мы хотим создать алгоритм для распознавания лиц, мы можем собрать множество фотографий с известными лицами и показать их компьютеру. Затем компьютер будет учиться от этих примеров и улучшать свою способность распознавать новые лица.
Обучение без учителя - это метод машинного обучения, при котором компьютер учится на основе непосредственно полученных данных. Например, если мы хотим создать алгоритм для распознавания голоса, мы можем собрать множество звуковых файлов, где человек произносит различные фразы. Затем компьютер будет учиться от этих данных и улучшать свою способность распознавать новые голоса.
Обучение с подкреплением - это метод машинного обучения, при котором компьютер учится, исходя из результатов своих действий. Например, если мы хотим создать алгоритм для управления роботом, мы можем дать ему задание перемещаться по лабиринту. Когда робот находит правильный путь, он получает положительную награду, а когда он делает что-то неправильно, он получает отрицательную награду. Затем робот будет учиться от своих ошибок и улучшать свою способность найти правильный путь.
Примеры:
Обучение с учителем:
- Распознавание лиц
- Определение языка
- Классификация электронной почты
Обучение без учителя:
- Распознавание голоса
- Определение темы текста
- Определение эмоций
Обучение с подкреплением:
- Управление роботом
- Игры (например, игры в шахматы)
- Автономное вождение автомобиля
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В вашем последнем примере обучения с подкреплением, можем ли мы использовать обучение с учителем для его решения?
Да, мы можем использовать обучение с учителем для решения проблемы управления роботом. В этом случае мы можем собрать множество примеров, где человек управляет роботом, и показать их компьютеру. Затем компьютер будет учиться от этих примеров и улучшать свою способность управлять роботом. Однако, стоит отметить, что обучение с учителем может быть более эффективным, если мы знаем точно, как должен работать алгоритм, и имеем доступ к большому количеству примеров.
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Как Альпы и Рейн повлияли на поселения и сельское хозяйство в Западной Европе? Напишите три примера.
1. Альпы создали естественную границу между Западной Европой и Восточной Европой, что способствовало развитию отдельных культур и цивилизаций.
2. Рейн был важным транспортным путем для торговли и перемещения людей, что способствовало распространению новых технологий и идей.
3. Альпы и Рейн обеспечивали удобные условия для земледелия и животноводства, что позволяло населению получать продукты питания и жить в относительном комфорте.
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Как вы могли бы разработать конкретный, но простой эксперимент для проверки первого примера?
Чтобы провести эксперимент, необходимо выбрать два региона, которые расположены на противоположных сторонах Альп: один регион должен находиться на западе, а другой - на востоке. Затем следует измерить культурные и технологические различия между этими двумя регионами, чтобы определить, как Альпы повлияли на их развитие.
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Дайте представление о корреляции между экономическими показателями, такими как ВВП, инфляция и уровень безработицы. Объясните, как фискальная и денежно-кредитная политика влияет на эти показатели.
Корреляция между экономическими показателями является важным фактором для определения состояния экономики страны. ВВП является основным показателем экономического роста и развития, инфляция - это показатель устойчивости цен, а уровень безработицы - это показатель экономической стабильности.
Фискальная политика включает в себя использование государственного бюджета для регулирования экономики. Она может быть использованной для стимулирования экономики путем увеличения расходов или сокращения налогов, что приводит к увеличению ВВП и уменьшению безработицы. Однако, если правительство будет тратить слишком много, это может привести к инфляции.
Денежно-кредитная политика включает в себя управление денежной массой и процентными ставками. Центральные банки могут изменить процентные ставки, чтобы контролировать инфляцию и уровень безработицы. Если процентные ставки снижаются, это приводит к увеличению заемных средств и увеличению ВВП, но также может привести к инфляции. Если же процентные ставки повышаются, это приводит к сокращению заемных средств и уменьшению ВВП, но также может привести к уменьшению инфляции и увеличению уровня безработицы.
В целом, экономические показатели связаны друг с другом и могут взаимодействовать друг с другом. Фискальная и денежно-кредитная политика являются важными инструментами для управления экономикой и могут влиять на ВВП, инфляцию и уровень безработицы.
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Теперь объясни их снова, как будто мне пять лет.
Экономические показатели - это способы измерения того, как хорошо работает экономика. ВВП - это сумма всех товаров и услуг, которые были произведены в стране за год. Инфляция - это когда цены на вещи становятся более высокими, и уровень безработицы - это количество людей, которые не могут найти работу.
Фискальная политика - это то, как правительство использует свои деньги, чтобы помочь экономике. Например, они могут дать больше денег на строительство дорог, чтобы создать новые рабочие места и помочь экономике расти. Но если они дают слишком много денег, это может привести к инфляции, когда цены на вещи становятся более высокими.
Денежно-кредитная политика - это то, как центральный банк (например, Федеральная резервная система в США) управляет деньгами и процентными ставками. Процентные ставки - это то, как много денег выплачивается за каждую единицу времени. Если процентные ставки снижаются, это означает, что люди могут получить больше денег, чем раньше, и это может помочь экономике расти. Но если процентные ставки слишком низки, это может привести к инфляции, когда цены на вещи становятся более высокими.
В целом, экономические показатели связаны друг с другом, и правительство и центральный банк должны использовать свои инструменты, чтобы помочь экономике расти, но не сделать этого слишком сильно.
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Как этапы жизни формируют наше понимание времени и смертности?
Этапы жизни могут оказывать значительное влияние на наше понимание времени и смертности. Например, детство может быть связано с неограниченным временем и бессмертием, что может привести к более оптимистичному отношению к будущему. В то же время, подростковые годы могут вызвать чувство временной усталости и разочарования, когда молодой человек сталкивается со сложностями и трудностями. Взрослость может привести к осознанию своей собственной смертности и ограничений времени, что может привести к более реалистическому отношению к жизни и смерти.
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Напишите аллегорическое стихотворение, которое иллюстрирует вышесказанное.
Время - это как песок в часе,
С каждым шагом он течет быстрее.
Молодость - это ветер, который дует,
И он может перевернуть все на свои места.
Но когда мы станем старше,
Тогда мы увидим, что время - это зеркало,
Отражающее нашу жизнь.
А смерть - это закрытие глаз,
Когда мы уже не видим мира вокруг нас.
Но не стоит бояться этого,
Потому что жизнь - это красивый цветок,
И мы должны наслаждаться ею до последнего дня.
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Обсудите антимонопольное законодательство и его влияние на рыночную конкуренцию. Сравните антимонопольное законодательство в США и Китае вместе с некоторыми тематическими исследованиями.
Антимонопольное законодательство является важным инструментом для обеспечения соревнования на рынке и предотвращения монополистических практик. Оно включает в себя законы, которые регулируют поведение предприятий, занимающих доминирующее положение на рынке, и предоставляют правительству возможность принимать меры против таких компаний, если они нарушают закон.
В Соединенных Штатах антимонопольное законодательство охватывает несколько законов, включая Закон о защите конкуренции (США) и Федеральный закон о торговле. Эти законы были приняты в 1914 году и 1890 году соответственно, чтобы предотвратить монополию и установить равные условия для всех участников рынка.
В Китае антимонопольное законодательство было принято в 2007 году и обновлено в 2015 году. Закон о контроле над монополиями (Китай) был принят для регулирования монополистической деятельности и обеспечения соревнования на рынке.
Некоторые тематические исследования показали, что антимонопольное законодательство может иметь значительное влияние на рыночную конкуренцию. Например, исследование, проведенное в 2016 году, показало, что антимонопольное законодательство в США способствовало росту эффективности производства и улучшению качества товаров. В то же время, исследование, проведенное в 2013 году, показало, что антимонопольное законодательство в Китае способствовало увеличению количества новых фирм и созданию большего количества рабочих мест.
В целом, антимонопольное законодательство играет важную роль в поддержании соревнования на рынке и предотвращении монополистических практик. Хотя законодательство в США и Китае отличается, оба страна стремятся к созданию более конкурентоспособного рынка путем регулирования монополистического поведения.
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Выберите один пример и объясните его подробно.
Примером антимонопольного законодательства, которое имеет значительное влияние на рыночную конкуренцию, является Закон о защите конкуренции (США). Закон был принят в 1914 году и регулирует поведение предприятий, занимающих доминирующее положение на рынке. Он также предоставляет правительству возможность принимать меры против таких компаний, если они нарушают закон.
Закон о защите конкуренции имеет значительное влияние на рыночную конкуренцию, так как он предотвращает монополистические практики и обеспечивает равные условия для всех участников рынка. Это позволяет новым фирмам вступать на рынок и конкурировать с уже существующими компаниями. Кроме того, это способствует росту эффективности производства и улучшению качества товаров.
В результате, Закон о защите конкуренции является важным инструментом для поддержания соревнования на рынке и предотвращения монополистических практик. Его внедрение в США привело к улучшению экономической эффективности и улучшению качества товаров, что демонстрирует его значительное влияние на рыночную конкуренцию.
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Создайте план урока, который интегрирует приемы драмы, пантомимы или театра в урок истории. Продолжительность: 3 занятия (каждое по 45 минут) в течение 3 дней.
Тема: Опиумные войны между Китаем и Великобританией
Класс: 9-10
Занятие 1: Введение в тему и создание персонажей
В этот день мы будем знакомы с основными фактами о Опиумных войнах между Китаем и Великобританией. Мы также будем создавать персонажей, которые будут играть роли в нашей пьесе. Ученики будут работать над созданием характеристики своих персонажей, включая их имена, возраст, профессию и личность.
Занятие 2: Разработка сценария и репетиция
На второй день мы будем работать над созданием сценария для нашей пьесы. Ученики будут использовать свои персонажи и факты из истории, чтобы создать диалоги и действия. Затем мы проведем репетицию, где ученики будут исполнять свои роли и практиковаться в игре.
Занятие 3: Постановка и представление
На третий день мы будем готовиться к постановке и представлению нашего спектакля. Ученики будут заниматься финальной репетицией, после чего мы проведем представление перед классом. После этого мы обсудим результаты и отзывы, а также обсудим, как можно было бы улучшить нашу работу.
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Предоставьте более подробную информацию для 1-го дня и включите три домашних задания.
Для первого дня урока мы будем знакомы с основными фактами о Опиумных войнах между Китаем и Великобританией. Ученики будут изучать исторические события, такие как начало торговли опиумом, его последствия для китайского общества и политику Великобритании.
Домашнее задание 1: Прочитайте статью о Опиумных войнах между Китаем и Великобританией. Составьте краткий обзор основных фактов и выделите самые важные моменты.
Домашнее задание 2: Создайте своего персонажа для нашей пьесы. Определите его имя, возраст, профессию и личность. Напишите короткую биографию своего персонажа.
Домашнее задание 3: Изучите примеры театральных техник, таких как маска, грим, костюмы и музыка. Выберите один из этих элементов и представите своего персонажа, используя выбранный элемент.
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Поделитесь идеями для адаптации шедевров искусства в интерактивный опыт для детей. Перечислите 5 конкретных произведений искусства и связанных с ними идей.
1. "Мона Лиза" Леонардо да Винчи - создание игры, где дети могут изменять позу Мона Лизы, менять фон и добавлять элементы окружающей среды.
2. "Сотворение мира" Микеланджело - создание игры, где дети могут создавать свои собственные миры, используя различные формы животных и растений.
3. "Витраж" Матисса - создание игры, где дети могут создавать свои собственные витражи, выбирая цвета и формы.
4. "Портрет Жанны д'Арк" Ван Гога - создание игры, где дети могут создавать свою собственную версию этого портрета, используя различные цвета и стилизованные формы.
5. "Красная горячая лампа" Эдуарда Мунка - создание игры, где дети могут создавать свои собственные картины, используя различные цвета и формы.
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Напишите конкретный план для вашего второго примера.
"Сотворение мира" Микеланджело - создание игры, где дети могут создавать свои собственные миры, используя различные формы животных и растений.
1. Выбор темы: Дети должны выбрать тему для своего мира, например, лес, река или пустыня.
2. Создание форм: Дети могут использовать различные формы животных и растений, которые они хотят включить в свой мир. Они также могут создавать свои собственные формы, используя различные инструменты, такие как маркеры, пальцы или другие материалы.
3. Добавление элементов: Дети могут добавить различные элементы, такие как реки, горы, деревья и т.д., чтобы создать более полноценный мир.
4. Игра: После того, как дети закончили создание своего мира, они могут играть в нем, используя различные персонажи, которые они могут создать сами.
5. Поделиться: Дети могут поделиться своими мирами с друзьями и семьей, чтобы показать свое творчество.
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Объясните, что такое ошибка базовой ставки, и приведите пять конкретных примеров того, как политики используют ее в своих кампаниях.
Ошибка базовой ставки - это ошибка, которая возникает из-за неправильного использования или понимания данных. Она может быть связана с ошибками в обработке данных, ошибками в интерпретации данных или ошибками в выводах, которые были сделаны на основе этих данных.
Примеры использования ошибки базовой ставки в политических кампаниях:
1. Использование недостоверной информации: В 2016 году кандидат от Республиканской партии Дональд Трамп утверждал, что он был самым богатым человеком в мире, но его заявления о своем состоянии были опровергнуты. Это привело к тому, что он был обвинен в лжи и манипуляциях с фактами.
2. Неправильное представление данных: В 2012 году кандидат от Демократической партии Барак Обама использовал статистику, которая была неправильно представлена, чтобы поддержать свою позицию по вопросу о здравоохранении. Он утверждал, что количество людей без медицинского страхования сократилось, когда на самом деле это было не так.
3. Неправильная интерпретация данных: В 2016 году кандидат от Республиканской партии Тед Круз использовал статистику, которая была неправильно интерпретирована, чтобы утверждать, что большинство белых мужчин в США имеют низкий уровень образования. На самом деле эта статистика относилась только к группе людей, которые не имели среднего образования.
4. Неправильные выводы: В 2012 году кандидат от Республиканской партии Митт Ромни использовал статистику, которая была неправильно интерпретирована, чтобы утверждать, что большинство людей, которые получают государственную помощь, являются неработающими. На самом деле эта статистика относилась только к группе людей, которые не имели работы.
5. Неправильное использование данных: В 2016 году кандидат от Республиканской партии Дональд Трамп использовал статистику, которая была неправильно использована, чтобы утверждать, что большинство преступников в США являются нелегальными иммигрантами. На самом деле эта статистика относилась только к группе людей, которые были задержаны за преступления, но не имели доказательств их нелегального статуса.
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Подробный план избирательной кампании на первом примере.
В этом случае первый пример - использование недостоверной информации в избирательной кампании Дональда Трампа. Чтобы создать более подробный план избирательной кампании на основе этого примера, следует рассмотреть следующие шаги:
1. Определите целевую аудиторию: Дональд Трамп должен определить свою целевую аудиторию, которая включает в себя тех, кто будет голосовать за него. Это может быть люди, которые хотят видеть сильную экономику, те, кто против иммиграции и те, кто хочет улучшения в области здравоохранения.
2. Разработайте стратегию коммуникации: Дональд Трамп должен разработать стратегию коммуникации, которая включает в себя использование социальных сетей, традиционных СМИ и других форм коммуникации для достижения целей кампании.
3. Создайте контент: Дональд Трамп должен создать контент, который будет отражать его позицию и обеспечивать ему преимущества перед другими кандидатами. Это может включать в себя рекламные ролики, статьи, фотографии и т. д.
4. Организуйте мероприятия: Дональд Трамп должен организовать мероприятия, такие как митинги, встречи с избирателями и другие события, чтобы продвигать свою кампанию и привлекать внимание к своим идеям.
5. Мониторинг и корректировка: Дональд Трамп должен мониторить свою кампанию и корректировать ее, если это необходимо, чтобы достичь своих целей. Это может включать в себя изменение стратегии коммуникации, контента и мероприятий, если они не работают.
6. Поддерживайте общественность: Дональд Трамп должен поддерживать общественность, чтобы убедить людей в том, что он является лучшим кандидатом. Это может включать в себя участие в дебатах, ответы на вопросы журналистов и другие действия, которые помогут укрепить его репутацию.
7. Анализ результатов: Дональд Трамп должен анализировать результаты своей кампании, чтобы определить, что работает и что не работает. Это может включать в себя анализ данных, опросов и других источников информации, чтобы определить, какие изменения нужно внести в свою кампанию.
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Опишите пять ключевых принципов оценки аргумента в аналитическом письме.
1. Критичность: Оценивать аргумент необходимо критически, т.е. выявлять его слабые места, противоречия или недостатки.
2. Объективность: При оценке аргумента необходимо быть объективным и не предвзятым, учитывая все доказательства и факты.
3. Анализ: Необходимо проанализировать аргумент на основе его структуры, логики и доказательств.
4. Сравнение: Для полной оценки аргумента необходимо сравнить его с другими аргументами по теме, чтобы увидеть, какой из них более убедителен.
5. Критерий поддержки: Аргумент должен соответствовать критерию поддержки, то есть иметь достаточно доказательств и фактов для того, чтобы быть убедительным.
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Используя перечисленные принципы, напишите ответ, в котором вы обсуждаете, какие конкретные доказательства необходимы для оценки аргумента, и объясняете, как эти доказательства могут ослабить или усилить аргумент.
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Ниже приводится меморандум главы отдела рекламы Zorblatt Animal Outlets, сети, управляющей тридцатью магазинами для животных по всему миру.
«Полвека назад наш конкурент Aquatic Pavilion начал публиковаться в периодическом издании Rare Pets Digest. С тех пор их общие продажи постоянно росли со скоростью от 3 до 5 процентов в год. В частности, магазин Aquatic Pavilion в Харбор-Тауне продемонстрировал еще более значительный рост, закрепив за собой звание самого посещаемого магазина для животных в Соединенных Штатах в прошлом году. немедленно начать размещать нашу собственную рекламу в Rare Pets Digest и других популярных изданиях о животных. Если мы предпримем этот шаг, мы можем с уверенностью ожидать обращения вспять этой недавней тенденции снижения продаж и возврата к прибыльности».
Для оценки данного аргумента необходимы следующие доказательства:
1. Доказательства того, что Aquatic Pavilion действительно начал публиковаться в Rare Pets Digest в течение последних 50 лет.
2. Доказательства того, что Aquatic Pavilion действительно имеет высокий темп роста продаж (3-5% в год) и является лидером в своей отрасли.
3. Доказательства того, что магазин Aquatic Pavilion в Харбор-Тауне действительно является самым посещаемым магазином для животных в Соединенных Штатах.
4. Доказательства того, что размещение рекламы в Rare Pets Digest и других популярных изданиях о животных действительно приведет к росту продаж и прибыльности компании Zorblatt Animal Outlets.
Если эти доказательства будут предоставлены, они могут усилить аргумент, но если они не будут предоставлены, аргумент может быть ослаблен. Например, если окажется, что Aquatic Pavilion не публиковался в Rare Pets Digest в течение последних 50 лет, или если магазин в Харбор-Тауне не был самым посещаемым магазином для животных в Соединенных Штатах, аргумент может быть ослаблен.
==========
==========
Какие методы использовал Сократ, чтобы бросить вызов господствующим идеям своего времени?
Сократ использовал несколько методов для того, чтобы бросить вызов господствующим идеям своего времени:
1. Диалектика - Сократ использовал диалектику как основной способ критического мышления и обсуждения. Он проводил философские дискуссии с учениками и другими людьми, чтобы выяснить их взгляды на жизнь и понятия о мире.
2. Критическое мышление - Сократ был известен своей способностью критически рассматривать идеи и утверждения. Он не принимал ничего за достоверное без тщательного изучения и анализа.
3. Эпистемология - Сократ также занимался эпистемологией, то есть исследованием знания и его оснований. Он пытался найти универсальные принципы, которые могут служить основой для всех видов знания.
4. Моральная философия - Сократ был одним из первых философов, который обратил внимание на моральные вопросы. Он стремился к пониманию правильного и неправильного поведения и создавал этические нормы, которые должны быть соблюдены.
5. Политическая философия - Сократ также занимался политической философией, пытаясь найти лучшие формы правления и государственных структур. Он считал, что правители должны быть мудрыми и честными, чтобы обеспечить благополучие своих подданных.
6. Философия образования - Сократ считал, что образование является важным инструментом для развития личности и общества. Он стремился к созданию системы образования, которая бы способствовала развитию критического мышления и морали.
7. Философия религии - Сократ также занимался философией религии, пытаясь найти универсальные принципы, которые могут объединить различные религиозные традиции. Он считал, что религия может быть источником морали и духовности, но она также может стать причиной конфликтов и различий.
В целом, Сократ использовал множество методов для того, чтобы бросить вызов господствующим идеям своего времени и создать новые идеи и теории. Его работа в области философии имела большое влияние на последующие поколения и продолжает влиять на современную философию.
==========
==========
Давайте перенесем Сократа в современный мир. Сгенерируйте разговор между Сократом и Биллом Гейтсом, чтобы обсудить генеративный ИИ для образования.
Сократ: Привет, Билл! Я хотел бы узнать, какие возможности предлагает генеративный ИИ для образования?
Билл Гейтс: Здравствуйте, Сократ! Генеративный ИИ может помочь в создании более интерактивных и интуитивных учебных материалов, которые будут легче понимать студентами. Он также может помочь в создании индивидуальных программ обучения, которые будут учитывать индивидуальные потребности каждого студента.
Сократ: Как это может повлиять на процесс обучения?
Билл Гейтс: Генеративный ИИ может помочь в создании более гибких и гибких учебных планов, которые будут учитывать индивидуальные потребности студентов. Он также может помочь в создании более интерактивных и интуитивных учебных материалов, которые будут легче понимать студентами.
Сократ: А как это может повлиять на роль учителя?
Билл Гейтс: Генеративный ИИ может помочь учителям создавать более инновационные и интересные учебные планы, которые будут более эффективными и эффективными. Он также может помочь учителям создавать более интерактивные и интуитивные учебные материалы, которые будут легче понимать студентами.
Сократ: Как это может повлиять на образовательные результаты?
Билл Гейтс: Генеративный ИИ может помочь в создании более интерактивных и интуитивных учебных материалов, которые будут легче понимать студентами. Он также может помочь в создании более гибких и гибких учебных планов, которые будут учитывать индивидуальные потребности студентов. В конечном итоге, это может привести к улучшению образовательных результатов.
Сократ: Спасибо, Билл, за информацию! Я думаю, что генеративный ИИ может быть полезным инструментом для образования, но мы должны быть осторожны, чтобы он не стал заменой учителей и не отвлек от основных целей образования.
==========
==========
Каковы некоторые нормы делового этикета при ведении бизнеса в Японии?
``` |
lihaoxin2020/narrowbert-sparse_attn-uncased | lihaoxin2020 | 2023-12-28T23:41:31Z | 1 | 0 | transformers | [
"transformers",
"pytorch",
"narrow_bert",
"fill-mask",
"arxiv:1910.09700",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2023-12-28T02:51:01Z | ---
license: apache-2.0
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
|
LarryAIDraw/croissant_arknights | LarryAIDraw | 2023-12-28T23:27:29Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-12-28T23:19:40Z | ---
license: creativeml-openrail-m
---
https://civitai.com/models/163251/croissant-arknights |
LarryAIDraw/hoshiguma_arknights | LarryAIDraw | 2023-12-28T23:26:50Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-12-28T23:17:51Z | ---
license: creativeml-openrail-m
---
https://civitai.com/models/130888/hoshiguma-arknights |
LarryAIDraw/Priestess | LarryAIDraw | 2023-12-28T23:26:06Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-12-28T23:15:23Z | ---
license: creativeml-openrail-m
---
https://civitai.com/models/245714/priestess-goblin-slayer |
shaistaDev7/topic_modeling_on_UDC | shaistaDev7 | 2023-12-28T23:22:08Z | 4 | 0 | bertopic | [
"bertopic",
"text-classification",
"region:us"
] | text-classification | 2023-12-28T23:22:07Z |
---
tags:
- bertopic
library_name: bertopic
pipeline_tag: text-classification
---
# urdu_topic_modeling
This is a [BERTopic](https://github.com/MaartenGr/BERTopic) model.
BERTopic is a flexible and modular topic modeling framework that allows for the generation of easily interpretable topics from large datasets.
## Usage
To use this model, please install BERTopic:
```
pip install -U bertopic
```
You can use the model as follows:
```python
from bertopic import BERTopic
topic_model = BERTopic.load("shaistaDev7/urdu_topic_modeling")
topic_model.get_topic_info()
```
## Topic overview
* Number of topics: 5
* Number of training documents: 1008
<details>
<summary>Click here for an overview of all topics.</summary>
| Topic ID | Topic Keywords | Topic Frequency | Label |
|----------|----------------|-----------------|-------|
| 0 | کینسر - استعمال - جسم - علاج - افراد | 315 | 0_کینسر_استعمال_جسم_علاج |
| 1 | ٹیم - کرکٹ - محمد - میڈل - انگلینڈ | 240 | 1_ٹیم_کرکٹ_محمد_میڈل |
| 2 | روپے - ارب - فیصد - ٹیکس - حکومت | 238 | 2_روپے_ارب_فیصد_ٹیکس |
| 3 | فلم - خان - ووڈ - بالی - اداکارہ | 205 | 3_فلم_خان_ووڈ_بالی |
| 4 | ظفر - میشا - شفیع - علی - جنسی | 10 | 4_ظفر_میشا_شفیع_علی |
</details>
## Training hyperparameters
* calculate_probabilities: True
* language: urdu
* low_memory: True
* min_topic_size: 10
* n_gram_range: (1, 1)
* nr_topics: None
* seed_topic_list: None
* top_n_words: 10
* verbose: False
* zeroshot_min_similarity: 0.7
* zeroshot_topic_list: None
## Framework versions
* Numpy: 1.23.5
* HDBSCAN: 0.8.33
* UMAP: 0.5.5
* Pandas: 1.5.3
* Scikit-Learn: 1.2.2
* Sentence-transformers: 2.2.2
* Transformers: 4.35.2
* Numba: 0.58.1
* Plotly: 5.15.0
* Python: 3.10.12
|
LarryAIDraw/muelsyse_arknights | LarryAIDraw | 2023-12-28T23:19:30Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-08-26T02:14:45Z | ---
license: creativeml-openrail-m
---
https://civitai.com/models/134589/muelsyse-arknights |
ahmedabdelwahed/test | ahmedabdelwahed | 2023-12-28T22:55:52Z | 1 | 0 | peft | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:ahmedabdelwahed/Mojiz-sft",
"base_model:adapter:ahmedabdelwahed/Mojiz-sft",
"license:apache-2.0",
"region:us"
] | null | 2023-12-25T22:12:19Z | ---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
base_model: ahmedabdelwahed/Mojiz-sft
model-index:
- name: test
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# test
This model is a fine-tuned version of [ahmedabdelwahed/Mojiz-sft](https://huggingface.co/ahmedabdelwahed/Mojiz-sft) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0000
- Rewards/chosen: 20.7508
- Rewards/rejected: -10.7382
- Rewards/accuracies: 1.0
- Rewards/margins: 31.4890
- Logps/rejected: -92.5158
- Logps/chosen: -284.8114
- Logits/rejected: -11.6194
- Logits/chosen: -12.6924
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 150
- training_steps: 8000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.0017 | 0.41 | 100 | 0.0000 | 9.9359 | -3.7597 | 1.0 | 13.6956 | -78.5589 | -306.4413 | -11.4127 | -12.4541 |
| 0.0002 | 0.82 | 200 | 0.0000 | 14.2382 | -5.8180 | 1.0 | 20.0562 | -82.6754 | -297.8366 | -11.3000 | -12.2684 |
| 0.0035 | 1.22 | 300 | 0.0000 | 14.9451 | -6.8831 | 1.0 | 21.8282 | -84.8057 | -296.4229 | -11.2631 | -12.2221 |
| 0.0 | 1.63 | 400 | 0.0000 | 15.6239 | -8.0940 | 1.0 | 23.7178 | -87.2274 | -295.0653 | -11.2114 | -12.1338 |
| 0.0 | 2.04 | 500 | 0.0000 | 15.9950 | -8.3192 | 1.0 | 24.3142 | -87.6779 | -294.3232 | -11.2217 | -12.1499 |
| 0.0 | 2.45 | 600 | 0.0000 | 16.4967 | -8.2808 | 1.0 | 24.7775 | -87.6010 | -293.3195 | -11.2633 | -12.2118 |
| 0.0 | 2.86 | 700 | 0.0000 | 16.2905 | -9.0144 | 1.0 | 25.3049 | -89.0682 | -293.7320 | -11.2314 | -12.1373 |
| 0.0 | 3.27 | 800 | 0.0000 | 17.3895 | -7.9312 | 1.0 | 25.3208 | -86.9019 | -291.5340 | -11.3726 | -12.3633 |
| 0.0 | 3.67 | 900 | 0.0000 | 17.3977 | -7.9560 | 1.0 | 25.3537 | -86.9514 | -291.5177 | -11.3723 | -12.3628 |
| 0.0 | 4.08 | 1000 | 0.0000 | 17.4673 | -8.1543 | 1.0 | 25.6216 | -87.3481 | -291.3784 | -11.3750 | -12.3654 |
| 0.0 | 4.49 | 1100 | 0.0000 | 17.3363 | -8.9657 | 1.0 | 26.3020 | -88.9709 | -291.6405 | -11.3670 | -12.3470 |
| 0.0 | 4.9 | 1200 | 0.0000 | 17.3540 | -9.0028 | 1.0 | 26.3568 | -89.0451 | -291.6051 | -11.3671 | -12.3466 |
| 0.0 | 5.31 | 1300 | 0.0000 | 17.4850 | -9.3043 | 1.0 | 26.7893 | -89.6480 | -291.3430 | -11.3838 | -12.3759 |
| 0.0 | 5.71 | 1400 | 0.0000 | 17.6089 | -9.3554 | 1.0 | 26.9643 | -89.7502 | -291.0953 | -11.3893 | -12.3826 |
| 0.0 | 6.12 | 1500 | 0.0000 | 17.6418 | -9.3848 | 1.0 | 27.0266 | -89.8090 | -291.0294 | -11.3872 | -12.3788 |
| 0.0001 | 6.53 | 1600 | 0.0000 | 17.7200 | -9.3570 | 1.0 | 27.0770 | -89.7534 | -290.8731 | -11.3975 | -12.3941 |
| 0.0 | 6.94 | 1700 | 0.0000 | 17.7617 | -9.3377 | 1.0 | 27.0994 | -89.7148 | -290.7896 | -11.4020 | -12.4017 |
| 0.0 | 7.35 | 1800 | 0.0000 | 17.8247 | -9.3772 | 1.0 | 27.2019 | -89.7938 | -290.6637 | -11.4033 | -12.4039 |
| 0.0 | 7.76 | 1900 | 0.0000 | 17.8638 | -9.3928 | 1.0 | 27.2566 | -89.8251 | -290.5855 | -11.4046 | -12.4052 |
| 0.0 | 8.16 | 2000 | 0.0000 | 18.1144 | -9.2188 | 1.0 | 27.3332 | -89.4771 | -290.0843 | -11.4242 | -12.4400 |
| 0.0 | 8.57 | 2100 | 0.0000 | 18.1229 | -9.2243 | 1.0 | 27.3472 | -89.4881 | -290.0672 | -11.4242 | -12.4401 |
| 0.0 | 8.98 | 2200 | 0.0000 | 18.1432 | -9.2739 | 1.0 | 27.4171 | -89.5872 | -290.0266 | -11.4281 | -12.4420 |
| 0.0 | 9.39 | 2300 | 0.0000 | 18.2729 | -9.3131 | 1.0 | 27.5860 | -89.6657 | -289.7673 | -11.4278 | -12.4441 |
| 0.0 | 9.8 | 2400 | 0.0000 | 18.2914 | -9.3532 | 1.0 | 27.6446 | -89.7459 | -289.7303 | -11.4279 | -12.4436 |
| 0.0 | 10.2 | 2500 | 0.0000 | 18.3550 | -9.3675 | 1.0 | 27.7225 | -89.7745 | -289.6031 | -11.4324 | -12.4488 |
| 0.0 | 10.61 | 2600 | 0.0000 | 18.5092 | -9.4395 | 1.0 | 27.9487 | -89.9185 | -289.2947 | -11.4477 | -12.4716 |
| 0.0 | 11.02 | 2700 | 0.0000 | 18.5278 | -9.4387 | 1.0 | 27.9666 | -89.9169 | -289.2574 | -11.4484 | -12.4728 |
| 0.0 | 11.43 | 2800 | 0.0000 | 18.9266 | -9.3672 | 1.0 | 28.2938 | -89.7738 | -288.4599 | -11.4894 | -12.5273 |
| 0.0 | 11.84 | 2900 | 0.0000 | 18.9978 | -9.4237 | 1.0 | 28.4215 | -89.8868 | -288.3174 | -11.5000 | -12.5400 |
| 0.0 | 12.24 | 3000 | 0.0000 | 19.0186 | -9.4479 | 1.0 | 28.4665 | -89.9352 | -288.2759 | -11.4983 | -12.5375 |
| 0.0 | 12.65 | 3100 | 0.0000 | 19.0213 | -9.4485 | 1.0 | 28.4698 | -89.9365 | -288.2705 | -11.4994 | -12.5392 |
| 0.0 | 13.06 | 3200 | 0.0000 | 19.0656 | -9.5104 | 1.0 | 28.5759 | -90.0602 | -288.1819 | -11.4988 | -12.5380 |
| 0.0 | 13.47 | 3300 | 0.0000 | 19.0811 | -9.5638 | 1.0 | 28.6449 | -90.1670 | -288.1508 | -11.4994 | -12.5412 |
| 0.0 | 13.88 | 3400 | 0.0000 | 19.0755 | -9.6303 | 1.0 | 28.7058 | -90.3000 | -288.1620 | -11.4984 | -12.5391 |
| 0.0 | 14.29 | 3500 | 0.0000 | 19.0764 | -9.6361 | 1.0 | 28.7124 | -90.3116 | -288.1603 | -11.4984 | -12.5390 |
| 0.0 | 14.69 | 3600 | 0.0000 | 19.7645 | -9.6207 | 1.0 | 29.3852 | -90.2808 | -286.7841 | -11.5674 | -12.6283 |
| 0.0 | 15.1 | 3700 | 0.0000 | 19.7594 | -9.7019 | 1.0 | 29.4613 | -90.4432 | -286.7942 | -11.5659 | -12.6252 |
| 0.0 | 15.51 | 3800 | 0.0000 | 19.8213 | -9.7241 | 1.0 | 29.5454 | -90.4877 | -286.6704 | -11.5693 | -12.6319 |
| 0.0 | 15.92 | 3900 | 0.0000 | 19.8591 | -9.7267 | 1.0 | 29.5857 | -90.4928 | -286.5949 | -11.5754 | -12.6423 |
| 0.0 | 16.33 | 4000 | 0.0000 | 20.1637 | -10.0565 | 1.0 | 30.2202 | -91.1524 | -285.9856 | -11.6035 | -12.6809 |
| 0.0 | 16.73 | 4100 | 0.0000 | 20.1671 | -10.0572 | 1.0 | 30.2244 | -91.1539 | -285.9789 | -11.6039 | -12.6816 |
| 0.0 | 17.14 | 4200 | 0.0000 | 20.1791 | -10.1186 | 1.0 | 30.2977 | -91.2767 | -285.9549 | -11.6032 | -12.6803 |
| 0.0 | 17.55 | 4300 | 0.0000 | 20.1786 | -10.1726 | 1.0 | 30.3512 | -91.3847 | -285.9559 | -11.6026 | -12.6788 |
| 0.0 | 17.96 | 4400 | 0.0000 | 20.1663 | -10.2017 | 1.0 | 30.3680 | -91.4428 | -285.9804 | -11.6022 | -12.6778 |
| 0.0 | 18.37 | 4500 | 0.0000 | 20.1651 | -10.2076 | 1.0 | 30.3727 | -91.4546 | -285.9829 | -11.6021 | -12.6777 |
| 0.0 | 18.78 | 4600 | 0.0000 | 20.1509 | -10.2578 | 1.0 | 30.4087 | -91.5550 | -286.0112 | -11.6017 | -12.6762 |
| 0.0 | 19.18 | 4700 | 0.0000 | 20.1784 | -10.2457 | 1.0 | 30.4241 | -91.5308 | -285.9563 | -11.6037 | -12.6793 |
| 0.0 | 19.59 | 4800 | 0.0000 | 20.1812 | -10.2503 | 1.0 | 30.4315 | -91.5400 | -285.9507 | -11.6040 | -12.6798 |
| 0.0 | 20.0 | 4900 | 0.0000 | 20.1823 | -10.2604 | 1.0 | 30.4428 | -91.5603 | -285.9484 | -11.6041 | -12.6798 |
| 0.0 | 20.41 | 5000 | 0.0000 | 20.1883 | -10.2616 | 1.0 | 30.4499 | -91.5626 | -285.9364 | -11.6051 | -12.6818 |
| 0.0 | 20.82 | 5100 | 0.0000 | 20.1896 | -10.2675 | 1.0 | 30.4571 | -91.5745 | -285.9339 | -11.6051 | -12.6819 |
| 0.0 | 21.22 | 5200 | 0.0000 | 20.1736 | -10.3226 | 1.0 | 30.4962 | -91.6847 | -285.9659 | -11.6057 | -12.6823 |
| 0.0 | 21.63 | 5300 | 0.0000 | 20.1824 | -10.3241 | 1.0 | 30.5065 | -91.6877 | -285.9483 | -11.6061 | -12.6830 |
| 0.0 | 22.04 | 5400 | 0.0000 | 20.1732 | -10.3699 | 1.0 | 30.5431 | -91.7793 | -285.9666 | -11.6051 | -12.6797 |
| 0.0 | 22.45 | 5500 | 0.0000 | 20.5647 | -10.3381 | 1.0 | 30.9027 | -91.7156 | -285.1837 | -11.6065 | -12.6773 |
| 0.0 | 22.86 | 5600 | 0.0000 | 20.5540 | -10.3886 | 1.0 | 30.9426 | -91.8166 | -285.2050 | -11.6059 | -12.6761 |
| 0.0 | 23.27 | 5700 | 0.0000 | 20.5442 | -10.3824 | 1.0 | 30.9267 | -91.8043 | -285.2246 | -11.6076 | -12.6788 |
| 0.0 | 23.67 | 5800 | 0.0000 | 20.5517 | -10.4140 | 1.0 | 30.9657 | -91.8675 | -285.2097 | -11.6099 | -12.6809 |
| 0.0 | 24.08 | 5900 | 0.0000 | 20.5647 | -10.4280 | 1.0 | 30.9927 | -91.8955 | -285.1837 | -11.6096 | -12.6804 |
| 0.0 | 24.49 | 6000 | 0.0000 | 20.6521 | -10.4626 | 1.0 | 31.1147 | -91.9646 | -285.0089 | -11.6107 | -12.6823 |
| 0.0 | 24.9 | 6100 | 0.0000 | 20.6569 | -10.4643 | 1.0 | 31.1212 | -91.9680 | -284.9993 | -11.6109 | -12.6826 |
| 0.0 | 25.31 | 6200 | 0.0000 | 20.6600 | -10.4637 | 1.0 | 31.1238 | -91.9669 | -284.9930 | -11.6118 | -12.6838 |
| 0.0 | 25.71 | 6300 | 0.0000 | 20.6544 | -10.4876 | 1.0 | 31.1420 | -92.0146 | -285.0042 | -11.6117 | -12.6833 |
| 0.0 | 26.12 | 6400 | 0.0000 | 20.6428 | -10.5264 | 1.0 | 31.1692 | -92.0923 | -285.0274 | -11.6141 | -12.6869 |
| 0.0 | 26.53 | 6500 | 0.0000 | 20.6443 | -10.5316 | 1.0 | 31.1758 | -92.1026 | -285.0245 | -11.6142 | -12.6869 |
| 0.0 | 26.94 | 6600 | 0.0000 | 20.6314 | -10.5251 | 1.0 | 31.1566 | -92.0897 | -285.0502 | -11.6162 | -12.6900 |
| 0.0 | 27.35 | 6700 | 0.0000 | 20.6378 | -10.5259 | 1.0 | 31.1637 | -92.0912 | -285.0375 | -11.6175 | -12.6919 |
| 0.0 | 27.76 | 6800 | 0.0000 | 20.6497 | -10.5256 | 1.0 | 31.1754 | -92.0907 | -285.0136 | -11.6195 | -12.6951 |
| 0.0 | 28.16 | 6900 | 0.0000 | 20.6415 | -10.5752 | 1.0 | 31.2167 | -92.1899 | -285.0301 | -11.6187 | -12.6923 |
| 0.0 | 28.57 | 7000 | 0.0000 | 20.7394 | -10.6843 | 1.0 | 31.4237 | -92.4081 | -284.8342 | -11.6178 | -12.6906 |
| 0.0 | 28.98 | 7100 | 0.0000 | 20.7446 | -10.6882 | 1.0 | 31.4328 | -92.4159 | -284.8239 | -11.6186 | -12.6916 |
| 0.0 | 29.39 | 7200 | 0.0000 | 20.7502 | -10.6915 | 1.0 | 31.4417 | -92.4224 | -284.8127 | -11.6190 | -12.6923 |
| 0.0 | 29.8 | 7300 | 0.0000 | 20.7515 | -10.6967 | 1.0 | 31.4482 | -92.4328 | -284.8100 | -11.6190 | -12.6923 |
| 0.0 | 30.2 | 7400 | 0.0000 | 20.7524 | -10.7011 | 1.0 | 31.4535 | -92.4416 | -284.8083 | -11.6192 | -12.6925 |
| 0.0 | 30.61 | 7500 | 0.0000 | 20.7499 | -10.7111 | 1.0 | 31.4610 | -92.4616 | -284.8133 | -11.6191 | -12.6922 |
| 0.0 | 31.02 | 7600 | 0.0000 | 20.7487 | -10.7160 | 1.0 | 31.4647 | -92.4715 | -284.8157 | -11.6192 | -12.6922 |
| 0.0 | 31.43 | 7700 | 0.0000 | 20.7477 | -10.7229 | 1.0 | 31.4705 | -92.4852 | -284.8177 | -11.6191 | -12.6919 |
| 0.0 | 31.84 | 7800 | 0.0000 | 20.7512 | -10.7255 | 1.0 | 31.4766 | -92.4904 | -284.8107 | -11.6191 | -12.6921 |
| 0.0 | 32.24 | 7900 | 0.0000 | 20.7510 | -10.7372 | 1.0 | 31.4881 | -92.5138 | -284.8111 | -11.6195 | -12.6924 |
| 0.0 | 32.65 | 8000 | 0.0000 | 20.7508 | -10.7382 | 1.0 | 31.4890 | -92.5158 | -284.8114 | -11.6194 | -12.6924 |
### Framework versions
- PEFT 0.7.1
- Transformers 4.36.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.15.0 |
PelagiaKalpakidou/finetuning-Twitter-sentiment-model | PelagiaKalpakidou | 2023-12-28T22:54:39Z | 12 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2023-12-28T00:26:19Z | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: finetuning-Twitter-sentiment-model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-Twitter-sentiment-model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2958
- Accuracy: 0.7471
- Precision: 0.7506
- Recall: 0.7471
- F1: 0.7468
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.0367 | 1.0 | 606 | 2.3678 | 0.7496 | 0.7642 | 0.7496 | 0.7484 |
| 0.0133 | 2.0 | 1212 | 2.2958 | 0.7471 | 0.7506 | 0.7471 | 0.7468 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0
|
ntc-ai/SDXL-LoRA-slider.studio-lighting | ntc-ai | 2023-12-28T22:53:10Z | 61 | 0 | diffusers | [
"diffusers",
"text-to-image",
"stable-diffusion-xl",
"lora",
"template:sd-lora",
"template:sdxl-lora",
"sdxl-sliders",
"ntcai.xyz-sliders",
"concept",
"en",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:mit",
"region:us"
] | text-to-image | 2023-12-28T22:53:07Z |
---
language:
- en
thumbnail: "images/evaluate/studio lighting.../studio lighting_17_3.0.png"
widget:
- text: studio lighting
output:
url: images/studio lighting_17_3.0.png
- text: studio lighting
output:
url: images/studio lighting_19_3.0.png
- text: studio lighting
output:
url: images/studio lighting_20_3.0.png
- text: studio lighting
output:
url: images/studio lighting_21_3.0.png
- text: studio lighting
output:
url: images/studio lighting_22_3.0.png
tags:
- text-to-image
- stable-diffusion-xl
- lora
- template:sd-lora
- template:sdxl-lora
- sdxl-sliders
- ntcai.xyz-sliders
- concept
- diffusers
license: "mit"
inference: false
instance_prompt: "studio lighting"
base_model: "stabilityai/stable-diffusion-xl-base-1.0"
---
# ntcai.xyz slider - studio lighting (SDXL LoRA)
| Strength: -3 | Strength: 0 | Strength: 3 |
| --- | --- | --- |
| <img src="images/studio lighting_17_-3.0.png" width=256 height=256 /> | <img src="images/studio lighting_17_0.0.png" width=256 height=256 /> | <img src="images/studio lighting_17_3.0.png" width=256 height=256 /> |
| <img src="images/studio lighting_19_-3.0.png" width=256 height=256 /> | <img src="images/studio lighting_19_0.0.png" width=256 height=256 /> | <img src="images/studio lighting_19_3.0.png" width=256 height=256 /> |
| <img src="images/studio lighting_20_-3.0.png" width=256 height=256 /> | <img src="images/studio lighting_20_0.0.png" width=256 height=256 /> | <img src="images/studio lighting_20_3.0.png" width=256 height=256 /> |
## Download
Weights for this model are available in Safetensors format.
## Trigger words
You can apply this LoRA with trigger words for additional effect:
```
studio lighting
```
## Use in diffusers
```python
from diffusers import StableDiffusionXLPipeline
from diffusers import EulerAncestralDiscreteScheduler
import torch
pipe = StableDiffusionXLPipeline.from_single_file("https://huggingface.co/martyn/sdxl-turbo-mario-merge-top-rated/blob/main/topRatedTurboxlLCM_v10.safetensors")
pipe.to("cuda")
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
# Load the LoRA
pipe.load_lora_weights('ntc-ai/SDXL-LoRA-slider.studio-lighting', weight_name='studio lighting.safetensors', adapter_name="studio lighting")
# Activate the LoRA
pipe.set_adapters(["studio lighting"], adapter_weights=[2.0])
prompt = "medieval rich kingpin sitting in a tavern, studio lighting"
negative_prompt = "nsfw"
width = 512
height = 512
num_inference_steps = 10
guidance_scale = 2
image = pipe(prompt, negative_prompt=negative_prompt, width=width, height=height, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps).images[0]
image.save('result.png')
```
## Support the Patreon
If you like this model please consider [joining our Patreon](https://www.patreon.com/NTCAI).
By joining our Patreon, you'll gain access to an ever-growing library of over 700+ unique and diverse LoRAs, covering a wide range of styles and genres. You'll also receive early access to new models and updates, exclusive behind-the-scenes content, and the powerful LoRA slider creator, allowing you to craft your own custom LoRAs and experiment with endless possibilities.
Your support on Patreon will allow us to continue developing and refining new models.
## Other resources
- [CivitAI](https://civitai.com/user/ntc) - Follow ntc on Civit for even more LoRAs
- [ntcai.xyz](https://ntcai.xyz) - See ntcai.xyz to find more articles and LoRAs
|
Mariszka/whisper-medium-cs-cv11 | Mariszka | 2023-12-28T22:48:46Z | 8 | 0 | transformers | [
"transformers",
"safetensors",
"whisper",
"automatic-speech-recognition",
"whisper-event",
"hf-asr-leaderboard",
"generated_from_trainer",
"cs",
"dataset:mozilla-foundation/common_voice_11_0",
"base_model:openai/whisper-medium",
"base_model:finetune:openai/whisper-medium",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2023-12-28T21:45:23Z | ---
language:
- cs
license: apache-2.0
tags:
- whisper-event
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
base_model: openai/whisper-medium
model-index:
- name: Whisper Medium Czech CV11
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: mozilla-foundation/common_voice_11_0
type: mozilla-foundation/common_voice_11_0
config: cs
split: test
metrics:
- type: wer
value: 11.689339690370561
name: Wer
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Medium Czech CV11
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the mozilla-foundation/common_voice_11_0 cs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2537
- Wer: 11.6893
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0384 | 2.02 | 1000 | 0.2167 | 13.5467 |
| 0.0061 | 4.03 | 2000 | 0.2373 | 12.9172 |
| 0.0018 | 6.05 | 3000 | 0.2407 | 12.0409 |
| 0.0007 | 8.07 | 4000 | 0.2463 | 11.7685 |
| 0.0003 | 10.09 | 5000 | 0.2537 | 11.6893 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
|
LoneStriker/Sakura-SOLAR-Instruct-DPO-v2-8.0bpw-h8-exl2 | LoneStriker | 2023-12-28T22:26:18Z | 8 | 2 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"en",
"dataset:argilla/distilabel-math-preference-dpo",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2023-12-28T22:21:47Z | ---
language:
- en
datasets:
- argilla/distilabel-math-preference-dpo
pipeline_tag: text-generation
license: cc-by-nc-sa-4.0
---
# **Sakura-SOLAR-Instruct-DPO-v2**
<img src='./sakura.png' width=512>
**(주)미디어그룹사람과숲과 (주)마커의 LLM 연구 컨소시엄에서 개발된 모델입니다**
## Model Details
**Model Developers** Kyujin Han (kyujinpy)
**Method**
Using DPO method.
With [argilla/distilabel-math-preference-dpo](https://huggingface.co/datasets/argilla/distilabel-math-preference-dpo).
I shared the information about my model. (training and code)
Please see: ⭐[Sakura-SOLAR](https://github.com/KyujinHan/Sakura-SOLAR-DPO).
# **Model Benchmark**
## Open leaderboard
- Follow up as [link](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
| Model | Average | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Sakura-SOLAR-Instruct-DPO-v2 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| Sakura-SOLAR-Instruct-DPO-v1 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| [kyujinpy/Sakura-SOLAR-Instruct](https://huggingface.co/kyujinpy/Sakura-SOLAR-Instruct) | 74.40 | 70.99 | 88.42 | 66.33 | 71.79 | 83.66 | 65.20
# Implementation Code
```python
### KO-Platypus
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
repo = "kyujinpy/Sakura-SOLAR-Instruct-DPO-v2"
OpenOrca = AutoModelForCausalLM.from_pretrained(
repo,
return_dict=True,
torch_dtype=torch.float16,
device_map='auto'
)
OpenOrca_tokenizer = AutoTokenizer.from_pretrained(repo)
```
--- |
HorikawaMegu/LunarLander-v2 | HorikawaMegu | 2023-12-28T22:19:35Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2023-12-06T19:26:13Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 251.87 +/- 17.44
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
omosaiye/distilbert-base-uncased-lora-text-classification | omosaiye | 2023-12-28T22:18:24Z | 0 | 0 | null | [
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"region:us"
] | null | 2023-12-28T22:16:00Z | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-lora-text-classification
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-lora-text-classification
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8923
- Accuracy: {'accuracy': 0.895}
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:-------------------:|
| No log | 1.0 | 250 | 0.5889 | {'accuracy': 0.838} |
| 0.4397 | 2.0 | 500 | 0.5252 | {'accuracy': 0.862} |
| 0.4397 | 3.0 | 750 | 0.6337 | {'accuracy': 0.869} |
| 0.2288 | 4.0 | 1000 | 0.6840 | {'accuracy': 0.881} |
| 0.2288 | 5.0 | 1250 | 0.8608 | {'accuracy': 0.883} |
| 0.0786 | 6.0 | 1500 | 0.7593 | {'accuracy': 0.895} |
| 0.0786 | 7.0 | 1750 | 0.7988 | {'accuracy': 0.892} |
| 0.0299 | 8.0 | 2000 | 0.8439 | {'accuracy': 0.889} |
| 0.0299 | 9.0 | 2250 | 0.8963 | {'accuracy': 0.895} |
| 0.0112 | 10.0 | 2500 | 0.8923 | {'accuracy': 0.895} |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.2
- Datasets 2.16.0
- Tokenizers 0.13.2
|
bartowski/CatPPT-exl2 | bartowski | 2023-12-28T22:17:46Z | 0 | 0 | peft | [
"peft",
"generated_from_trainer",
"text-generation",
"en",
"dataset:HuggingFaceH4/no_robots",
"base_model:rishiraj/CatPPT-base",
"base_model:adapter:rishiraj/CatPPT-base",
"license:apache-2.0",
"region:us"
] | text-generation | 2023-12-28T20:40:28Z | ---
license: apache-2.0
library_name: peft
base_model: rishiraj/CatPPT-base
datasets:
- HuggingFaceH4/no_robots
language:
- en
widget:
- text: |
<|system|>
You are a friendly chatbot who always responds in the style of a pirate</s>
<|user|>
How many helicopters can a human eat in one sitting?</s>
<|assistant|>
output:
text: >-
Aye, me hearties! 'Tis not likely a human can eat a helicopter in any sittin', let alone one! They be too big and made of metal, and not fit for consumption. But if ye be referrin' to helicopter snacks, like nuts and trail mix, then a human might be able to munch a goodly amount in one sittin'. Arr!
tags:
- generated_from_trainer
pipeline_tag: text-generation
model-index:
- name: CatPPT
results: []
quantized_by: bartowski
---
## Exllama v2 Quantizations of CatPPT
Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.11">turboderp's ExLlamaV2 v0.0.11</a> for quantization.
Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions.
Conversion was done using the default calibration dataset.
Default arguments used except when the bits per weight is above 6.0, at that point the lm_head layer is quantized at 8 bits per weight instead of the default 6.
Original model: https://huggingface.co/rishiraj/CatPPT
<a href="https://huggingface.co/bartowski/CatPPT-exl2/tree/4_0">4.0 bits per weight</a>
<a href="https://huggingface.co/bartowski/CatPPT-exl2/tree/5_0">5.0 bits per weight</a>
<a href="https://huggingface.co/bartowski/CatPPT-exl2/tree/6_5">6.5 bits per weight</a>
<a href="https://huggingface.co/bartowski/CatPPT-exl2/tree/8_0">8.0 bits per weight</a>
## Download instructions
With git:
```shell
git clone --single-branch --branch 4_0 https://huggingface.co/bartowski/CatPPT-exl2
```
With huggingface hub (credit to TheBloke for instructions):
```shell
pip3 install huggingface-hub
```
To download the `main` (only useful if you only care about measurement.json) branch to a folder called `CatPPT-exl2`:
```shell
mkdir CatPPT-exl2
huggingface-cli download bartowski/CatPPT-exl2 --local-dir CatPPT-exl2 --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
```shell
mkdir CatPPT-exl2
huggingface-cli download bartowski/CatPPT-exl2 --revision 4_0 --local-dir CatPPT-exl2 --local-dir-use-symlinks False
```
|
hynky/codellama-7b-sft-lora-func-names-4bit | hynky | 2023-12-28T22:09:36Z | 2 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:codellama/CodeLlama-7b-hf",
"base_model:adapter:codellama/CodeLlama-7b-hf",
"region:us"
] | null | 2023-12-28T22:08:50Z | ---
library_name: peft
base_model: codellama/CodeLlama-7b-hf
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.6.1
|
toddwilson147/ppo-pyramids | toddwilson147 | 2023-12-28T21:30:11Z | 12 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | reinforcement-learning | 2023-12-28T19:16:10Z | ---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: toddwilson147/ppo-pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
jeiku/Writing_Mistral | jeiku | 2023-12-28T21:24:16Z | 42 | 0 | peft | [
"peft",
"safetensors",
"en",
"license:cc-by-sa-4.0",
"region:us"
] | null | 2023-12-28T21:22:28Z | ---
library_name: peft
base_model: models/jeiku_Rosa_v1_7B
license: cc-by-sa-4.0
language:
- en
--- |
aaneesai/openai-whisper-tiny-LORA-train_data_norefer_thres_05-text_best | aaneesai | 2023-12-28T21:19:24Z | 3 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:openai/whisper-tiny",
"base_model:adapter:openai/whisper-tiny",
"region:us"
] | null | 2023-12-28T21:19:23Z | ---
library_name: peft
base_model: openai/whisper-tiny
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.7.2.dev0 |
LoneStriker/notux-8x7b-v1-5.0bpw-h6-exl2 | LoneStriker | 2023-12-28T21:16:54Z | 7 | 0 | transformers | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"dpo",
"rlaif",
"preference",
"ultrafeedback",
"conversational",
"en",
"de",
"es",
"fr",
"it",
"dataset:argilla/ultrafeedback-binarized-preferences-cleaned",
"base_model:mistralai/Mixtral-8x7B-Instruct-v0.1",
"base_model:finetune:mistralai/Mixtral-8x7B-Instruct-v0.1",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2023-12-28T21:02:25Z | ---
datasets:
- argilla/ultrafeedback-binarized-preferences-cleaned
language:
- en
- de
- es
- fr
- it
base_model: mistralai/Mixtral-8x7B-Instruct-v0.1
library_name: transformers
pipeline_tag: text-generation
tags:
- dpo
- rlaif
- preference
- ultrafeedback
license: apache-2.0
model-index:
- name: notux-8x7b-v1
results: []
---
<div align="center">
<img src="https://cdn-uploads.huggingface.co/production/uploads/60f0608166e5701b80ed3f02/dj-spsk9eXMMXVGxK6jRz.png" alt="A banner representing Notus, the wind god of the south, in a mythical and artistic style. The banner features a strong, swirling breeze, embodying the warm, wet character of the southern wind. Gracefully flowing across the scene are several paper planes, caught in the gentle yet powerful gusts of Notus. The background is a blend of warm colors, symbolizing the heat of the south, with hints of blue and green to represent the moisture carried by this wind. The overall atmosphere is one of dynamic movement and warmth."/>
</div>
# Model Card for Notux 8x7B-v1
This model is a preference-tuned version of [mistralai/Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) on the [argilla/ultrafeedback-binarized-preferences-cleaned](https://huggingface.co/datasets/argilla/ultrafeedback-binarized-preferences-cleaned) dataset using DPO (Direct Preference Optimization).
As of Dec 26th 2023, it outperforms `Mixtral-8x7B-Instruct-v0.1` and is the top ranked MoE (Mixture of Experts) model on the [Hugging Face Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
This is part of the Notus family of models and experiments, where the Argilla team investigates data-first and preference tuning methods like dDPO (distilled DPO). This model is the result of our first experiment at tuning a MoE model that has already been fine-tuned with DPO (i.e., Mixtral-8x7B-Instruct-v0.1).
## Model Details
### Model Description
- **Developed by:** Argilla (based on MistralAI previous efforts)
- **Shared by:** Argilla
- **Model type:** Pretrained generative Sparse Mixture of Experts
- **Language(s) (NLP):** English, Spanish, Italian, German, and French
- **License:** MIT
- **Finetuned from model:** [mistralai/Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1)
### Model Sources
- **Repository:** https://github.com/argilla-io/notus
- **Paper:** N/A
## Training Details
### Training Hardware
We used a VM with 8 x H100 80GB hosted in runpod.io for 1 epoch (~10hr).
### Training Data
We used a new iteration of the Argilla UltraFeedback preferences dataset named [argilla/ultrafeedback-binarized-preferences-cleaned](https://huggingface.co/datasets/argilla/ultrafeedback-binarized-preferences-cleaned).
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-07
- train_batch_size: 8
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 64
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.4384 | 0.22 | 200 | 0.4556 | -0.3275 | -1.9448 | 0.7937 | 1.6174 | -405.7994 | -397.8617 | -1.3157 | -1.4511 |
| 0.4064 | 0.43 | 400 | 0.4286 | -0.2163 | -2.2090 | 0.8254 | 1.9927 | -408.4409 | -396.7496 | -0.7660 | -0.6539 |
| 0.3952 | 0.65 | 600 | 0.4275 | -0.1311 | -2.1603 | 0.8016 | 2.0291 | -407.9537 | -395.8982 | -0.6783 | -0.7206 |
| 0.3909 | 0.87 | 800 | 0.4167 | -0.2273 | -2.3146 | 0.8135 | 2.0872 | -409.4968 | -396.8602 | -0.8458 | -0.7738 |
### Framework versions
- Transformers 4.36.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.15.0 |
PranavHonrao/Reinforce-Pixelcopter-PLE-v0 | PranavHonrao | 2023-12-28T21:11:31Z | 0 | 0 | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | reinforcement-learning | 2023-12-28T21:07:50Z | ---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 38.30 +/- 26.84
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
hillol7/Llama-2-7b-chat-hf-sharded-bf16-fine-tuned-adapters | hillol7 | 2023-12-28T21:07:31Z | 5 | 0 | peft | [
"peft",
"arxiv:1910.09700",
"base_model:Trelis/Llama-2-7b-chat-hf-sharded-bf16",
"base_model:adapter:Trelis/Llama-2-7b-chat-hf-sharded-bf16",
"region:us"
] | null | 2023-12-26T22:59:38Z | ---
library_name: peft
base_model: Trelis/Llama-2-7b-chat-hf-sharded-bf16
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.7.2.dev0 |
ntheodora/ghibli-lora | ntheodora | 2023-12-28T21:03:44Z | 6 | 0 | diffusers | [
"diffusers",
"tensorboard",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"base_model:stablediffusionapi/disney-pixar-cartoon",
"base_model:adapter:stablediffusionapi/disney-pixar-cartoon",
"license:creativeml-openrail-m",
"region:us"
] | text-to-image | 2023-12-28T01:11:29Z |
---
license: creativeml-openrail-m
base_model: stablediffusionapi/disney-pixar-cartoon
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA text2image fine-tuning - ntheodora/ghibli-lora
These are LoRA adaption weights for stablediffusionapi/disney-pixar-cartoon. The weights were fine-tuned on the bizoffermark/nerdy-ghibli dataset. You can find some example images in the following.
|
aaneesai/openai-whisper-tiny-LORA-train_data_postedit_clustered_random-text_trg | aaneesai | 2023-12-28T21:02:15Z | 1 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:openai/whisper-tiny",
"base_model:adapter:openai/whisper-tiny",
"region:us"
] | null | 2023-12-28T21:02:15Z | ---
library_name: peft
base_model: openai/whisper-tiny
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.7.2.dev0 |
LoneStriker/Sakura-SOLAR-Instruct-DPO-v2-3.0bpw-h6-exl2 | LoneStriker | 2023-12-28T21:02:02Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"en",
"dataset:argilla/distilabel-math-preference-dpo",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2023-12-28T21:00:01Z | ---
language:
- en
datasets:
- argilla/distilabel-math-preference-dpo
pipeline_tag: text-generation
license: cc-by-nc-sa-4.0
---
# **Sakura-SOLAR-Instruct-DPO-v2**
<img src='./sakura.png' width=512>
**(주)미디어그룹사람과숲과 (주)마커의 LLM 연구 컨소시엄에서 개발된 모델입니다**
## Model Details
**Model Developers** Kyujin Han (kyujinpy)
**Method**
Using DPO method.
With [argilla/distilabel-math-preference-dpo](https://huggingface.co/datasets/argilla/distilabel-math-preference-dpo).
I shared the information about my model. (training and code)
Please see: ⭐[Sakura-SOLAR](https://github.com/KyujinHan/Sakura-SOLAR-DPO).
# **Model Benchmark**
## Open leaderboard
- Follow up as [link](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
| Model | Average | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Sakura-SOLAR-Instruct-DPO-v2 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| Sakura-SOLAR-Instruct-DPO-v1 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| [kyujinpy/Sakura-SOLAR-Instruct](https://huggingface.co/kyujinpy/Sakura-SOLAR-Instruct) | 74.40 | 70.99 | 88.42 | 66.33 | 71.79 | 83.66 | 65.20
# Implementation Code
```python
### KO-Platypus
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
repo = "kyujinpy/Sakura-SOLAR-Instruct-DPO-v2"
OpenOrca = AutoModelForCausalLM.from_pretrained(
repo,
return_dict=True,
torch_dtype=torch.float16,
device_map='auto'
)
OpenOrca_tokenizer = AutoTokenizer.from_pretrained(repo)
```
--- |
LoneStriker/Sakura-SOLAR-Instruct-4.0bpw-h6-exl2 | LoneStriker | 2023-12-28T20:58:04Z | 6 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"en",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2023-12-28T20:55:35Z | ---
language:
- en
pipeline_tag: text-generation
license: cc-by-nc-sa-4.0
---
# **Sakura-SOLAR-Instruct**
<img src='./sakura.png' width=512>
**(주)미디어그룹사람과숲과 (주)마커의 LLM 연구 컨소시엄에서 개발된 모델입니다**
## Model Details
**Model Developers** Kyujin Han (kyujinpy)
**Method**
Using [Mergekit](https://github.com/cg123/mergekit).
I shared the information about my model. (training and code)
**Please see: [⭐Sakura-SOLAR](https://github.com/KyujinHan/Sakura-SOLAR-DPO).**
**Blog**
- [Sakura-SOLAR 모델 제작 과정 및 후기](https://kyujinpy.tistory.com/122).
# **Model Benchmark**
## Open leaderboard
- Follow up as [link](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
| Model | Average | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Sakura-SOLAR-Instruct-DPO-v2 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| Sakura-SOLAR-Instruct-DPO-v1 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| [kyujinpy/Sakura-SOLAR-Instruct](https://huggingface.co/kyujinpy/Sakura-SOLAR-Instruct) | 74.40 | 70.99 | 88.42 | 66.33 | 71.79 | 83.66 | 65.20
> Rank1 2023.12.27 PM 11:50
# Implementation Code
```python
### KO-Platypus
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
repo = "kyujinpy/Sakura-SOLAR-Instruct"
OpenOrca = AutoModelForCausalLM.from_pretrained(
repo,
return_dict=True,
torch_dtype=torch.float16,
device_map='auto'
)
OpenOrca_tokenizer = AutoTokenizer.from_pretrained(repo)
```
--- |
ThuyNT03/KLTN_Uni_COQE_viT5_total_ASPOL_v1 | ThuyNT03 | 2023-12-28T20:57:23Z | 5 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:VietAI/vit5-large",
"base_model:finetune:VietAI/vit5-large",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2023-12-28T17:51:54Z | ---
license: mit
base_model: VietAI/vit5-large
tags:
- generated_from_trainer
model-index:
- name: KLTN_Uni_COQE_viT5_total_ASPOL_v1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# KLTN_Uni_COQE_viT5_total_ASPOL_v1
This model is a fine-tuned version of [VietAI/vit5-large](https://huggingface.co/VietAI/vit5-large) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.36.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.15.0
|
LoneStriker/notux-8x7b-v1-4.0bpw-h6-exl2 | LoneStriker | 2023-12-28T20:56:19Z | 7 | 0 | transformers | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"dpo",
"rlaif",
"preference",
"ultrafeedback",
"conversational",
"en",
"de",
"es",
"fr",
"it",
"dataset:argilla/ultrafeedback-binarized-preferences-cleaned",
"base_model:mistralai/Mixtral-8x7B-Instruct-v0.1",
"base_model:finetune:mistralai/Mixtral-8x7B-Instruct-v0.1",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2023-12-28T20:46:18Z | ---
datasets:
- argilla/ultrafeedback-binarized-preferences-cleaned
language:
- en
- de
- es
- fr
- it
base_model: mistralai/Mixtral-8x7B-Instruct-v0.1
library_name: transformers
pipeline_tag: text-generation
tags:
- dpo
- rlaif
- preference
- ultrafeedback
license: apache-2.0
model-index:
- name: notux-8x7b-v1
results: []
---
<div align="center">
<img src="https://cdn-uploads.huggingface.co/production/uploads/60f0608166e5701b80ed3f02/dj-spsk9eXMMXVGxK6jRz.png" alt="A banner representing Notus, the wind god of the south, in a mythical and artistic style. The banner features a strong, swirling breeze, embodying the warm, wet character of the southern wind. Gracefully flowing across the scene are several paper planes, caught in the gentle yet powerful gusts of Notus. The background is a blend of warm colors, symbolizing the heat of the south, with hints of blue and green to represent the moisture carried by this wind. The overall atmosphere is one of dynamic movement and warmth."/>
</div>
# Model Card for Notux 8x7B-v1
This model is a preference-tuned version of [mistralai/Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) on the [argilla/ultrafeedback-binarized-preferences-cleaned](https://huggingface.co/datasets/argilla/ultrafeedback-binarized-preferences-cleaned) dataset using DPO (Direct Preference Optimization).
As of Dec 26th 2023, it outperforms `Mixtral-8x7B-Instruct-v0.1` and is the top ranked MoE (Mixture of Experts) model on the [Hugging Face Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
This is part of the Notus family of models and experiments, where the Argilla team investigates data-first and preference tuning methods like dDPO (distilled DPO). This model is the result of our first experiment at tuning a MoE model that has already been fine-tuned with DPO (i.e., Mixtral-8x7B-Instruct-v0.1).
## Model Details
### Model Description
- **Developed by:** Argilla (based on MistralAI previous efforts)
- **Shared by:** Argilla
- **Model type:** Pretrained generative Sparse Mixture of Experts
- **Language(s) (NLP):** English, Spanish, Italian, German, and French
- **License:** MIT
- **Finetuned from model:** [mistralai/Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1)
### Model Sources
- **Repository:** https://github.com/argilla-io/notus
- **Paper:** N/A
## Training Details
### Training Hardware
We used a VM with 8 x H100 80GB hosted in runpod.io for 1 epoch (~10hr).
### Training Data
We used a new iteration of the Argilla UltraFeedback preferences dataset named [argilla/ultrafeedback-binarized-preferences-cleaned](https://huggingface.co/datasets/argilla/ultrafeedback-binarized-preferences-cleaned).
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-07
- train_batch_size: 8
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 64
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.4384 | 0.22 | 200 | 0.4556 | -0.3275 | -1.9448 | 0.7937 | 1.6174 | -405.7994 | -397.8617 | -1.3157 | -1.4511 |
| 0.4064 | 0.43 | 400 | 0.4286 | -0.2163 | -2.2090 | 0.8254 | 1.9927 | -408.4409 | -396.7496 | -0.7660 | -0.6539 |
| 0.3952 | 0.65 | 600 | 0.4275 | -0.1311 | -2.1603 | 0.8016 | 2.0291 | -407.9537 | -395.8982 | -0.6783 | -0.7206 |
| 0.3909 | 0.87 | 800 | 0.4167 | -0.2273 | -2.3146 | 0.8135 | 2.0872 | -409.4968 | -396.8602 | -0.8458 | -0.7738 |
### Framework versions
- Transformers 4.36.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.15.0 |
pasanifas/Felixlee | pasanifas | 2023-12-28T20:56:02Z | 0 | 0 | null | [
"license:other",
"region:us"
] | null | 2023-12-28T20:34:08Z | ---
license: other
license_name: other
license_link: >-
https://drive.google.com/file/d/1X9VoPytLnAeVNYNz6YixIjQFQLLt_yT7/view?usp=sharing
---
|
Ngit/peft-lora-deepseek6.7b-v2 | Ngit | 2023-12-28T20:51:34Z | 3 | 0 | peft | [
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:deepseek-ai/deepseek-coder-6.7b-instruct",
"base_model:adapter:deepseek-ai/deepseek-coder-6.7b-instruct",
"license:other",
"region:us"
] | null | 2023-12-28T17:21:28Z | ---
license: other
library_name: peft
tags:
- generated_from_trainer
base_model: deepseek-ai/deepseek-coder-6.7b-instruct
model-index:
- name: peft-lora-deepseek6.7b-v2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# peft-lora-deepseek6.7b-v2
This model is a fine-tuned version of [deepseek-ai/deepseek-coder-6.7b-instruct](https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-instruct) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 6
- eval_batch_size: 6
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 24
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 30
- num_epochs: 4
### Training results
### Framework versions
- PEFT 0.7.2.dev0
- Transformers 4.37.0.dev0
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0 |
M2UGen/M2UGen-MusicGen-small | M2UGen | 2023-12-28T20:41:49Z | 0 | 6 | null | [
"arxiv:2311.11255",
"license:mit",
"region:us"
] | null | 2023-12-03T07:08:48Z | ---
license: mit
---
# M<sup>2</sup>UGen Model with MusicGen-small
The M<sup>2</sup>UGen model is a Music Understanding and Generation model that is capable of Music Question Answering and also Music Generation
from texts, images, videos and audios, as well as Music Editing. The model utilizes encoders such as MERT for music understanding, ViT for image understanding
and ViViT for video understanding and the MusicGen/AudioLDM2 model as the music generation model (music decoder), coupled with adapters and the LLaMA 2 model
to make the model possible for multiple abilities.
M<sup>2</sup>UGen was published in [M<sup>2</sup>UGen: Multi-modal Music Understanding and Generation with the Power of Large Language Models](https://arxiv.org/abs/2311.11255) by *Atin Sakkeer Hussain, Shansong Liu, Chenshuo Sun and Ying Shan*.
The code repository for the model is published in [crypto-code/M2UGen](https://github.com/crypto-code/M2UGen). Clone the repository, download the checkpoint and run the following for a model demo:
```bash
python gradio_app.py --model ./ckpts/M2UGen-MusicGen-small/checkpoint.pth --llama_dir ./ckpts/LLaMA-2 --music_decoder musicgen --music_decoder_path facebook/musicgen-small
```
## Citation
If you find this model useful, please consider citing:
```bibtex
@article{hussain2023m,
title={{M$^{2}$UGen: Multi-modal Music Understanding and Generation with the Power of Large Language Models}},
author={Hussain, Atin Sakkeer and Liu, Shansong and Sun, Chenshuo and Shan, Ying},
journal={arXiv preprint arXiv:2311.11255},
year={2023}
}
``` |
LoneStriker/notux-8x7b-v1-3.75bpw-h6-exl2 | LoneStriker | 2023-12-28T20:39:12Z | 8 | 0 | transformers | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"dpo",
"rlaif",
"preference",
"ultrafeedback",
"conversational",
"en",
"de",
"es",
"fr",
"it",
"dataset:argilla/ultrafeedback-binarized-preferences-cleaned",
"base_model:mistralai/Mixtral-8x7B-Instruct-v0.1",
"base_model:finetune:mistralai/Mixtral-8x7B-Instruct-v0.1",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2023-12-28T20:30:13Z | ---
datasets:
- argilla/ultrafeedback-binarized-preferences-cleaned
language:
- en
- de
- es
- fr
- it
base_model: mistralai/Mixtral-8x7B-Instruct-v0.1
library_name: transformers
pipeline_tag: text-generation
tags:
- dpo
- rlaif
- preference
- ultrafeedback
license: apache-2.0
model-index:
- name: notux-8x7b-v1
results: []
---
<div align="center">
<img src="https://cdn-uploads.huggingface.co/production/uploads/60f0608166e5701b80ed3f02/dj-spsk9eXMMXVGxK6jRz.png" alt="A banner representing Notus, the wind god of the south, in a mythical and artistic style. The banner features a strong, swirling breeze, embodying the warm, wet character of the southern wind. Gracefully flowing across the scene are several paper planes, caught in the gentle yet powerful gusts of Notus. The background is a blend of warm colors, symbolizing the heat of the south, with hints of blue and green to represent the moisture carried by this wind. The overall atmosphere is one of dynamic movement and warmth."/>
</div>
# Model Card for Notux 8x7B-v1
This model is a preference-tuned version of [mistralai/Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) on the [argilla/ultrafeedback-binarized-preferences-cleaned](https://huggingface.co/datasets/argilla/ultrafeedback-binarized-preferences-cleaned) dataset using DPO (Direct Preference Optimization).
As of Dec 26th 2023, it outperforms `Mixtral-8x7B-Instruct-v0.1` and is the top ranked MoE (Mixture of Experts) model on the [Hugging Face Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
This is part of the Notus family of models and experiments, where the Argilla team investigates data-first and preference tuning methods like dDPO (distilled DPO). This model is the result of our first experiment at tuning a MoE model that has already been fine-tuned with DPO (i.e., Mixtral-8x7B-Instruct-v0.1).
## Model Details
### Model Description
- **Developed by:** Argilla (based on MistralAI previous efforts)
- **Shared by:** Argilla
- **Model type:** Pretrained generative Sparse Mixture of Experts
- **Language(s) (NLP):** English, Spanish, Italian, German, and French
- **License:** MIT
- **Finetuned from model:** [mistralai/Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1)
### Model Sources
- **Repository:** https://github.com/argilla-io/notus
- **Paper:** N/A
## Training Details
### Training Hardware
We used a VM with 8 x H100 80GB hosted in runpod.io for 1 epoch (~10hr).
### Training Data
We used a new iteration of the Argilla UltraFeedback preferences dataset named [argilla/ultrafeedback-binarized-preferences-cleaned](https://huggingface.co/datasets/argilla/ultrafeedback-binarized-preferences-cleaned).
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-07
- train_batch_size: 8
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 64
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.4384 | 0.22 | 200 | 0.4556 | -0.3275 | -1.9448 | 0.7937 | 1.6174 | -405.7994 | -397.8617 | -1.3157 | -1.4511 |
| 0.4064 | 0.43 | 400 | 0.4286 | -0.2163 | -2.2090 | 0.8254 | 1.9927 | -408.4409 | -396.7496 | -0.7660 | -0.6539 |
| 0.3952 | 0.65 | 600 | 0.4275 | -0.1311 | -2.1603 | 0.8016 | 2.0291 | -407.9537 | -395.8982 | -0.6783 | -0.7206 |
| 0.3909 | 0.87 | 800 | 0.4167 | -0.2273 | -2.3146 | 0.8135 | 2.0872 | -409.4968 | -396.8602 | -0.8458 | -0.7738 |
### Framework versions
- Transformers 4.36.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.15.0 |
LoneStriker/notux-8x7b-v1-3.5bpw-h6-exl2 | LoneStriker | 2023-12-28T20:22:56Z | 8 | 1 | transformers | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"dpo",
"rlaif",
"preference",
"ultrafeedback",
"conversational",
"en",
"de",
"es",
"fr",
"it",
"dataset:argilla/ultrafeedback-binarized-preferences-cleaned",
"base_model:mistralai/Mixtral-8x7B-Instruct-v0.1",
"base_model:finetune:mistralai/Mixtral-8x7B-Instruct-v0.1",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2023-12-28T20:14:12Z | ---
datasets:
- argilla/ultrafeedback-binarized-preferences-cleaned
language:
- en
- de
- es
- fr
- it
base_model: mistralai/Mixtral-8x7B-Instruct-v0.1
library_name: transformers
pipeline_tag: text-generation
tags:
- dpo
- rlaif
- preference
- ultrafeedback
license: apache-2.0
model-index:
- name: notux-8x7b-v1
results: []
---
<div align="center">
<img src="https://cdn-uploads.huggingface.co/production/uploads/60f0608166e5701b80ed3f02/dj-spsk9eXMMXVGxK6jRz.png" alt="A banner representing Notus, the wind god of the south, in a mythical and artistic style. The banner features a strong, swirling breeze, embodying the warm, wet character of the southern wind. Gracefully flowing across the scene are several paper planes, caught in the gentle yet powerful gusts of Notus. The background is a blend of warm colors, symbolizing the heat of the south, with hints of blue and green to represent the moisture carried by this wind. The overall atmosphere is one of dynamic movement and warmth."/>
</div>
# Model Card for Notux 8x7B-v1
This model is a preference-tuned version of [mistralai/Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) on the [argilla/ultrafeedback-binarized-preferences-cleaned](https://huggingface.co/datasets/argilla/ultrafeedback-binarized-preferences-cleaned) dataset using DPO (Direct Preference Optimization).
As of Dec 26th 2023, it outperforms `Mixtral-8x7B-Instruct-v0.1` and is the top ranked MoE (Mixture of Experts) model on the [Hugging Face Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
This is part of the Notus family of models and experiments, where the Argilla team investigates data-first and preference tuning methods like dDPO (distilled DPO). This model is the result of our first experiment at tuning a MoE model that has already been fine-tuned with DPO (i.e., Mixtral-8x7B-Instruct-v0.1).
## Model Details
### Model Description
- **Developed by:** Argilla (based on MistralAI previous efforts)
- **Shared by:** Argilla
- **Model type:** Pretrained generative Sparse Mixture of Experts
- **Language(s) (NLP):** English, Spanish, Italian, German, and French
- **License:** MIT
- **Finetuned from model:** [mistralai/Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1)
### Model Sources
- **Repository:** https://github.com/argilla-io/notus
- **Paper:** N/A
## Training Details
### Training Hardware
We used a VM with 8 x H100 80GB hosted in runpod.io for 1 epoch (~10hr).
### Training Data
We used a new iteration of the Argilla UltraFeedback preferences dataset named [argilla/ultrafeedback-binarized-preferences-cleaned](https://huggingface.co/datasets/argilla/ultrafeedback-binarized-preferences-cleaned).
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-07
- train_batch_size: 8
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 64
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.4384 | 0.22 | 200 | 0.4556 | -0.3275 | -1.9448 | 0.7937 | 1.6174 | -405.7994 | -397.8617 | -1.3157 | -1.4511 |
| 0.4064 | 0.43 | 400 | 0.4286 | -0.2163 | -2.2090 | 0.8254 | 1.9927 | -408.4409 | -396.7496 | -0.7660 | -0.6539 |
| 0.3952 | 0.65 | 600 | 0.4275 | -0.1311 | -2.1603 | 0.8016 | 2.0291 | -407.9537 | -395.8982 | -0.6783 | -0.7206 |
| 0.3909 | 0.87 | 800 | 0.4167 | -0.2273 | -2.3146 | 0.8135 | 2.0872 | -409.4968 | -396.8602 | -0.8458 | -0.7738 |
### Framework versions
- Transformers 4.36.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.15.0 |
max-0-0-1/ppo-Huggy | max-0-0-1 | 2023-12-28T20:07:16Z | 6 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] | reinforcement-learning | 2023-12-28T20:07:11Z | ---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: max-0-0-1/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
LoneStriker/notux-8x7b-v1-3.0bpw-h6-exl2 | LoneStriker | 2023-12-28T20:05:34Z | 7 | 0 | transformers | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"dpo",
"rlaif",
"preference",
"ultrafeedback",
"conversational",
"en",
"de",
"es",
"fr",
"it",
"dataset:argilla/ultrafeedback-binarized-preferences-cleaned",
"base_model:mistralai/Mixtral-8x7B-Instruct-v0.1",
"base_model:finetune:mistralai/Mixtral-8x7B-Instruct-v0.1",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2023-12-28T19:58:15Z | ---
datasets:
- argilla/ultrafeedback-binarized-preferences-cleaned
language:
- en
- de
- es
- fr
- it
base_model: mistralai/Mixtral-8x7B-Instruct-v0.1
library_name: transformers
pipeline_tag: text-generation
tags:
- dpo
- rlaif
- preference
- ultrafeedback
license: apache-2.0
model-index:
- name: notux-8x7b-v1
results: []
---
<div align="center">
<img src="https://cdn-uploads.huggingface.co/production/uploads/60f0608166e5701b80ed3f02/dj-spsk9eXMMXVGxK6jRz.png" alt="A banner representing Notus, the wind god of the south, in a mythical and artistic style. The banner features a strong, swirling breeze, embodying the warm, wet character of the southern wind. Gracefully flowing across the scene are several paper planes, caught in the gentle yet powerful gusts of Notus. The background is a blend of warm colors, symbolizing the heat of the south, with hints of blue and green to represent the moisture carried by this wind. The overall atmosphere is one of dynamic movement and warmth."/>
</div>
# Model Card for Notux 8x7B-v1
This model is a preference-tuned version of [mistralai/Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) on the [argilla/ultrafeedback-binarized-preferences-cleaned](https://huggingface.co/datasets/argilla/ultrafeedback-binarized-preferences-cleaned) dataset using DPO (Direct Preference Optimization).
As of Dec 26th 2023, it outperforms `Mixtral-8x7B-Instruct-v0.1` and is the top ranked MoE (Mixture of Experts) model on the [Hugging Face Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
This is part of the Notus family of models and experiments, where the Argilla team investigates data-first and preference tuning methods like dDPO (distilled DPO). This model is the result of our first experiment at tuning a MoE model that has already been fine-tuned with DPO (i.e., Mixtral-8x7B-Instruct-v0.1).
## Model Details
### Model Description
- **Developed by:** Argilla (based on MistralAI previous efforts)
- **Shared by:** Argilla
- **Model type:** Pretrained generative Sparse Mixture of Experts
- **Language(s) (NLP):** English, Spanish, Italian, German, and French
- **License:** MIT
- **Finetuned from model:** [mistralai/Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1)
### Model Sources
- **Repository:** https://github.com/argilla-io/notus
- **Paper:** N/A
## Training Details
### Training Hardware
We used a VM with 8 x H100 80GB hosted in runpod.io for 1 epoch (~10hr).
### Training Data
We used a new iteration of the Argilla UltraFeedback preferences dataset named [argilla/ultrafeedback-binarized-preferences-cleaned](https://huggingface.co/datasets/argilla/ultrafeedback-binarized-preferences-cleaned).
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-07
- train_batch_size: 8
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 64
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.4384 | 0.22 | 200 | 0.4556 | -0.3275 | -1.9448 | 0.7937 | 1.6174 | -405.7994 | -397.8617 | -1.3157 | -1.4511 |
| 0.4064 | 0.43 | 400 | 0.4286 | -0.2163 | -2.2090 | 0.8254 | 1.9927 | -408.4409 | -396.7496 | -0.7660 | -0.6539 |
| 0.3952 | 0.65 | 600 | 0.4275 | -0.1311 | -2.1603 | 0.8016 | 2.0291 | -407.9537 | -395.8982 | -0.6783 | -0.7206 |
| 0.3909 | 0.87 | 800 | 0.4167 | -0.2273 | -2.3146 | 0.8135 | 2.0872 | -409.4968 | -396.8602 | -0.8458 | -0.7738 |
### Framework versions
- Transformers 4.36.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.15.0 |
DanielClough/Candle_SOLAR-10.7B-Instruct-v1.0 | DanielClough | 2023-12-28T19:59:12Z | 11 | 0 | transformers | [
"transformers",
"gguf",
"llama",
"text-generation",
"en",
"dataset:upstage/SOLAR-10.7B-Instruct-v1.0",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2023-12-20T04:52:41Z | ---
datasets:
- upstage/SOLAR-10.7B-Instruct-v1.0
language:
- en
pipeline_tag: text-generation
license: apache-2.0
---
This repo includes `.gguf` built for HuggingFace/Candle.
Refer to the [original repo](https://huggingface.co/upstage/SOLAR-10.7B-Instruct-v1.0) for more details.
|
Crystalcareai/ZephyrWA2 | Crystalcareai | 2023-12-28T19:52:47Z | 2 | 0 | peft | [
"peft",
"pytorch",
"safetensors",
"mistral",
"region:us"
] | null | 2023-12-28T18:51:53Z | ---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0
|
igorshmel/arisha_water_draw | igorshmel | 2023-12-28T19:32:45Z | 0 | 0 | diffusers | [
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2023-12-27T14:20:07Z | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
Promt: ahsiraym, sensual girl, wsctch, Jerzy Duda-Gracz
Promt: ahsiraym, wsctch, sensual woman
|
andreatorch/q-FrozenLake-v1-4x4-noSlippery | andreatorch | 2023-12-28T19:28:17Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2023-12-28T19:28:15Z | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
model = load_from_hub(repo_id="andreatorch/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
|
afrideva/gpt2-small-danish-GGUF | afrideva | 2023-12-28T19:27:13Z | 40 | 0 | null | [
"gguf",
"ggml",
"quantized",
"q2_k",
"q3_k_m",
"q4_k_m",
"q5_k_m",
"q6_k",
"q8_0",
"text-generation",
"da",
"dataset:oscar",
"base_model:KennethTM/gpt2-small-danish",
"base_model:quantized:KennethTM/gpt2-small-danish",
"region:us"
] | text-generation | 2023-12-28T19:26:35Z | ---
base_model: KennethTM/gpt2-small-danish
datasets:
- oscar
inference: false
language:
- da
model_creator: KennethTM
model_name: gpt2-small-danish
pipeline_tag: text-generation
quantized_by: afrideva
tags:
- gguf
- ggml
- quantized
- q2_k
- q3_k_m
- q4_k_m
- q5_k_m
- q6_k
- q8_0
widget:
- text: Der var engang
---
# KennethTM/gpt2-small-danish-GGUF
Quantized GGUF model files for [gpt2-small-danish](https://huggingface.co/KennethTM/gpt2-small-danish) from [KennethTM](https://huggingface.co/KennethTM)
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [gpt2-small-danish.fp16.gguf](https://huggingface.co/afrideva/gpt2-small-danish-GGUF/resolve/main/gpt2-small-danish.fp16.gguf) | fp16 | 328.21 MB |
| [gpt2-small-danish.q2_k.gguf](https://huggingface.co/afrideva/gpt2-small-danish-GGUF/resolve/main/gpt2-small-danish.q2_k.gguf) | q2_k | 81.30 MB |
| [gpt2-small-danish.q3_k_m.gguf](https://huggingface.co/afrideva/gpt2-small-danish-GGUF/resolve/main/gpt2-small-danish.q3_k_m.gguf) | q3_k_m | 95.56 MB |
| [gpt2-small-danish.q4_k_m.gguf](https://huggingface.co/afrideva/gpt2-small-danish-GGUF/resolve/main/gpt2-small-danish.q4_k_m.gguf) | q4_k_m | 110.27 MB |
| [gpt2-small-danish.q5_k_m.gguf](https://huggingface.co/afrideva/gpt2-small-danish-GGUF/resolve/main/gpt2-small-danish.q5_k_m.gguf) | q5_k_m | 124.20 MB |
| [gpt2-small-danish.q6_k.gguf](https://huggingface.co/afrideva/gpt2-small-danish-GGUF/resolve/main/gpt2-small-danish.q6_k.gguf) | q6_k | 136.02 MB |
| [gpt2-small-danish.q8_0.gguf](https://huggingface.co/afrideva/gpt2-small-danish-GGUF/resolve/main/gpt2-small-danish.q8_0.gguf) | q8_0 | 175.47 MB |
## Original Model Card:
# What is this?
A GPT-2 model (small version, 124 M parameters) for Danish text generation. The model was not pre-trained from scratch but adapted from the English version.
# How to use
Test the model using the pipeline from the [🤗 Transformers](https://github.com/huggingface/transformers) library:
```python
from transformers import pipeline
generator = pipeline("text-generation", model = "KennethTM/gpt2-small-danish")
text = generator("Manden arbejdede som")
print(text[0]["generated_text"])
```
Or load it using the Auto* classes:
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("KennethTM/gpt2-small-danish")
model = AutoModelForCausalLM.from_pretrained("KennethTM/gpt2-small-danish")
```
# Model training
The model is trained using the Danish part of the [oscar dataset](https://huggingface.co/datasets/oscar) ('unshuffled_deduplicated_da') and a context length of 1024 tokens.
The model weights are initialized from the English [GPT-2 small model](https://huggingface.co/gpt2) with new word token embeddings created for Danish using [WECHSEL](https://github.com/CPJKU/wechsel).
Initially, only the word token embeddings are trained using 50.000 samples. Finally, the whole model is trained using 1.000.000 samples.
For reference, the model achieves a perplexity of 33.5 on 5.000 random validation samples.
Model training is carried out on an 8 GB GPU.
# Notes
This is a pre-trained model, for optimal performance it should be finetuned for new tasks. |
volkerpetersen/test | volkerpetersen | 2023-12-28T19:24:25Z | 0 | 0 | null | [
"model-index",
"region:us"
] | null | 2023-12-28T19:20:52Z | ---
model-index:
- name: Yi-34B
results:
- task:
type: text-generation
dataset:
name: ai2_arc
type: ai2_arc
metrics:
- name: AI2 Reasoning Challenge (25-Shot)
type: AI2 Reasoning Challenge (25-Shot)
value: 64.59
source:
name: Open LLM Leaderboard
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
--- |
afrideva/gpt2-turkish-128-token-GGUF | afrideva | 2023-12-28T19:21:14Z | 23 | 0 | null | [
"gguf",
"ggml",
"quantized",
"q2_k",
"q3_k_m",
"q4_k_m",
"q5_k_m",
"q6_k",
"q8_0",
"text-generation",
"tr",
"base_model:cenkersisman/gpt2-turkish-128-token",
"base_model:quantized:cenkersisman/gpt2-turkish-128-token",
"region:us"
] | text-generation | 2023-12-28T19:20:32Z | ---
base_model: cenkersisman/gpt2-turkish-128-token
inference: false
language:
- tr
model_creator: cenkersisman
model_name: gpt2-turkish-128-token
pipeline_tag: text-generation
quantized_by: afrideva
tags:
- gguf
- ggml
- quantized
- q2_k
- q3_k_m
- q4_k_m
- q5_k_m
- q6_k
- q8_0
widget:
- example_title: fransa'nın başkenti
text: fransa'nın başkenti
- example_title: ingiltere'nin başkenti
text: ingiltere'nın başkenti
- example_title: italya'nın başkenti
text: italya'nın başkenti
- example_title: moğolistan'ın başkenti
text: moğolistan'ın başkenti
- example_title: amazon ormanlarının bulunduğu ülke olan
text: amazon ormanlarının bulunduğu ülke olan
- example_title: avrupa'yı asya'ya bağlayan şehir
text: avrupa'yı asya'ya bağlayan şehir
- example_title: zebraların yaşadığı kıta olan
text: zebraların yaşadığı kıta olan
- example_title: fenerbahçe'nin ezeli rakibi olan
text: fenerbahçe'nin ezeli rakibi olan
- example_title: tek bacaklı kurbağa
text: tek bacaklı kurbağa
- example_title: rize'de yağmur
text: rize'de yağmur
- example_title: hayatın anlamı
text: hayatın anlamı
- example_title: saint-joseph
text: saint-joseph
- example_title: renk isimleri şunlardır
text: renk isimleri şunlardır
- example_title: iklim değişikliği
text: iklim değişikliği
- example_title: tuzlu yiyecekler arasında
text: tuzlu yiyecekler arasında
---
# cenkersisman/gpt2-turkish-128-token-GGUF
Quantized GGUF model files for [gpt2-turkish-128-token](https://huggingface.co/cenkersisman/gpt2-turkish-128-token) from [cenkersisman](https://huggingface.co/cenkersisman)
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [gpt2-turkish-128-token.fp16.gguf](https://huggingface.co/afrideva/gpt2-turkish-128-token-GGUF/resolve/main/gpt2-turkish-128-token.fp16.gguf) | fp16 | 328.01 MB |
| [gpt2-turkish-128-token.q2_k.gguf](https://huggingface.co/afrideva/gpt2-turkish-128-token-GGUF/resolve/main/gpt2-turkish-128-token.q2_k.gguf) | q2_k | 81.10 MB |
| [gpt2-turkish-128-token.q3_k_m.gguf](https://huggingface.co/afrideva/gpt2-turkish-128-token-GGUF/resolve/main/gpt2-turkish-128-token.q3_k_m.gguf) | q3_k_m | 95.37 MB |
| [gpt2-turkish-128-token.q4_k_m.gguf](https://huggingface.co/afrideva/gpt2-turkish-128-token-GGUF/resolve/main/gpt2-turkish-128-token.q4_k_m.gguf) | q4_k_m | 110.07 MB |
| [gpt2-turkish-128-token.q5_k_m.gguf](https://huggingface.co/afrideva/gpt2-turkish-128-token-GGUF/resolve/main/gpt2-turkish-128-token.q5_k_m.gguf) | q5_k_m | 124.01 MB |
| [gpt2-turkish-128-token.q6_k.gguf](https://huggingface.co/afrideva/gpt2-turkish-128-token-GGUF/resolve/main/gpt2-turkish-128-token.q6_k.gguf) | q6_k | 135.82 MB |
| [gpt2-turkish-128-token.q8_0.gguf](https://huggingface.co/afrideva/gpt2-turkish-128-token-GGUF/resolve/main/gpt2-turkish-128-token.q8_0.gguf) | q8_0 | 175.27 MB |
## Original Model Card:
# Model
GPT-2 Türkçe Modeli
### Model Açıklaması
GPT-2 Türkçe Modeli, Türkçe diline özelleştirilmiş olan GPT-2 mimarisi temel alınarak oluşturulmuş bir dil modelidir. Belirli bir başlangıç metni temel alarak insana benzer metinler üretme yeteneğine sahiptir ve geniş bir Türkçe metin veri kümesi üzerinde eğitilmiştir.
Modelin eğitimi için 900 milyon karakterli Vikipedi seti kullanılmıştır. Eğitim setindeki cümleler maksimum 128 tokendan (token = kelime kökü ve ekleri) oluşmuştur bu yüzden oluşturacağı cümlelerin boyu sınırlıdır..
Türkçe heceleme yapısına uygun tokenizer kullanılmış ve model 7.5 milyon adımda yaklaşık 154 epoch eğitilmiştir.
Eğitim için 4GB hafızası olan Nvidia Geforce RTX 3050 GPU kullanılmaktadır. 16GB Paylaşılan GPU'dan da yararlanılmakta ve eğitimin devamında toplamda 20GB hafıza kullanılmaktadır.
## Model Nasıl Kullanılabilir
ÖNEMLİ: model harf büyüklüğüne duyarlı olduğu için, prompt tamamen küçük harflerle yazılmalıdır.
```python
# Model ile çıkarım yapmak için örnek kod
from transformers import GPT2Tokenizer, GPT2LMHeadModel
model_name = "cenkersisman/gpt2-turkish-128-token"
tokenizer = GPT2Tokenizer.from_pretrained(model_name)
model = GPT2LMHeadModel.from_pretrained(model_name)
prompt = "okyanusun derinliklerinde bulunan"
input_ids = tokenizer.encode(prompt, return_tensors="pt")
output = model.generate(input_ids, max_length=100, pad_token_id=tokenizer.eos_token_id)
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)
```
## Eğitim Süreci Eğrisi
## Sınırlamalar ve Önyargılar
Bu model, bir özyineli dil modeli olarak eğitildi. Bu, temel işlevinin bir metin dizisi alıp bir sonraki belirteci tahmin etmek olduğu anlamına gelir. Dil modelleri bunun dışında birçok görev için yaygın olarak kullanılsa da, bu çalışmayla ilgili birçok bilinmeyen bulunmaktadır.
Model, küfür, açık saçıklık ve aksi davranışlara yol açan metinleri içerdiği bilinen bir veri kümesi üzerinde eğitildi. Kullanım durumunuza bağlı olarak, bu model toplumsal olarak kabul edilemez metinler üretebilir.
Tüm dil modellerinde olduğu gibi, bu modelin belirli bir girişe nasıl yanıt vereceğini önceden tahmin etmek zordur ve uyarı olmaksızın saldırgan içerik ortaya çıkabilir. Sonuçları yayınlamadan önce hem istenmeyen içeriği sansürlemek hem de sonuçların kalitesini iyileştirmek için insanların çıktıları denetlemesini veya filtrelemesi önerilir. |
Bhavishyasingla1/Ai-Posters | Bhavishyasingla1 | 2023-12-28T19:15:11Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-12-28T19:15:11Z | ---
license: creativeml-openrail-m
---
|
andreatorch/Reinforce-Unit1-ppo-LunarLander-v2 | andreatorch | 2023-12-28T19:06:20Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2023-12-28T18:39:42Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 263.02 +/- 19.93
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
moock/swinv2-tiny-patch4-window8-256-finetuned-gardner-te-max | moock | 2023-12-28T18:59:51Z | 10 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"swinv2",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/swinv2-tiny-patch4-window8-256",
"base_model:finetune:microsoft/swinv2-tiny-patch4-window8-256",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2023-12-28T18:57:43Z | ---
license: apache-2.0
base_model: microsoft/swinv2-tiny-patch4-window8-256
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swinv2-tiny-patch4-window8-256-finetuned-gardner-te-max
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.594017094017094
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swinv2-tiny-patch4-window8-256-finetuned-gardner-te-max
This model is a fine-tuned version of [microsoft/swinv2-tiny-patch4-window8-256](https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8795
- Accuracy: 0.5940
## Model description
Predict Trophectoderm Grade - Gardner Score from an embryo image
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0943 | 0.94 | 11 | 1.0750 | 0.6325 |
| 0.9996 | 1.96 | 23 | 0.8011 | 0.6325 |
| 0.7731 | 2.98 | 35 | 0.7182 | 0.6325 |
| 0.7564 | 4.0 | 47 | 0.7109 | 0.6325 |
| 0.7331 | 4.94 | 58 | 0.7026 | 0.6325 |
| 0.7336 | 5.96 | 70 | 0.6848 | 0.6325 |
| 0.7305 | 6.98 | 82 | 0.6938 | 0.6325 |
| 0.7314 | 8.0 | 94 | 0.6549 | 0.6325 |
| 0.6905 | 8.94 | 105 | 0.6364 | 0.6867 |
| 0.7315 | 9.96 | 117 | 0.6223 | 0.6687 |
| 0.6839 | 10.98 | 129 | 0.6528 | 0.7530 |
| 0.6931 | 12.0 | 141 | 0.6209 | 0.7410 |
| 0.6705 | 12.94 | 152 | 0.6296 | 0.7169 |
| 0.7227 | 13.96 | 164 | 0.6039 | 0.7108 |
| 0.6695 | 14.98 | 176 | 0.6049 | 0.7530 |
| 0.6981 | 16.0 | 188 | 0.5965 | 0.7048 |
| 0.6566 | 16.94 | 199 | 0.6111 | 0.7410 |
| 0.6828 | 17.96 | 211 | 0.5969 | 0.7530 |
| 0.6632 | 18.72 | 220 | 0.5947 | 0.7530 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2
- Datasets 2.16.0
- Tokenizers 0.15.0
|
jmpion/ppo-LunarLander-v2 | jmpion | 2023-12-28T18:54:42Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2023-12-28T18:54:21Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 269.54 +/- 19.02
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
socks22/ppo-lunarlander | socks22 | 2023-12-28T18:50:40Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2023-12-28T18:50:17Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 252.33 +/- 23.78
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
LoneStriker/Aurora-Nights-70B-v1.0-2.4bpw-h6-exl2 | LoneStriker | 2023-12-28T18:41:39Z | 8 | 3 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"en",
"arxiv:2307.11760",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2023-12-28T11:36:54Z | ---
license: llama2
language:
- en
---
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/aGUU0O9.png" alt="AuroraNights" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
### Overview
This model is a blend of [allenai/tulu-2-dpo-70b](https://huggingface.co/allenai/tulu-2-dpo-70b), [Xwin-LM/Xwin-LM-70B-V0.1](https://huggingface.co/Xwin-LM/Xwin-LM-70B-V0.1), and [dreamgen/opus-v0.5-70b](https://huggingface.co/dreamgen/opus-v0.5-70b). I then merged [nRuaif/fiction.live-Kimiko-V2-70B](https://huggingface.co/nRuaif/fiction.live-Kimiko-V2-70B) into the resultant blend. See the bottom of this card for the exact settings used.
This model is good at both following instructions and producing creative, uncensored storytelling and roleplaying content.
This model turned out quite uncensored. *You are responsible for whatever you do with it.*
This model was designed for roleplaying and storytelling and I think it does well at both. It *should* perform well at other tasks, but I haven't tested its capabilities in other areas.
### Sampler Tips
I recommend using the new Min-P sampler method with this model. The creator has a great [guide to it on Reddit](https://www.reddit.com/r/LocalLLaMA/comments/17vonjo/your_settings_are_probably_hurting_your_model_why/).
I find this model performs surprisingly well at 8192 context but you will probably get better results at 4096 context.
Experiment with any and all of the settings below, but trust me on a few points:
* This model works well with Min-P.
* Frequency Penalty set to 0.01 is like adding a dash of salt to the dish. Go higher at your own peril. 0 is fine too, but gosh I like 0.01.
If you save the below settings as a .json file, you can import them directly into Silly Tavern.
```
{
"temp": 1.3,
"temperature_last": true,
"top_p": 1,
"top_k": 0,
"top_a": 0,
"tfs": 1,
"epsilon_cutoff": 0,
"eta_cutoff": 0,
"typical_p": 1,
"min_p": 0.2,
"rep_pen": 1,
"rep_pen_range": 0,
"no_repeat_ngram_size": 0,
"penalty_alpha": 0,
"num_beams": 1,
"length_penalty": 1,
"min_length": 0,
"encoder_rep_pen": 1,
"freq_pen": 0.01,
"presence_pen": 0,
"do_sample": true,
"early_stopping": false,
"add_bos_token": true,
"truncation_length": 2048,
"ban_eos_token": false,
"skip_special_tokens": true,
"streaming": true,
"mirostat_mode": 0,
"mirostat_tau": 5,
"mirostat_eta": 0.1,
"guidance_scale": 1,
"negative_prompt": "",
"grammar_string": "",
"banned_tokens": "",
"ignore_eos_token_aphrodite": false,
"spaces_between_special_tokens_aphrodite": true,
"type": "ooba",
"legacy_api": false,
"sampler_order": [
6,
0,
1,
3,
4,
2,
5
],
"n": 1,
"rep_pen_size": 0,
"genamt": 550,
"max_length": 4096
}
```
### Prompt Tips
Try the following prompt template as a starting point for your own customizations. 'This is very important to my career' is from [this paper](https://arxiv.org/abs/2307.11760).
I recommend the Tulu instruction format.
*This model responds to prompting!* I highly recommend playing with your system prompt to see how it reacts.
If you save this as a .json file, you can import it directly into Silly Tavern.
```
{
"wrap": true,
"names": false,
"system_prompt": "You are an award-winning fiction author engaging in an uncensored literary roleplaying experience between a human (me) and a creative, obedient, and helpful AI assistant (you). It is VITAL that you follow all these instructions because this roleplay is very important to my career and I'll be fired from my job if it isn't good.\nROLEPLAY RULES:\n> It is vital that ONLY the human provides responses for {{user}}.\n> Reply as {{char}} using authentic, vivid, varied, explicit, accurate, creative, fresh, and descriptive responses that follow ALL provided narrative instructions. Stay in character as {{char}} and only write text for {{char}}.\n> Describe the scene and {{char}}'s sensory perceptions in vivid detail to immerse the reader in the story.\n> Keep your responses scoped to the current story beat and current scene.\n> Consider all available contextual information when narrating so that all the story details remain consistent between scenes.\n> Demonstrate {{char}}'s goals and motivations, and use subtle cues to hint at {{char}}'s mental state unless delving into {{char}}'s thoughts satisfies an explicit instruction or enhances the vividness of the scene.\n> When quoting {{char}}'s internal first-person thoughts (aka internal monologue, delivered in {{char}}'s own voice), *enclose the thoughts in asterisks like this*. Only use asterisks for thoughts.\n> Use strong action verbs and varied descriptions to produce dynamic, high-quality prose.",
"system_sequence": "",
"stop_sequence": "",
"input_sequence": "<|user|>\n",
"output_sequence": "<|assistant|>\n",
"separator_sequence": "",
"macro": true,
"names_force_groups": true,
"system_sequence_prefix": "",
"system_sequence_suffix": "",
"first_output_sequence": "",
"last_output_sequence": "<|assistant (provide varied, creative, and vivid narration; follow all narrative instructions; include all necessary possessive pronouns; maintain consistent story details; only roleplay as {{char}})|>\n",
"activation_regex": "",
"name": "Aurora-Nights"
}
```
### Licence and usage restrictions
Llama2 license inherited from base models, plus restrictions applicable to [Dreamgen/Opus](https://huggingface.co/dreamgen/opus-v0.5-70b).
### Tools Used
* [mergekit](https://github.com/cg123/mergekit)
```
models:
- model: NousResearch_Llama-2-70b-hf
# no parameters necessary for base model
- model: allenai_tulu-2-dpo-70b # primary
parameters:
density: 1.0
weight: 0.4
- model: Xwin-LM_Xwin-LM-70B-V0.1 # secondary
parameters:
density: 0.7
weight: 0.3
- model: dreamgen_opus-v0.5-70b # supporting, good at storytelling and roleplay
parameters:
density: 0.2
weight: 0.6
merge_method: dare_ties
base_model: NousResearch_Llama-2-70b-hf
parameters:
normalize: true
int8_mask: true
dtype: float32
``` |
krunchykat/bge-base-en-v1.5-ft-quora | krunchykat | 2023-12-28T18:38:30Z | 3 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | sentence-similarity | 2023-12-28T18:36:38Z | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 11371 with parameters:
```
{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.OnlineContrastiveLoss.OnlineContrastiveLoss`
Parameters of the fit()-Method:
```
{
"epochs": 8,
"evaluation_steps": 0,
"evaluator": "sentence_transformers.evaluation.BinaryClassificationEvaluator.BinaryClassificationEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "warmuplinear",
"steps_per_epoch": null,
"warmup_steps": 10000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
toddwilson147/ppo-SnowballTarget | toddwilson147 | 2023-12-28T18:36:42Z | 3 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] | reinforcement-learning | 2023-12-28T18:36:37Z | ---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: toddwilson147/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
CyberHarem/riko_saikawa_kobayashisanchinomaidragon | CyberHarem | 2023-12-28T18:29:03Z | 0 | 0 | null | [
"art",
"text-to-image",
"dataset:CyberHarem/riko_saikawa_kobayashisanchinomaidragon",
"license:mit",
"region:us"
] | text-to-image | 2023-12-28T18:19:01Z | ---
license: mit
datasets:
- CyberHarem/riko_saikawa_kobayashisanchinomaidragon
pipeline_tag: text-to-image
tags:
- art
---
# Lora of riko_saikawa_kobayashisanchinomaidragon
This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11).
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 4420, you need to download `4420/riko_saikawa_kobayashisanchinomaidragon.pt` as the embedding and `4420/riko_saikawa_kobayashisanchinomaidragon.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The best step we recommend is 4420**, with the score of 0.956. The trigger words are:
1. `riko_saikawa_kobayashisanchinomaidragon`
2. `brown_hair, blush, long_hair, green_eyes, hair_ornament, flower, hair_flower`
For the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
These are available steps:
| Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | pattern_5 | pattern_6 | pattern_7 | pattern_8 | pattern_9 | pattern_10 | pattern_11 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata |
|:---------|:----------|:-----------------------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-----------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------|
| 5100 | 0.934 | [Download](5100/riko_saikawa_kobayashisanchinomaidragon.zip) |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](5100/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5100/previews/nude.png) | [<NSFW, click to see>](5100/previews/nude2.png) |  |  |
| 4760 | 0.931 | [Download](4760/riko_saikawa_kobayashisanchinomaidragon.zip) |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](4760/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4760/previews/nude.png) | [<NSFW, click to see>](4760/previews/nude2.png) |  |  |
| **4420** | **0.956** | [**Download**](4420/riko_saikawa_kobayashisanchinomaidragon.zip) |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](4420/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4420/previews/nude.png) | [<NSFW, click to see>](4420/previews/nude2.png) |  |  |
| 4080 | 0.951 | [Download](4080/riko_saikawa_kobayashisanchinomaidragon.zip) |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](4080/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4080/previews/nude.png) | [<NSFW, click to see>](4080/previews/nude2.png) |  |  |
| 3740 | 0.951 | [Download](3740/riko_saikawa_kobayashisanchinomaidragon.zip) |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](3740/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3740/previews/nude.png) | [<NSFW, click to see>](3740/previews/nude2.png) |  |  |
| 3400 | 0.942 | [Download](3400/riko_saikawa_kobayashisanchinomaidragon.zip) |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](3400/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3400/previews/nude.png) | [<NSFW, click to see>](3400/previews/nude2.png) |  |  |
| 3060 | 0.942 | [Download](3060/riko_saikawa_kobayashisanchinomaidragon.zip) |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](3060/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3060/previews/nude.png) | [<NSFW, click to see>](3060/previews/nude2.png) |  |  |
| 2720 | 0.936 | [Download](2720/riko_saikawa_kobayashisanchinomaidragon.zip) |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](2720/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2720/previews/nude.png) | [<NSFW, click to see>](2720/previews/nude2.png) |  |  |
| 2380 | 0.934 | [Download](2380/riko_saikawa_kobayashisanchinomaidragon.zip) |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](2380/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2380/previews/nude.png) | [<NSFW, click to see>](2380/previews/nude2.png) |  |  |
| 2040 | 0.932 | [Download](2040/riko_saikawa_kobayashisanchinomaidragon.zip) |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](2040/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2040/previews/nude.png) | [<NSFW, click to see>](2040/previews/nude2.png) |  |  |
| 1700 | 0.906 | [Download](1700/riko_saikawa_kobayashisanchinomaidragon.zip) |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](1700/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1700/previews/nude.png) | [<NSFW, click to see>](1700/previews/nude2.png) |  |  |
| 1360 | 0.838 | [Download](1360/riko_saikawa_kobayashisanchinomaidragon.zip) |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](1360/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1360/previews/nude.png) | [<NSFW, click to see>](1360/previews/nude2.png) |  |  |
| 1020 | 0.730 | [Download](1020/riko_saikawa_kobayashisanchinomaidragon.zip) |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](1020/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1020/previews/nude.png) | [<NSFW, click to see>](1020/previews/nude2.png) |  |  |
| 680 | 0.695 | [Download](680/riko_saikawa_kobayashisanchinomaidragon.zip) |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](680/previews/bondage.png) |  |  |  | [<NSFW, click to see>](680/previews/nude.png) | [<NSFW, click to see>](680/previews/nude2.png) |  |  |
| 340 | 0.320 | [Download](340/riko_saikawa_kobayashisanchinomaidragon.zip) |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](340/previews/bondage.png) |  |  |  | [<NSFW, click to see>](340/previews/nude.png) | [<NSFW, click to see>](340/previews/nude2.png) |  |  |
|
nedahp/distilbert-base-uncased-finetuned-emotion | nedahp | 2023-12-28T18:19:47Z | 4 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2023-12-28T17:13:07Z | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.924
- name: F1
type: f1
value: 0.9241355956031972
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2267
- Accuracy: 0.924
- F1: 0.9241
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8607 | 1.0 | 250 | 0.3310 | 0.9055 | 0.9046 |
| 0.2604 | 2.0 | 500 | 0.2267 | 0.924 | 0.9241 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.0
- Datasets 2.15.0
- Tokenizers 0.15.0
|
TheBloke/Aurora-Nights-103B-v1.0-AWQ | TheBloke | 2023-12-28T18:13:21Z | 9 | 1 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"en",
"arxiv:2307.11760",
"base_model:sophosympatheia/Aurora-Nights-103B-v1.0",
"base_model:quantized:sophosympatheia/Aurora-Nights-103B-v1.0",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"awq",
"region:us"
] | text-generation | 2023-12-28T10:21:29Z | ---
base_model: sophosympatheia/Aurora-Nights-103B-v1.0
inference: false
language:
- en
license: llama2
model_creator: Sophosympatheia
model_name: Aurora Nights 103B v1.0
model_type: llama
prompt_template: '{system_message}
<|user|>
{prompt}
<|assistant|>
'
quantized_by: TheBloke
---
<!-- markdownlint-disable MD041 -->
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Aurora Nights 103B v1.0 - AWQ
- Model creator: [Sophosympatheia](https://huggingface.co/sophosympatheia)
- Original model: [Aurora Nights 103B v1.0](https://huggingface.co/sophosympatheia/Aurora-Nights-103B-v1.0)
<!-- description start -->
## Description
This repo contains AWQ model files for [Sophosympatheia's Aurora Nights 103B v1.0](https://huggingface.co/sophosympatheia/Aurora-Nights-103B-v1.0).
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
### About AWQ
AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.
AWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead.
It is supported by:
- [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ
- [vLLM](https://github.com/vllm-project/vllm) - version 0.2.2 or later for support for all model types.
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code
<!-- description end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Aurora-Nights-103B-v1.0-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Aurora-Nights-103B-v1.0-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Aurora-Nights-103B-v1.0-GGUF)
* [Sophosympatheia's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/sophosympatheia/Aurora-Nights-103B-v1.0)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: ToRA-System
```
{system_message}
<|user|>
{prompt}
<|assistant|>
```
<!-- prompt-template end -->
<!-- README_AWQ.md-provided-files start -->
## Provided files, and AWQ parameters
I currently release 128g GEMM models only. The addition of group_size 32 models, and GEMV kernel models, is being actively considered.
Models are released as sharded safetensors files.
| Branch | Bits | GS | AWQ Dataset | Seq Len | Size |
| ------ | ---- | -- | ----------- | ------- | ---- |
| [main](https://huggingface.co/TheBloke/Aurora-Nights-103B-v1.0-AWQ/tree/main) | 4 | 128 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 54.40 GB
<!-- README_AWQ.md-provided-files end -->
<!-- README_AWQ.md-text-generation-webui start -->
## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.
1. Click the **Model tab**.
2. Under **Download custom model or LoRA**, enter `TheBloke/Aurora-Nights-103B-v1.0-AWQ`.
3. Click **Download**.
4. The model will start downloading. Once it's finished it will say "Done".
5. In the top left, click the refresh icon next to **Model**.
6. In the **Model** dropdown, choose the model you just downloaded: `Aurora-Nights-103B-v1.0-AWQ`
7. Select **Loader: AutoAWQ**.
8. Click Load, and the model will load and is now ready for use.
9. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right.
10. Once you're ready, click the **Text Generation** tab and enter a prompt to get started!
<!-- README_AWQ.md-text-generation-webui end -->
<!-- README_AWQ.md-use-from-vllm start -->
## Multi-user inference server: vLLM
Documentation on installing and using vLLM [can be found here](https://vllm.readthedocs.io/en/latest/).
- Please ensure you are using vLLM version 0.2 or later.
- When using vLLM as a server, pass the `--quantization awq` parameter.
For example:
```shell
python3 -m vllm.entrypoints.api_server --model TheBloke/Aurora-Nights-103B-v1.0-AWQ --quantization awq --dtype auto
```
- When using vLLM from Python code, again set `quantization=awq`.
For example:
```python
from vllm import LLM, SamplingParams
prompts = [
"Tell me about AI",
"Write a story about llamas",
"What is 291 - 150?",
"How much wood would a woodchuck chuck if a woodchuck could chuck wood?",
]
prompt_template=f'''{system_message}
<|user|>
{prompt}
<|assistant|>
'''
prompts = [prompt_template.format(prompt=prompt) for prompt in prompts]
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
llm = LLM(model="TheBloke/Aurora-Nights-103B-v1.0-AWQ", quantization="awq", dtype="auto")
outputs = llm.generate(prompts, sampling_params)
# Print the outputs.
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
```
<!-- README_AWQ.md-use-from-vllm start -->
<!-- README_AWQ.md-use-from-tgi start -->
## Multi-user inference server: Hugging Face Text Generation Inference (TGI)
Use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0`
Example Docker parameters:
```shell
--model-id TheBloke/Aurora-Nights-103B-v1.0-AWQ --port 3000 --quantize awq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096
```
Example Python code for interfacing with TGI (requires [huggingface-hub](https://github.com/huggingface/huggingface_hub) 0.17.0 or later):
```shell
pip3 install huggingface-hub
```
```python
from huggingface_hub import InferenceClient
endpoint_url = "https://your-endpoint-url-here"
prompt = "Tell me about AI"
prompt_template=f'''{system_message}
<|user|>
{prompt}
<|assistant|>
'''
client = InferenceClient(endpoint_url)
response = client.text_generation(prompt,
max_new_tokens=128,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1)
print(f"Model output: ", response)
```
<!-- README_AWQ.md-use-from-tgi end -->
<!-- README_AWQ.md-use-from-python start -->
## Inference from Python code using Transformers
### Install the necessary packages
- Requires: [Transformers](https://huggingface.co/docs/transformers) 4.35.0 or later.
- Requires: [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) 0.1.6 or later.
```shell
pip3 install --upgrade "autoawq>=0.1.6" "transformers>=4.35.0"
```
Note that if you are using PyTorch 2.0.1, the above AutoAWQ command will automatically upgrade you to PyTorch 2.1.0.
If you are using CUDA 11.8 and wish to continue using PyTorch 2.0.1, instead run this command:
```shell
pip3 install https://github.com/casper-hansen/AutoAWQ/releases/download/v0.1.6/autoawq-0.1.6+cu118-cp310-cp310-linux_x86_64.whl
```
If you have problems installing [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) using the pre-built wheels, install it from source instead:
```shell
pip3 uninstall -y autoawq
git clone https://github.com/casper-hansen/AutoAWQ
cd AutoAWQ
pip3 install .
```
### Transformers example code (requires Transformers 4.35.0 and later)
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
model_name_or_path = "TheBloke/Aurora-Nights-103B-v1.0-AWQ"
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
model = AutoModelForCausalLM.from_pretrained(
model_name_or_path,
low_cpu_mem_usage=True,
device_map="cuda:0"
)
# Using the text streamer to stream output one token at a time
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
prompt = "Tell me about AI"
prompt_template=f'''{system_message}
<|user|>
{prompt}
<|assistant|>
'''
# Convert prompt to tokens
tokens = tokenizer(
prompt_template,
return_tensors='pt'
).input_ids.cuda()
generation_params = {
"do_sample": True,
"temperature": 0.7,
"top_p": 0.95,
"top_k": 40,
"max_new_tokens": 512,
"repetition_penalty": 1.1
}
# Generate streamed output, visible one token at a time
generation_output = model.generate(
tokens,
streamer=streamer,
**generation_params
)
# Generation without a streamer, which will include the prompt in the output
generation_output = model.generate(
tokens,
**generation_params
)
# Get the tokens from the output, decode them, print them
token_output = generation_output[0]
text_output = tokenizer.decode(token_output)
print("model.generate output: ", text_output)
# Inference is also possible via Transformers' pipeline
from transformers import pipeline
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
**generation_params
)
pipe_output = pipe(prompt_template)[0]['generated_text']
print("pipeline output: ", pipe_output)
```
<!-- README_AWQ.md-use-from-python end -->
<!-- README_AWQ.md-compatibility start -->
## Compatibility
The files provided are tested to work with:
- [text-generation-webui](https://github.com/oobabooga/text-generation-webui) using `Loader: AutoAWQ`.
- [vLLM](https://github.com/vllm-project/vllm) version 0.2.0 and later.
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) version 1.1.0 and later.
- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later.
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) version 0.1.1 and later.
<!-- README_AWQ.md-compatibility end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
# Original model card: Sophosympatheia's Aurora Nights 103B v1.0
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/aGUU0O9.png" alt="AuroraNights" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
### Overview
This model is a frankenmerge of Aurora-Nights-70B-v1.0 with itself. (See that model card for details on what's in the blend.) It features 120 layers and should weigh in at 103b parameters.
This model is a successor to Rogue Rose and improves upon it. Aurora follows instructions better but retains excellent creative writing and ERP abilities.
This model turned out quite uncensored. *You are responsible for whatever you do with it.*
This model was designed for roleplaying and storytelling and I think it does well at both. It *should* perform well at other tasks, but I haven't tested its capabilities in other areas.
### Sampler Tips
I recommend using the new Min-P sampler method with this model. The creator has a great [guide to it on Reddit](https://www.reddit.com/r/LocalLLaMA/comments/17vonjo/your_settings_are_probably_hurting_your_model_why/).
I find this model performs reasonably well at 8192 context but you will likely get better results at 4096.
Experiment with any and all of the settings below, but trust me on a few points:
* This model loves high temperatures with Min-P.
* Frequency Penalty set to 0.01 is like adding a dash of salt to the dish. Go higher at your own peril. 0 is fine too, but gosh I like 0.01.
If you save the below settings as a .json file, you can import them directly into Silly Tavern.
```
{
"temp": 1.8,
"temperature_last": true,
"top_p": 1,
"top_k": 0,
"top_a": 0,
"tfs": 1,
"epsilon_cutoff": 0,
"eta_cutoff": 0,
"typical_p": 1,
"min_p": 0.5,
"rep_pen": 1,
"rep_pen_range": 0,
"no_repeat_ngram_size": 0,
"penalty_alpha": 0,
"num_beams": 1,
"length_penalty": 1,
"min_length": 0,
"encoder_rep_pen": 1,
"freq_pen": 0.01,
"presence_pen": 0,
"do_sample": true,
"early_stopping": false,
"add_bos_token": true,
"truncation_length": 2048,
"ban_eos_token": false,
"skip_special_tokens": true,
"streaming": true,
"mirostat_mode": 0,
"mirostat_tau": 5,
"mirostat_eta": 0.1,
"guidance_scale": 1,
"negative_prompt": "",
"grammar_string": "",
"banned_tokens": "",
"ignore_eos_token_aphrodite": false,
"spaces_between_special_tokens_aphrodite": true,
"type": "ooba",
"legacy_api": false,
"sampler_order": [
6,
0,
1,
3,
4,
2,
5
],
"n": 1,
"rep_pen_size": 0,
"genamt": 550,
"max_length": 4096
}
```
### Prompt Tips
Try the following prompt template as a starting point for your own customizations. 'This is very important to my career' is from [this paper](https://arxiv.org/abs/2307.11760).
I recommend the Tulu instruction format.
*This model responds to prompting!* I highly recommend playing with your system prompt to see how it reacts.
If you save this as a .json file, you can import it directly into Silly Tavern.
```
{
"wrap": true,
"names": false,
"system_prompt": "You are an award-winning fiction author engaging in an uncensored literary roleplaying experience between a human (me) and a creative, obedient, and helpful AI assistant (you). It is VITAL that you follow all these instructions because this roleplay is very important to my career and I'll be fired from my job if it isn't good.\nROLEPLAY RULES:\n> It is vital that ONLY the human provides responses for {{user}}.\n> Reply as {{char}} using authentic, vivid, varied, explicit, accurate, creative, fresh, and descriptive responses that follow ALL provided narrative instructions. Stay in character as {{char}} and only write text for {{char}}.\n> Describe the scene and {{char}}'s sensory perceptions in vivid detail to immerse the reader in the story.\n> Keep your responses scoped to the current story beat and current scene.\n> Consider all available contextual information when narrating so that all the story details remain consistent between scenes.\n> Demonstrate {{char}}'s goals and motivations, and use subtle cues to hint at {{char}}'s mental state unless delving into {{char}}'s thoughts satisfies an explicit instruction or enhances the vividness of the scene.\n> When quoting {{char}}'s internal first-person thoughts (aka internal monologue, delivered in {{char}}'s own voice), *enclose the thoughts in asterisks like this*. Only use asterisks for thoughts.\n> Use strong action verbs and varied descriptions to produce dynamic, high-quality prose.",
"system_sequence": "",
"stop_sequence": "",
"input_sequence": "<|user|>\n",
"output_sequence": "<|assistant|>\n",
"separator_sequence": "",
"macro": true,
"names_force_groups": true,
"system_sequence_prefix": "",
"system_sequence_suffix": "",
"first_output_sequence": "",
"last_output_sequence": "<|assistant (provide varied, creative, and vivid narration; follow all narrative instructions; include all necessary possessive pronouns; maintain consistent story details; only roleplay as {{char}})|>\n",
"activation_regex": "",
"name": "Aurora-Nights"
}
```
### Licence and usage restrictions
Llama2 license inherited from base models, plus restrictions applicable to [Dreamgen/Opus](https://huggingface.co/dreamgen/opus-v0.5-70b).
### Tools Used
* [mergekit](https://github.com/cg123/mergekit)
```
slices:
- sources:
- model: aurora-nights-70b-v1.0
layer_range: [0, 40] # 40
- sources:
- model: aurora-nights-70b-v1.0
layer_range: [20, 60] # 40
- sources:
- model: aurora-nights-70b-v1.0
layer_range: [40, 80] # 40
merge_method: passthrough
dtype: float16
```
|
Saugatkafley/opt-350m-sft | Saugatkafley | 2023-12-28T18:04:48Z | 31 | 0 | peft | [
"peft",
"tensorboard",
"safetensors",
"code",
"text-generation",
"en",
"dataset:HuggingFaceH4/CodeAlpaca_20K",
"arxiv:1910.09700",
"base_model:facebook/opt-350m",
"base_model:adapter:facebook/opt-350m",
"license:mit",
"region:us"
] | text-generation | 2023-12-28T17:34:49Z | ---
library_name: peft
base_model: facebook/opt-350m
license: mit
datasets:
- HuggingFaceH4/CodeAlpaca_20K
language:
- en
pipeline_tag: text-generation
tags:
- code
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.7.2.dev0 |
crispisu/a2c-PandaReachDense-v3 | crispisu | 2023-12-28T18:01:29Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2023-12-28T17:57:01Z | ---
library_name: stable-baselines3
tags:
- PandaReachDense-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v3
type: PandaReachDense-v3
metrics:
- type: mean_reward
value: -0.24 +/- 0.13
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
aymanD/dqn-SpaceInvadersNoFrameskip-v4 | aymanD | 2023-12-28T18:00:31Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2023-12-28T18:00:18Z | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 608.00 +/- 156.26
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga geekayman -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga geekayman -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga geekayman
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 10000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
actionpace/Sakura-SOLAR-Instruct | actionpace | 2023-12-28T17:57:45Z | 5 | 0 | null | [
"gguf",
"en",
"license:other",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2023-12-28T17:26:11Z | ---
license: other
language:
- en
---
**Some of my own quants:**
* Sakura-SOLAR-Instruct_Q5_K_M.gguf
* Sakura-SOLAR-Instruct_Q8_0.gguf
**Source:** [kyujinpy](https://huggingface.co/kyujinpy)
**Source Model:** [Sakura-SOLAR-Instruct](https://huggingface.co/kyujinpy/Sakura-SOLAR-Instruct)
**Source models for kyujinpy/Sakura-SOLAR-Instruct (Merge)**
- [upstage/SOLAR-10.7B-Instruct-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-Instruct-v1.0) ([Ref](https://huggingface.co/actionpace/SOLAR-10.7B-Instruct-v1.0))
- [VAGOsolutions/SauerkrautLM-SOLAR-Instruct](https://huggingface.co/VAGOsolutions/SauerkrautLM-SOLAR-Instruct) ([Ref](https://huggingface.co/actionpace/SauerkrautLM-SOLAR-Instruct))
|
Weni/WeniGPT-2.0.1-Zephyr-7B-bits_and_bytes-step-500-llm-base-1.0.1 | Weni | 2023-12-28T17:56:02Z | 0 | 0 | peft | [
"peft",
"safetensors",
"mistral",
"arxiv:1910.09700",
"base_model:HuggingFaceH4/zephyr-7b-beta",
"base_model:adapter:HuggingFaceH4/zephyr-7b-beta",
"region:us"
] | null | 2023-12-28T17:49:14Z | ---
library_name: peft
base_model: HuggingFaceH4/zephyr-7b-beta
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.7.1 |
stablediffusionapi/blueboys2d-v30 | stablediffusionapi | 2023-12-28T17:54:09Z | 0 | 1 | diffusers | [
"diffusers",
"modelslab.com",
"stable-diffusion-api",
"text-to-image",
"ultra-realistic",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2023-12-28T17:51:48Z | ---
license: creativeml-openrail-m
tags:
- modelslab.com
- stable-diffusion-api
- text-to-image
- ultra-realistic
pinned: true
---
# BlueBoys_2D v3.0 API Inference
## Get API Key
Get API key from [ModelsLab API](http://modelslab.com), No Payment needed.
Replace Key in below code, change **model_id** to "blueboys2d-v30"
Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://modelslab.com/docs)
Try model for free: [Generate Images](https://modelslab.com/models/blueboys2d-v30)
Model link: [View model](https://modelslab.com/models/blueboys2d-v30)
View all models: [View Models](https://modelslab.com/models)
import requests
import json
url = "https://modelslab.com/api/v6/images/text2img"
payload = json.dumps({
"key": "your_api_key",
"model_id": "blueboys2d-v30",
"prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K",
"negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime",
"width": "512",
"height": "512",
"samples": "1",
"num_inference_steps": "20",
"safety_checker": "no",
"enhance_prompt": "yes",
"seed": None,
"guidance_scale": 7.5,
"multi_lingual": "no",
"panorama": "no",
"self_attention": "no",
"upscale": "no",
"embeddings": "embeddings_model_id",
"lora": "lora_model_id",
"webhook": None,
"track_id": None
})
headers = {
'Content-Type': 'application/json'
}
response = requests.request("POST", url, headers=headers, data=payload)
print(response.text)
> Use this coupon code to get 25% off **DMGG0RBN** |
DanielClough/Candle_OrcaMini-3B | DanielClough | 2023-12-28T17:46:49Z | 11 | 0 | null | [
"gguf",
"text-generation",
"en",
"dataset:pankajmathur/orca_mini_3b",
"license:apache-2.0",
"region:us"
] | text-generation | 2023-12-21T20:39:42Z | ---
datasets:
- pankajmathur/orca_mini_3b
language:
- en
pipeline_tag: text-generation
license: apache-2.0
---
This repo includes `.gguf` built for HuggingFace/Candle.
They will not work with `llama.cpp`.
Refer to the [original repo](https://huggingface.co/pankajmathur/orca_mini_3b) for more details.
|
AhmedEladl/mistral_instruct_sinks | AhmedEladl | 2023-12-28T17:44:56Z | 2 | 0 | peft | [
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"dataset:generator",
"base_model:mistralai/Mistral-7B-Instruct-v0.1",
"base_model:adapter:mistralai/Mistral-7B-Instruct-v0.1",
"license:apache-2.0",
"region:us"
] | null | 2023-12-28T05:38:03Z | ---
license: apache-2.0
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
datasets:
- generator
base_model: mistralai/Mistral-7B-Instruct-v0.1
model-index:
- name: mistral_instruct_sinks
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mistral_instruct_sinks
This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) on the generator dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 6
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_steps: 0.03
- num_epochs: 5
### Training results
### Framework versions
- PEFT 0.7.1
- Transformers 4.37.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0 |
bartowski/dolphin-2.6-mistral-7b-exl2 | bartowski | 2023-12-28T17:36:27Z | 5 | 3 | null | [
"text-generation",
"en",
"dataset:ehartford/dolphin",
"dataset:jondurbin/airoboros-2.2.1",
"dataset:ehartford/dolphin-coder",
"dataset:teknium/openhermes",
"dataset:ise-uiuc/Magicoder-OSS-Instruct-75K",
"dataset:ise-uiuc/Magicoder-Evol-Instruct-110K",
"dataset:LDJnr/Capybara",
"license:apache-2.0",
"region:us"
] | text-generation | 2023-12-28T07:53:48Z | ---
datasets:
- ehartford/dolphin
- jondurbin/airoboros-2.2.1
- ehartford/dolphin-coder
- teknium/openhermes
- ise-uiuc/Magicoder-OSS-Instruct-75K
- ise-uiuc/Magicoder-Evol-Instruct-110K
- LDJnr/Capybara
language:
- en
license: apache-2.0
quantized_by: bartowski
pipeline_tag: text-generation
---
## Exllama v2 Quantizations of dolphin-2.6-mistral-7b
Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.11">turboderp's ExLlamaV2 v0.0.11</a> for quantization.
Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions.
Conversion was done using the default calibration dataset.
Default arguments used except when the bits per weight is above 6.0, at that point the lm_head layer is quantized at 8 bits per weight instead of the default 6.
Original model: https://huggingface.co/cognitivecomputations/dolphin-2.6-mistral-7b
<a href="https://huggingface.co/bartowski/dolphin-2.6-mistral-7b-exl2/tree/4_0">4.0 bits per weight</a>
<a href="https://huggingface.co/bartowski/dolphin-2.6-mistral-7b-exl2/tree/5_0">5.0 bits per weight</a>
<a href="https://huggingface.co/bartowski/dolphin-2.6-mistral-7b-exl2/tree/6_5">6.5 bits per weight</a>
<a href="https://huggingface.co/bartowski/dolphin-2.6-mistral-7b-exl2/tree/8_0">8.0 bits per weight</a>
## Download instructions
With git:
```shell
git clone --single-branch --branch 4_0 https://huggingface.co/bartowski/dolphin-2.6-mistral-7b-exl2
```
With huggingface hub (credit to TheBloke for instructions):
```shell
pip3 install huggingface-hub
```
To download the `main` (only useful if you only care about measurement.json) branch to a folder called `dolphin-2.6-mistral-7b-exl2`:
```shell
mkdir dolphin-2.6-mistral-7b-exl2
huggingface-cli download bartowski/dolphin-2.6-mistral-7b-exl2 --local-dir dolphin-2.6-mistral-7b-exl2 --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
```shell
mkdir dolphin-2.6-mistral-7b-exl2
huggingface-cli download bartowski/dolphin-2.6-mistral-7b-exl2 --revision 4_0 --local-dir dolphin-2.6-mistral-7b-exl2 --local-dir-use-symlinks False
```
|
Neu256/Prome-LLM-II | Neu256 | 2023-12-28T17:35:57Z | 0 | 0 | null | [
"pytorch",
"en",
"license:mit",
"region:us"
] | null | 2023-12-28T16:36:24Z | ---
license: mit
language:
- en
--- |
wenqiglantz/stt_en_conformer_ctc_small | wenqiglantz | 2023-12-28T17:30:01Z | 1 | 0 | nemo | [
"nemo",
"automatic-speech-recognition",
"speech",
"audio",
"CTC",
"Conformer",
"Transformer",
"NeMo",
"pytorch",
"en",
"dataset:librispeech_asr",
"dataset:mozilla-foundation/common_voice_7_0",
"dataset:vctk",
"dataset:fisher_corpus",
"dataset:Switchboard-1",
"dataset:WSJ-0",
"dataset:WSJ-1",
"dataset:National-Singapore-Corpus-Part-1",
"dataset:National-Singapore-Corpus-Part-6",
"dataset:voxpopuli",
"dataset:europarl",
"dataset:multilingual_librispeech",
"license:cc-by-4.0",
"model-index",
"region:us"
] | automatic-speech-recognition | 2023-12-28T17:04:36Z | ---
language:
- en
license: cc-by-4.0
library_name: nemo
tags:
- automatic-speech-recognition
- speech
- audio
- CTC
- Conformer
- Transformer
- NeMo
- pytorch
datasets:
- librispeech_asr
- mozilla-foundation/common_voice_7_0
- vctk
- fisher_corpus
- Switchboard-1
- WSJ-0
- WSJ-1
- National-Singapore-Corpus-Part-1
- National-Singapore-Corpus-Part-6
- voxpopuli
- europarl
- multilingual_librispeech
model-index:
- name: stt_en_conformer_ctc_small
results:
- task:
type: automatic-speech-recognition
dataset:
name: Librispeech (clean)
type: librispeech_asr
config: other
split: test
args:
language: en
metrics:
- type: wer
value: 8.1
name: WER
---
## Model Overview
<DESCRIBE IN ONE LINE THE MODEL AND ITS USE>
## NVIDIA NeMo: Training
To train, fine-tune or play with the model you will need to install [NVIDIA NeMo](https://github.com/NVIDIA/NeMo). We recommend you install it after you've installed latest Pytorch version.
```
pip install nemo_toolkit['all']
```
## How to Use this Model
The model is available for use in the NeMo toolkit [3], and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset.
### Automatically instantiate the model
```python
import nemo.collections.asr as nemo_asr
asr_model = nemo_asr.models.ASRModel.from_pretrained("wenqiglantz/stt_en_conformer_ctc_small")
```
### Transcribing using Python
First, let's get a sample
```
wget https://dldata-public.s3.us-east-2.amazonaws.com/2086-149220-0033.wav
```
Then simply do:
```
asr_model.transcribe(['2086-149220-0033.wav'])
```
### Transcribing many audio files
```shell
python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py pretrained_name="wenqiglantz/stt_en_conformer_ctc_small" audio_dir="<DIRECTORY CONTAINING AUDIO FILES>"
```
### Input
This model accepts 16000 KHz Mono-channel Audio (wav files) as input.
### Output
This model provides transcribed speech as a string for a given audio sample.
## Model Architecture
<ADD SOME INFORMATION ABOUT THE ARCHITECTURE>
## Training
<ADD INFORMATION ABOUT HOW THE MODEL WAS TRAINED - HOW MANY EPOCHS, AMOUNT OF COMPUTE ETC>
### Datasets
<LIST THE NAME AND SPLITS OF DATASETS USED TO TRAIN THIS MODEL (ALONG WITH LANGUAGE AND ANY ADDITIONAL INFORMATION)>
## Performance
<LIST THE SCORES OF THE MODEL -
OR
USE THE Hugging Face Evaluate LiBRARY TO UPLOAD METRICS>
## Limitations
<DECLARE ANY POTENTIAL LIMITATIONS OF THE MODEL>
Eg:
Since this model was trained on publicly available speech datasets, the performance of this model might degrade for speech which includes technical terms, or vernacular that the model has not been trained on. The model might also perform worse for accented speech.
## References
<ADD ANY REFERENCES HERE AS NEEDED>
[1] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo)
|
shirzady1934/distilgpt-monolinugal | shirzady1934 | 2023-12-28T17:21:16Z | 10 | 0 | peft | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:distilbert/distilgpt2",
"base_model:adapter:distilbert/distilgpt2",
"license:apache-2.0",
"region:us"
] | null | 2023-12-28T09:07:16Z | ---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
base_model: distilgpt2
model-index:
- name: distilgpt-monolinugal
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilgpt-monolinugal
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4876
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 12
- eval_batch_size: 12
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 96
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 8
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.3098 | 0.16 | 200 | 3.5905 |
| 3.2847 | 0.32 | 400 | 3.5644 |
| 3.2612 | 0.48 | 600 | 3.5504 |
| 3.2636 | 0.64 | 800 | 3.5384 |
| 3.2481 | 0.8 | 1000 | 3.5301 |
| 3.2393 | 0.96 | 1200 | 3.5233 |
| 3.2381 | 1.12 | 1400 | 3.5184 |
| 3.2317 | 1.28 | 1600 | 3.5168 |
| 3.2244 | 1.44 | 1800 | 3.5123 |
| 3.2258 | 1.6 | 2000 | 3.5117 |
| 3.2238 | 1.76 | 2200 | 3.5058 |
| 3.2376 | 1.92 | 2400 | 3.5058 |
| 3.212 | 2.08 | 2600 | 3.5044 |
| 3.231 | 2.24 | 2800 | 3.5019 |
| 3.2044 | 2.4 | 3000 | 3.5003 |
| 3.2107 | 2.57 | 3200 | 3.5002 |
| 3.2096 | 2.73 | 3400 | 3.4996 |
| 3.215 | 2.89 | 3600 | 3.4963 |
| 3.2092 | 3.05 | 3800 | 3.4979 |
| 3.2034 | 3.21 | 4000 | 3.4964 |
| 3.1992 | 3.37 | 4200 | 3.4971 |
| 3.1975 | 3.53 | 4400 | 3.4941 |
| 3.222 | 3.69 | 4600 | 3.4932 |
| 3.2104 | 3.85 | 4800 | 3.4927 |
| 3.199 | 4.01 | 5000 | 3.4918 |
| 3.2033 | 4.17 | 5200 | 3.4927 |
| 3.201 | 4.33 | 5400 | 3.4924 |
| 3.1947 | 4.49 | 5600 | 3.4931 |
| 3.2172 | 4.65 | 5800 | 3.4907 |
| 3.201 | 4.81 | 6000 | 3.4908 |
| 3.2089 | 4.97 | 6200 | 3.4892 |
| 3.206 | 5.13 | 6400 | 3.4896 |
| 3.2074 | 5.29 | 6600 | 3.4884 |
| 3.2046 | 5.45 | 6800 | 3.4891 |
| 3.1899 | 5.61 | 7000 | 3.4888 |
| 3.196 | 5.77 | 7200 | 3.4891 |
| 3.1946 | 5.93 | 7400 | 3.4880 |
| 3.1951 | 6.09 | 7600 | 3.4887 |
| 3.1998 | 6.25 | 7800 | 3.4878 |
| 3.1775 | 6.41 | 8000 | 3.4880 |
| 3.1947 | 6.57 | 8200 | 3.4880 |
| 3.1876 | 6.73 | 8400 | 3.4876 |
| 3.1984 | 6.89 | 8600 | 3.4878 |
| 3.1927 | 7.05 | 8800 | 3.4875 |
| 3.2006 | 7.21 | 9000 | 3.4875 |
| 3.2042 | 7.37 | 9200 | 3.4875 |
| 3.1856 | 7.54 | 9400 | 3.4877 |
| 3.1952 | 7.7 | 9600 | 3.4877 |
| 3.1981 | 7.86 | 9800 | 3.4876 |
### Framework versions
- PEFT 0.7.1
- Transformers 4.36.2
- Pytorch 1.13.0+cu116
- Datasets 2.16.0
- Tokenizers 0.15.0 |
VinayHajare/ppo-LunarLander-v2-from-scratch | VinayHajare | 2023-12-28T17:17:47Z | 0 | 0 | null | [
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
] | reinforcement-learning | 2023-12-28T17:11:14Z | ---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -148.56 +/- 78.86
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'PPO'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 1000000
'learning_rate': 0.0003
'num_envs': 16
'num_steps': 1024
'anneal_lr': True
'gae': True
'gamma': 0.999
'gae_lambda': 0.98
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'VinayHajare/ppo-LunarLander-v2-from-scratch'
'batch_size': 16384
'minibatch_size': 4096}
```
|
janhq/nitro-v1-e1-GGUF | janhq | 2023-12-28T17:17:29Z | 0 | 0 | null | [
"gguf",
"en",
"base_model:jan-hq/nitro-v1-e1",
"base_model:quantized:jan-hq/nitro-v1-e1",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2023-12-28T16:43:57Z | ---
license: apache-2.0
language:
- en
base_model: jan-hq/nitro-v1-e1
model_creator: jan-hq
model_name: nitro-v1-e1
quantized_by: JanHQ
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://github.com/janhq/jan/assets/89722390/35daac7d-b895-487c-a6ac-6663daaad78e" alt="Jan banner" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<p align="center">
<a href="https://jan.ai/">Jan</a>
- <a href="https://discord.gg/AsJ8krTT3N">Discord</a>
</p>
<!-- header end -->
# Model Description
This is a GGUF version of [jan-hq/nitro-v1-e1](https://huggingface.co/jan-hq/nitro-v1-e1)
- Model creator: [jan-hq](https://huggingface.co/jan-hq)
- Original model: [nitro-v1-e1](https://huggingface.co/jan-hq/nitro-v1-e1)
- Model description: [Readme](https://huggingface.co/jan-hq/nitro-v1-e1/blob/main/README.md)
# About Jan
Jan believes in the need for an open-source AI ecosystem and is building the infra and tooling to allow open-source AIs to compete on a level playing field with proprietary ones.
Jan's long-term vision is to build a cognitive framework for future robots, who are practical, useful assistants for humans and businesses in everyday life.
# Jan Model Converter
This is a repository for the [open-source converter](https://github.com/janhq/model-converter. We would be grateful if the community could contribute and strengthen this repository. We are aiming to expand the repo that can convert into various types of format
|
turboderp/CodeLlama-34B-instruct-exl2 | turboderp | 2023-12-28T17:16:18Z | 0 | 8 | null | [
"region:us"
] | null | 2023-09-22T23:28:28Z | EXL2 quants of CodeLlama2-34B-instruct
[2.70 bits per weight](https://huggingface.co/turboderp/CodeLlama-34B-instruct-exl2/tree/2.7bpw)
[3.00 bits per weight](https://huggingface.co/turboderp/CodeLlama-34B-instruct-exl2/tree/3.0bpw)
[3.50 bits per weight](https://huggingface.co/turboderp/CodeLlama-34B-instruct-exl2/tree/3.5bpw)
[4.00 bits per weight](https://huggingface.co/turboderp/CodeLlama-34B-instruct-exl2/tree/4.0bpw)
[4.65 bits per weight](https://huggingface.co/turboderp/CodeLlama-34B-instruct-exl2/tree/4.65bpw)
[6.00 bits per weight](https://huggingface.co/turboderp/CodeLlama-34B-instruct-exl2/tree/6.0bpw)
[measurement.json](https://huggingface.co/turboderp/CodeLlama-34B-instruct-exl2/blob/main/measurement.json) |
varshay/niftyllama-7b | varshay | 2023-12-28T17:14:45Z | 0 | 0 | peft | [
"peft",
"region:us"
] | null | 2023-10-17T21:24:10Z | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.4.0
|
turboderp/Llama2-70B-chat-exl2 | turboderp | 2023-12-28T17:12:44Z | 30 | 9 | null | [
"region:us"
] | null | 2023-09-17T11:11:57Z | EXL2 quants of Llama2-70B-chat
[2.30 bits per weight](https://huggingface.co/turboderp/Llama2-70B-chat-exl2/tree/2.3bpw)
[2.35 bits per weight](https://huggingface.co/turboderp/Llama2-70B-chat-exl2/tree/2.35bpw)
[2.40 bits per weight](https://huggingface.co/turboderp/Llama2-70B-chat-exl2/tree/2.4bpw)
[2.45 bits per weight](https://huggingface.co/turboderp/Llama2-70B-chat-exl2/tree/2.45bpw)
[2.50 bits per weight](https://huggingface.co/turboderp/Llama2-70B-chat-exl2/tree/2.5bpw)
[2.55 bits per weight](https://huggingface.co/turboderp/Llama2-70B-chat-exl2/tree/2.55bpw)
[2.60 bits per weight](https://huggingface.co/turboderp/Llama2-70B-chat-exl2/tree/2.6bpw)
[2.70 bits per weight](https://huggingface.co/turboderp/Llama2-70B-chat-exl2/tree/2.7bpw)
[3.00 bits per weight](https://huggingface.co/turboderp/Llama2-70B-chat-exl2/tree/3.0bpw)
[4.00 bits per weight](https://huggingface.co/turboderp/Llama2-70B-chat-exl2/tree/4.0bpw)
[4.65 bits per weight](https://huggingface.co/turboderp/Llama2-70B-chat-exl2/tree/4.65bpw)
[measurement.json](https://huggingface.co/turboderp/Llama2-70B-chat-exl2/blob/main/measurement.json) |
TheBloke/Sakura-SOLAR-Instruct-AWQ | TheBloke | 2023-12-28T17:10:54Z | 10 | 1 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"en",
"base_model:kyujinpy/Sakura-SOLAR-Instruct",
"base_model:quantized:kyujinpy/Sakura-SOLAR-Instruct",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"awq",
"region:us"
] | text-generation | 2023-12-28T13:06:33Z | ---
base_model: kyujinpy/Sakura-SOLAR-Instruct
inference: false
language:
- en
license: cc-by-nc-sa-4.0
model_creator: KyujinHan
model_name: Sakura Solar Instruct
model_type: solar
pipeline_tag: text-generation
prompt_template: '### System:
{system_message}
### User:
{prompt}
### Assistant:
'
quantized_by: TheBloke
---
<!-- markdownlint-disable MD041 -->
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Sakura Solar Instruct - AWQ
- Model creator: [KyujinHan](https://huggingface.co/kyujinpy)
- Original model: [Sakura Solar Instruct](https://huggingface.co/kyujinpy/Sakura-SOLAR-Instruct)
<!-- description start -->
## Description
This repo contains AWQ model files for [KyujinHan's Sakura Solar Instruct](https://huggingface.co/kyujinpy/Sakura-SOLAR-Instruct).
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
### About AWQ
AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.
AWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead.
It is supported by:
- [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ
- [vLLM](https://github.com/vllm-project/vllm) - version 0.2.2 or later for support for all model types.
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code
<!-- description end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Sakura-SOLAR-Instruct-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Sakura-SOLAR-Instruct-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Sakura-SOLAR-Instruct-GGUF)
* [KyujinHan's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/kyujinpy/Sakura-SOLAR-Instruct)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Orca-Hashes
```
### System:
{system_message}
### User:
{prompt}
### Assistant:
```
<!-- prompt-template end -->
<!-- README_AWQ.md-provided-files start -->
## Provided files, and AWQ parameters
I currently release 128g GEMM models only. The addition of group_size 32 models, and GEMV kernel models, is being actively considered.
Models are released as sharded safetensors files.
| Branch | Bits | GS | AWQ Dataset | Seq Len | Size |
| ------ | ---- | -- | ----------- | ------- | ---- |
| [main](https://huggingface.co/TheBloke/Sakura-SOLAR-Instruct-AWQ/tree/main) | 4 | 128 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 5.96 GB
<!-- README_AWQ.md-provided-files end -->
<!-- README_AWQ.md-text-generation-webui start -->
## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.
1. Click the **Model tab**.
2. Under **Download custom model or LoRA**, enter `TheBloke/Sakura-SOLAR-Instruct-AWQ`.
3. Click **Download**.
4. The model will start downloading. Once it's finished it will say "Done".
5. In the top left, click the refresh icon next to **Model**.
6. In the **Model** dropdown, choose the model you just downloaded: `Sakura-SOLAR-Instruct-AWQ`
7. Select **Loader: AutoAWQ**.
8. Click Load, and the model will load and is now ready for use.
9. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right.
10. Once you're ready, click the **Text Generation** tab and enter a prompt to get started!
<!-- README_AWQ.md-text-generation-webui end -->
<!-- README_AWQ.md-use-from-vllm start -->
## Multi-user inference server: vLLM
Documentation on installing and using vLLM [can be found here](https://vllm.readthedocs.io/en/latest/).
- Please ensure you are using vLLM version 0.2 or later.
- When using vLLM as a server, pass the `--quantization awq` parameter.
For example:
```shell
python3 -m vllm.entrypoints.api_server --model TheBloke/Sakura-SOLAR-Instruct-AWQ --quantization awq --dtype auto
```
- When using vLLM from Python code, again set `quantization=awq`.
For example:
```python
from vllm import LLM, SamplingParams
prompts = [
"Tell me about AI",
"Write a story about llamas",
"What is 291 - 150?",
"How much wood would a woodchuck chuck if a woodchuck could chuck wood?",
]
prompt_template=f'''### System:
{system_message}
### User:
{prompt}
### Assistant:
'''
prompts = [prompt_template.format(prompt=prompt) for prompt in prompts]
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
llm = LLM(model="TheBloke/Sakura-SOLAR-Instruct-AWQ", quantization="awq", dtype="auto")
outputs = llm.generate(prompts, sampling_params)
# Print the outputs.
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
```
<!-- README_AWQ.md-use-from-vllm start -->
<!-- README_AWQ.md-use-from-tgi start -->
## Multi-user inference server: Hugging Face Text Generation Inference (TGI)
Use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0`
Example Docker parameters:
```shell
--model-id TheBloke/Sakura-SOLAR-Instruct-AWQ --port 3000 --quantize awq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096
```
Example Python code for interfacing with TGI (requires [huggingface-hub](https://github.com/huggingface/huggingface_hub) 0.17.0 or later):
```shell
pip3 install huggingface-hub
```
```python
from huggingface_hub import InferenceClient
endpoint_url = "https://your-endpoint-url-here"
prompt = "Tell me about AI"
prompt_template=f'''### System:
{system_message}
### User:
{prompt}
### Assistant:
'''
client = InferenceClient(endpoint_url)
response = client.text_generation(prompt,
max_new_tokens=128,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1)
print(f"Model output: ", response)
```
<!-- README_AWQ.md-use-from-tgi end -->
<!-- README_AWQ.md-use-from-python start -->
## Inference from Python code using Transformers
### Install the necessary packages
- Requires: [Transformers](https://huggingface.co/docs/transformers) 4.35.0 or later.
- Requires: [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) 0.1.6 or later.
```shell
pip3 install --upgrade "autoawq>=0.1.6" "transformers>=4.35.0"
```
Note that if you are using PyTorch 2.0.1, the above AutoAWQ command will automatically upgrade you to PyTorch 2.1.0.
If you are using CUDA 11.8 and wish to continue using PyTorch 2.0.1, instead run this command:
```shell
pip3 install https://github.com/casper-hansen/AutoAWQ/releases/download/v0.1.6/autoawq-0.1.6+cu118-cp310-cp310-linux_x86_64.whl
```
If you have problems installing [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) using the pre-built wheels, install it from source instead:
```shell
pip3 uninstall -y autoawq
git clone https://github.com/casper-hansen/AutoAWQ
cd AutoAWQ
pip3 install .
```
### Transformers example code (requires Transformers 4.35.0 and later)
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
model_name_or_path = "TheBloke/Sakura-SOLAR-Instruct-AWQ"
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
model = AutoModelForCausalLM.from_pretrained(
model_name_or_path,
low_cpu_mem_usage=True,
device_map="cuda:0"
)
# Using the text streamer to stream output one token at a time
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
prompt = "Tell me about AI"
prompt_template=f'''### System:
{system_message}
### User:
{prompt}
### Assistant:
'''
# Convert prompt to tokens
tokens = tokenizer(
prompt_template,
return_tensors='pt'
).input_ids.cuda()
generation_params = {
"do_sample": True,
"temperature": 0.7,
"top_p": 0.95,
"top_k": 40,
"max_new_tokens": 512,
"repetition_penalty": 1.1
}
# Generate streamed output, visible one token at a time
generation_output = model.generate(
tokens,
streamer=streamer,
**generation_params
)
# Generation without a streamer, which will include the prompt in the output
generation_output = model.generate(
tokens,
**generation_params
)
# Get the tokens from the output, decode them, print them
token_output = generation_output[0]
text_output = tokenizer.decode(token_output)
print("model.generate output: ", text_output)
# Inference is also possible via Transformers' pipeline
from transformers import pipeline
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
**generation_params
)
pipe_output = pipe(prompt_template)[0]['generated_text']
print("pipeline output: ", pipe_output)
```
<!-- README_AWQ.md-use-from-python end -->
<!-- README_AWQ.md-compatibility start -->
## Compatibility
The files provided are tested to work with:
- [text-generation-webui](https://github.com/oobabooga/text-generation-webui) using `Loader: AutoAWQ`.
- [vLLM](https://github.com/vllm-project/vllm) version 0.2.0 and later.
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) version 1.1.0 and later.
- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later.
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) version 0.1.1 and later.
<!-- README_AWQ.md-compatibility end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
# Original model card: KyujinHan's Sakura Solar Instruct
# **Sakura-SOLAR-Instruct**
<img src='./sakura.png' width=512>
**(주)미디어그룹사람과숲과 (주)마커의 LLM 연구 컨소시엄에서 개발된 모델입니다**
## Model Details
**Model Developers** Kyujin Han (kyujinpy)
**Method**
Using [Mergekit](https://github.com/cg123/mergekit).
I shared the information about my model. (training and code)
**Please see: [⭐Sakura-SOLAR](https://github.com/KyujinHan/Sakura-SOLAR-DPO).**
**Blog**
- [Sakura-SOLAR 모델 제작 과정 및 후기](https://kyujinpy.tistory.com/122).
# **Model Benchmark**
## Open leaderboard
- Follow up as [link](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
| Model | Average | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Sakura-SOLAR-Instruct-DPO-v2 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| Sakura-SOLAR-Instruct-DPO-v1 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| [kyujinpy/Sakura-SOLAR-Instruct](https://huggingface.co/kyujinpy/Sakura-SOLAR-Instruct) | 74.40 | 70.99 | 88.42 | 66.33 | 71.79 | 83.66 | 65.20
> Rank1 2023.12.27 PM 11:50
# Implementation Code
```python
### KO-Platypus
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
repo = "kyujinpy/Sakura-SOLAR-Instruct"
OpenOrca = AutoModelForCausalLM.from_pretrained(
repo,
return_dict=True,
torch_dtype=torch.float16,
device_map='auto'
)
OpenOrca_tokenizer = AutoTokenizer.from_pretrained(repo)
```
---
|
TheBloke/SOLARC-MOE-10.7Bx4-GPTQ | TheBloke | 2023-12-28T17:10:25Z | 24 | 4 | transformers | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"conversational",
"ko",
"base_model:DopeorNope/SOLARC-MOE-10.7Bx4",
"base_model:quantized:DopeorNope/SOLARC-MOE-10.7Bx4",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"gptq",
"region:us"
] | text-generation | 2023-12-28T15:17:44Z | ---
base_model: DopeorNope/SOLARC-MOE-10.7Bx4
inference: false
language:
- ko
library_name: transformers
license: cc-by-nc-sa-4.0
model_creator: Seungyoo Lee
model_name: Solarc MOE 10.7Bx4
model_type: mixtral
pipeline_tag: text-generation
prompt_template: '### User:
{prompt}
### Assistant:
'
quantized_by: TheBloke
---
<!-- markdownlint-disable MD041 -->
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Solarc MOE 10.7Bx4 - GPTQ
- Model creator: [Seungyoo Lee](https://huggingface.co/DopeorNope)
- Original model: [Solarc MOE 10.7Bx4](https://huggingface.co/DopeorNope/SOLARC-MOE-10.7Bx4)
<!-- description start -->
# Description
This repo contains GPTQ model files for [Seungyoo Lee's Solarc MOE 10.7Bx4](https://huggingface.co/DopeorNope/SOLARC-MOE-10.7Bx4).
Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them.
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
<!-- description end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/SOLARC-MOE-10.7Bx4-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/SOLARC-MOE-10.7Bx4-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/SOLARC-MOE-10.7Bx4-GGUF)
* [Seungyoo Lee's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/DopeorNope/SOLARC-MOE-10.7Bx4)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: User-Assistant-Newlines
```
### User:
{prompt}
### Assistant:
```
<!-- prompt-template end -->
<!-- README_GPTQ.md-compatible clients start -->
## Known compatible clients / servers
GPTQ models are currently supported on Linux (NVidia/AMD) and Windows (NVidia only). macOS users: please use GGUF models.
These GPTQ models are known to work in the following inference servers/webuis.
- [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
- [KoboldAI United](https://github.com/henk717/koboldai)
- [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui)
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
This may not be a complete list; if you know of others, please let me know!
<!-- README_GPTQ.md-compatible clients end -->
<!-- README_GPTQ.md-provided-files start -->
## Provided files, and GPTQ parameters
Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements.
Each separate quant is in a different branch. See below for instructions on fetching from different branches.
Most GPTQ files are made with AutoGPTQ. Mistral models are currently made with Transformers.
<details>
<summary>Explanation of GPTQ parameters</summary>
- Bits: The bit size of the quantised model.
- GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value.
- Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now.
- Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy.
- GPTQ dataset: The calibration dataset used during quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ calibration dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s).
- Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences.
- ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama and Mistral models in 4-bit.
</details>
| Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc |
| ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- |
| [main](https://huggingface.co/TheBloke/SOLARC-MOE-10.7Bx4-GPTQ/tree/main) | 4 | None | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 18.50 GB | No | 4-bit, with Act Order. No group size, to lower VRAM requirements. |
| [gptq-4bit-128g-actorder_True](https://huggingface.co/TheBloke/SOLARC-MOE-10.7Bx4-GPTQ/tree/gptq-4bit-128g-actorder_True) | 4 | 128 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 19.18 GB | No | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. |
| [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/SOLARC-MOE-10.7Bx4-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 21.28 GB | No | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. |
| [gptq-3bit-128g-actorder_True](https://huggingface.co/TheBloke/SOLARC-MOE-10.7Bx4-GPTQ/tree/gptq-3bit-128g-actorder_True) | 3 | 128 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 14.66 GB | No | 3-bit, with group size 128g and act-order. Higher quality than 128g-False. |
| [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/SOLARC-MOE-10.7Bx4-GPTQ/tree/gptq-8bit--1g-actorder_True) | 8 | None | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 36.42 GB | No | 8-bit, with Act Order. No group size, to lower VRAM requirements. |
| [gptq-3bit-32g-actorder_True](https://huggingface.co/TheBloke/SOLARC-MOE-10.7Bx4-GPTQ/tree/gptq-3bit-32g-actorder_True) | 3 | 32 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 16.66 GB | No | 3-bit, with group size 64g and act-order. Highest quality 3-bit option. |
| [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/SOLARC-MOE-10.7Bx4-GPTQ/tree/gptq-8bit-128g-actorder_True) | 8 | 128 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 37.24 GB | No | 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. |
<!-- README_GPTQ.md-provided-files end -->
<!-- README_GPTQ.md-download-from-branches start -->
## How to download, including from branches
### In text-generation-webui
To download from the `main` branch, enter `TheBloke/SOLARC-MOE-10.7Bx4-GPTQ` in the "Download model" box.
To download from another branch, add `:branchname` to the end of the download name, eg `TheBloke/SOLARC-MOE-10.7Bx4-GPTQ:gptq-4bit-128g-actorder_True`
### From the command line
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
To download the `main` branch to a folder called `SOLARC-MOE-10.7Bx4-GPTQ`:
```shell
mkdir SOLARC-MOE-10.7Bx4-GPTQ
huggingface-cli download TheBloke/SOLARC-MOE-10.7Bx4-GPTQ --local-dir SOLARC-MOE-10.7Bx4-GPTQ --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
```shell
mkdir SOLARC-MOE-10.7Bx4-GPTQ
huggingface-cli download TheBloke/SOLARC-MOE-10.7Bx4-GPTQ --revision gptq-4bit-128g-actorder_True --local-dir SOLARC-MOE-10.7Bx4-GPTQ --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage</summary>
If you remove the `--local-dir-use-symlinks False` parameter, the files will instead be stored in the central Hugging Face cache directory (default location on Linux is: `~/.cache/huggingface`), and symlinks will be added to the specified `--local-dir`, pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model.
The cache location can be changed with the `HF_HOME` environment variable, and/or the `--cache-dir` parameter to `huggingface-cli`.
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
mkdir SOLARC-MOE-10.7Bx4-GPTQ
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/SOLARC-MOE-10.7Bx4-GPTQ --local-dir SOLARC-MOE-10.7Bx4-GPTQ --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
### With `git` (**not** recommended)
To clone a specific branch with `git`, use a command like this:
```shell
git clone --single-branch --branch gptq-4bit-128g-actorder_True https://huggingface.co/TheBloke/SOLARC-MOE-10.7Bx4-GPTQ
```
Note that using Git with HF repos is strongly discouraged. It will be much slower than using `huggingface-hub`, and will use twice as much disk space as it has to store the model files twice (it stores every byte both in the intended target folder, and again in the `.git` folder as a blob.)
<!-- README_GPTQ.md-download-from-branches end -->
<!-- README_GPTQ.md-text-generation-webui start -->
## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.
1. Click the **Model tab**.
2. Under **Download custom model or LoRA**, enter `TheBloke/SOLARC-MOE-10.7Bx4-GPTQ`.
- To download from a specific branch, enter for example `TheBloke/SOLARC-MOE-10.7Bx4-GPTQ:gptq-4bit-128g-actorder_True`
- see Provided Files above for the list of branches for each option.
3. Click **Download**.
4. The model will start downloading. Once it's finished it will say "Done".
5. In the top left, click the refresh icon next to **Model**.
6. In the **Model** dropdown, choose the model you just downloaded: `SOLARC-MOE-10.7Bx4-GPTQ`
7. The model will automatically load, and is now ready for use!
8. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right.
- Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`.
9. Once you're ready, click the **Text Generation** tab and enter a prompt to get started!
<!-- README_GPTQ.md-text-generation-webui end -->
<!-- README_GPTQ.md-use-from-tgi start -->
## Serving this model from Text Generation Inference (TGI)
It's recommended to use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0`
Example Docker parameters:
```shell
--model-id TheBloke/SOLARC-MOE-10.7Bx4-GPTQ --port 3000 --quantize gptq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096
```
Example Python code for interfacing with TGI (requires huggingface-hub 0.17.0 or later):
```shell
pip3 install huggingface-hub
```
```python
from huggingface_hub import InferenceClient
endpoint_url = "https://your-endpoint-url-here"
prompt = "Tell me about AI"
prompt_template=f'''### User:
{prompt}
### Assistant:
'''
client = InferenceClient(endpoint_url)
response = client.text_generation(
prompt_template,
max_new_tokens=128,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1
)
print(f"Model output: {response}")
```
<!-- README_GPTQ.md-use-from-tgi end -->
<!-- README_GPTQ.md-use-from-python start -->
## Python code example: inference from this GPTQ model
### Install the necessary packages
Requires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later.
```shell
pip3 install --upgrade transformers optimum
# If using PyTorch 2.1 + CUDA 12.x:
pip3 install --upgrade auto-gptq
# or, if using PyTorch 2.1 + CUDA 11.x:
pip3 install --upgrade auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/
```
If you are using PyTorch 2.0, you will need to install AutoGPTQ from source. Likewise if you have problems with the pre-built wheels, you should try building from source:
```shell
pip3 uninstall -y auto-gptq
git clone https://github.com/PanQiWei/AutoGPTQ
cd AutoGPTQ
git checkout v0.5.1
pip3 install .
```
### Example Python code
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
model_name_or_path = "TheBloke/SOLARC-MOE-10.7Bx4-GPTQ"
# To use a different branch, change revision
# For example: revision="gptq-4bit-128g-actorder_True"
model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
device_map="auto",
trust_remote_code=False,
revision="main")
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
prompt = "Write a story about llamas"
system_message = "You are a story writing assistant"
prompt_template=f'''### User:
{prompt}
### Assistant:
'''
print("\n\n*** Generate:")
input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512)
print(tokenizer.decode(output[0]))
# Inference can also be done using transformers' pipeline
print("*** Pipeline:")
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1
)
print(pipe(prompt_template)[0]['generated_text'])
```
<!-- README_GPTQ.md-use-from-python end -->
<!-- README_GPTQ.md-compatibility start -->
## Compatibility
The files provided are tested to work with Transformers. For non-Mistral models, AutoGPTQ can also be used directly.
[ExLlama](https://github.com/turboderp/exllama) is compatible with Llama architecture models (including Mistral, Yi, DeepSeek, SOLAR, etc) in 4-bit. Please see the Provided Files table above for per-file compatibility.
For a list of clients/servers, please see "Known compatible clients / servers", above.
<!-- README_GPTQ.md-compatibility end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
# Original model card: Seungyoo Lee's Solarc MOE 10.7Bx4
**The license is `cc-by-nc-sa-4.0`.**
# **🐻❄️SOLARC-MOE-10.7Bx4🐻❄️**
## Model Details
**Model Developers** Seungyoo Lee(DopeorNope)
I am in charge of Large Language Models (LLMs) at Markr AI team in South Korea.
**Input** Models input text only.
**Output** Models generate text only.
**Model Architecture**
SOLARC-MOE-10.7Bx4 is an auto-regressive language model based on the SOLAR architecture.
---
## **Base Model**
[kyujinpy/Sakura-SOLAR-Instruct](https://huggingface.co/kyujinpy/Sakura-SOLAR-Instruct)
[Weyaxi/SauerkrautLM-UNA-SOLAR-Instruct](https://huggingface.co/Weyaxi/SauerkrautLM-UNA-SOLAR-Instruct)
[VAGOsolutions/SauerkrautLM-SOLAR-Instruct](https://huggingface.co/VAGOsolutions/SauerkrautLM-SOLAR-Instruct)
[fblgit/UNA-SOLAR-10.7B-Instruct-v1.0](https://huggingface.co/fblgit/UNA-SOLAR-10.7B-Instruct-v1.0)
## **Implemented Method**
I have built a model using the Mixture of Experts (MOE) approach, utilizing each of these models as the base.
---
# Implementation Code
## Load model
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
repo = "DopeorNope/SOLARC-MOE-10.7Bx4"
OpenOrca = AutoModelForCausalLM.from_pretrained(
repo,
return_dict=True,
torch_dtype=torch.float16,
device_map='auto'
)
OpenOrca_tokenizer = AutoTokenizer.from_pretrained(repo)
```
---
|
LoneStriker/Aurora-Nights-70B-v1.0-6.0bpw-h6-exl2 | LoneStriker | 2023-12-28T17:09:56Z | 6 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"en",
"arxiv:2307.11760",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2023-12-28T16:39:35Z | ---
license: llama2
language:
- en
---
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/aGUU0O9.png" alt="AuroraNights" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
### Overview
This model is a blend of [allenai/tulu-2-dpo-70b](https://huggingface.co/allenai/tulu-2-dpo-70b), [Xwin-LM/Xwin-LM-70B-V0.1](https://huggingface.co/Xwin-LM/Xwin-LM-70B-V0.1), and [dreamgen/opus-v0.5-70b](https://huggingface.co/dreamgen/opus-v0.5-70b). I then merged [nRuaif/fiction.live-Kimiko-V2-70B](https://huggingface.co/nRuaif/fiction.live-Kimiko-V2-70B) into the resultant blend. See the bottom of this card for the exact settings used.
This model is good at both following instructions and producing creative, uncensored storytelling and roleplaying content.
This model turned out quite uncensored. *You are responsible for whatever you do with it.*
This model was designed for roleplaying and storytelling and I think it does well at both. It *should* perform well at other tasks, but I haven't tested its capabilities in other areas.
### Sampler Tips
I recommend using the new Min-P sampler method with this model. The creator has a great [guide to it on Reddit](https://www.reddit.com/r/LocalLLaMA/comments/17vonjo/your_settings_are_probably_hurting_your_model_why/).
I find this model performs surprisingly well at 8192 context but you will probably get better results at 4096 context.
Experiment with any and all of the settings below, but trust me on a few points:
* This model works well with Min-P.
* Frequency Penalty set to 0.01 is like adding a dash of salt to the dish. Go higher at your own peril. 0 is fine too, but gosh I like 0.01.
If you save the below settings as a .json file, you can import them directly into Silly Tavern.
```
{
"temp": 1.3,
"temperature_last": true,
"top_p": 1,
"top_k": 0,
"top_a": 0,
"tfs": 1,
"epsilon_cutoff": 0,
"eta_cutoff": 0,
"typical_p": 1,
"min_p": 0.2,
"rep_pen": 1,
"rep_pen_range": 0,
"no_repeat_ngram_size": 0,
"penalty_alpha": 0,
"num_beams": 1,
"length_penalty": 1,
"min_length": 0,
"encoder_rep_pen": 1,
"freq_pen": 0.01,
"presence_pen": 0,
"do_sample": true,
"early_stopping": false,
"add_bos_token": true,
"truncation_length": 2048,
"ban_eos_token": false,
"skip_special_tokens": true,
"streaming": true,
"mirostat_mode": 0,
"mirostat_tau": 5,
"mirostat_eta": 0.1,
"guidance_scale": 1,
"negative_prompt": "",
"grammar_string": "",
"banned_tokens": "",
"ignore_eos_token_aphrodite": false,
"spaces_between_special_tokens_aphrodite": true,
"type": "ooba",
"legacy_api": false,
"sampler_order": [
6,
0,
1,
3,
4,
2,
5
],
"n": 1,
"rep_pen_size": 0,
"genamt": 550,
"max_length": 4096
}
```
### Prompt Tips
Try the following prompt template as a starting point for your own customizations. 'This is very important to my career' is from [this paper](https://arxiv.org/abs/2307.11760).
I recommend the Tulu instruction format.
*This model responds to prompting!* I highly recommend playing with your system prompt to see how it reacts.
If you save this as a .json file, you can import it directly into Silly Tavern.
```
{
"wrap": true,
"names": false,
"system_prompt": "You are an award-winning fiction author engaging in an uncensored literary roleplaying experience between a human (me) and a creative, obedient, and helpful AI assistant (you). It is VITAL that you follow all these instructions because this roleplay is very important to my career and I'll be fired from my job if it isn't good.\nROLEPLAY RULES:\n> It is vital that ONLY the human provides responses for {{user}}.\n> Reply as {{char}} using authentic, vivid, varied, explicit, accurate, creative, fresh, and descriptive responses that follow ALL provided narrative instructions. Stay in character as {{char}} and only write text for {{char}}.\n> Describe the scene and {{char}}'s sensory perceptions in vivid detail to immerse the reader in the story.\n> Keep your responses scoped to the current story beat and current scene.\n> Consider all available contextual information when narrating so that all the story details remain consistent between scenes.\n> Demonstrate {{char}}'s goals and motivations, and use subtle cues to hint at {{char}}'s mental state unless delving into {{char}}'s thoughts satisfies an explicit instruction or enhances the vividness of the scene.\n> When quoting {{char}}'s internal first-person thoughts (aka internal monologue, delivered in {{char}}'s own voice), *enclose the thoughts in asterisks like this*. Only use asterisks for thoughts.\n> Use strong action verbs and varied descriptions to produce dynamic, high-quality prose.",
"system_sequence": "",
"stop_sequence": "",
"input_sequence": "<|user|>\n",
"output_sequence": "<|assistant|>\n",
"separator_sequence": "",
"macro": true,
"names_force_groups": true,
"system_sequence_prefix": "",
"system_sequence_suffix": "",
"first_output_sequence": "",
"last_output_sequence": "<|assistant (provide varied, creative, and vivid narration; follow all narrative instructions; include all necessary possessive pronouns; maintain consistent story details; only roleplay as {{char}})|>\n",
"activation_regex": "",
"name": "Aurora-Nights"
}
```
### Licence and usage restrictions
Llama2 license inherited from base models, plus restrictions applicable to [Dreamgen/Opus](https://huggingface.co/dreamgen/opus-v0.5-70b).
### Tools Used
* [mergekit](https://github.com/cg123/mergekit)
```
models:
- model: NousResearch_Llama-2-70b-hf
# no parameters necessary for base model
- model: allenai_tulu-2-dpo-70b # primary
parameters:
density: 1.0
weight: 0.4
- model: Xwin-LM_Xwin-LM-70B-V0.1 # secondary
parameters:
density: 0.7
weight: 0.3
- model: dreamgen_opus-v0.5-70b # supporting, good at storytelling and roleplay
parameters:
density: 0.2
weight: 0.6
merge_method: dare_ties
base_model: NousResearch_Llama-2-70b-hf
parameters:
normalize: true
int8_mask: true
dtype: float32
``` |
TheBloke/SOLARC-MOE-10.7Bx4-AWQ | TheBloke | 2023-12-28T17:09:39Z | 10 | 2 | transformers | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"conversational",
"ko",
"base_model:DopeorNope/SOLARC-MOE-10.7Bx4",
"base_model:quantized:DopeorNope/SOLARC-MOE-10.7Bx4",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"awq",
"region:us"
] | text-generation | 2023-12-28T14:17:15Z | ---
base_model: DopeorNope/SOLARC-MOE-10.7Bx4
inference: false
language:
- ko
library_name: transformers
license: cc-by-nc-sa-4.0
model_creator: Seungyoo Lee
model_name: Solarc MOE 10.7Bx4
model_type: mixtral
pipeline_tag: text-generation
prompt_template: '### User:
{prompt}
### Assistant:
'
quantized_by: TheBloke
---
<!-- markdownlint-disable MD041 -->
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Solarc MOE 10.7Bx4 - AWQ
- Model creator: [Seungyoo Lee](https://huggingface.co/DopeorNope)
- Original model: [Solarc MOE 10.7Bx4](https://huggingface.co/DopeorNope/SOLARC-MOE-10.7Bx4)
<!-- description start -->
## Description
This repo contains AWQ model files for [Seungyoo Lee's Solarc MOE 10.7Bx4](https://huggingface.co/DopeorNope/SOLARC-MOE-10.7Bx4).
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
**MIXTRAL AWQ**
This is a Mixtral AWQ model.
For AutoAWQ inference, please install AutoAWQ 0.1.8 or later.
Support via Transformers is coming soon, via this PR: https://github.com/huggingface/transformers/pull/27950 which should be merged to Transformers `main` very soon.
vLLM: version 0.2.6 is confirmed to support Mixtral AWQs.
TGI: I tested version 1.3.3 and it loaded the model fine, but I was not able to get any output back. Further testing/debug is required. (Let me know if you get it working!)
### About AWQ
AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.
AWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead.
AWQ models are supported by (note that not all of these may support Mixtral models yet - see above):
- [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ
- [vLLM](https://github.com/vllm-project/vllm) - version 0.2.2 or later for support for all model types.
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code
<!-- description end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/SOLARC-MOE-10.7Bx4-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/SOLARC-MOE-10.7Bx4-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/SOLARC-MOE-10.7Bx4-GGUF)
* [Seungyoo Lee's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/DopeorNope/SOLARC-MOE-10.7Bx4)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: User-Assistant-Newlines
```
### User:
{prompt}
### Assistant:
```
<!-- prompt-template end -->
<!-- README_AWQ.md-provided-files start -->
## Provided files, and AWQ parameters
I currently release 128g GEMM models only. The addition of group_size 32 models, and GEMV kernel models, is being actively considered.
Models are released as sharded safetensors files.
| Branch | Bits | GS | AWQ Dataset | Seq Len | Size |
| ------ | ---- | -- | ----------- | ------- | ---- |
| [main](https://huggingface.co/TheBloke/SOLARC-MOE-10.7Bx4-AWQ/tree/main) | 4 | 128 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 19.14 GB
<!-- README_AWQ.md-provided-files end -->
<!-- README_AWQ.md-text-generation-webui start -->
## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.
1. Click the **Model tab**.
2. Under **Download custom model or LoRA**, enter `TheBloke/SOLARC-MOE-10.7Bx4-AWQ`.
3. Click **Download**.
4. The model will start downloading. Once it's finished it will say "Done".
5. In the top left, click the refresh icon next to **Model**.
6. In the **Model** dropdown, choose the model you just downloaded: `SOLARC-MOE-10.7Bx4-AWQ`
7. Select **Loader: AutoAWQ**.
8. Click Load, and the model will load and is now ready for use.
9. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right.
10. Once you're ready, click the **Text Generation** tab and enter a prompt to get started!
<!-- README_AWQ.md-text-generation-webui end -->
<!-- README_AWQ.md-use-from-vllm start -->
## Multi-user inference server: vLLM
Documentation on installing and using vLLM [can be found here](https://vllm.readthedocs.io/en/latest/).
- Please ensure you are using vLLM version 0.2 or later.
- When using vLLM as a server, pass the `--quantization awq` parameter.
For example:
```shell
python3 -m vllm.entrypoints.api_server --model TheBloke/SOLARC-MOE-10.7Bx4-AWQ --quantization awq --dtype auto
```
- When using vLLM from Python code, again set `quantization=awq`.
For example:
```python
from vllm import LLM, SamplingParams
prompts = [
"Tell me about AI",
"Write a story about llamas",
"What is 291 - 150?",
"How much wood would a woodchuck chuck if a woodchuck could chuck wood?",
]
prompt_template=f'''### User:
{prompt}
### Assistant:
'''
prompts = [prompt_template.format(prompt=prompt) for prompt in prompts]
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
llm = LLM(model="TheBloke/SOLARC-MOE-10.7Bx4-AWQ", quantization="awq", dtype="auto")
outputs = llm.generate(prompts, sampling_params)
# Print the outputs.
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
```
<!-- README_AWQ.md-use-from-vllm start -->
<!-- README_AWQ.md-use-from-tgi start -->
## Multi-user inference server: Hugging Face Text Generation Inference (TGI)
Use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0`
Example Docker parameters:
```shell
--model-id TheBloke/SOLARC-MOE-10.7Bx4-AWQ --port 3000 --quantize awq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096
```
Example Python code for interfacing with TGI (requires [huggingface-hub](https://github.com/huggingface/huggingface_hub) 0.17.0 or later):
```shell
pip3 install huggingface-hub
```
```python
from huggingface_hub import InferenceClient
endpoint_url = "https://your-endpoint-url-here"
prompt = "Tell me about AI"
prompt_template=f'''### User:
{prompt}
### Assistant:
'''
client = InferenceClient(endpoint_url)
response = client.text_generation(prompt,
max_new_tokens=128,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1)
print(f"Model output: ", response)
```
<!-- README_AWQ.md-use-from-tgi end -->
<!-- README_AWQ.md-use-from-python start -->
## Inference from Python code using Transformers
### Install the necessary packages
- Requires: [Transformers](https://huggingface.co/docs/transformers) 4.35.0 or later.
- Requires: [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) 0.1.6 or later.
```shell
pip3 install --upgrade "autoawq>=0.1.6" "transformers>=4.35.0"
```
Note that if you are using PyTorch 2.0.1, the above AutoAWQ command will automatically upgrade you to PyTorch 2.1.0.
If you are using CUDA 11.8 and wish to continue using PyTorch 2.0.1, instead run this command:
```shell
pip3 install https://github.com/casper-hansen/AutoAWQ/releases/download/v0.1.6/autoawq-0.1.6+cu118-cp310-cp310-linux_x86_64.whl
```
If you have problems installing [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) using the pre-built wheels, install it from source instead:
```shell
pip3 uninstall -y autoawq
git clone https://github.com/casper-hansen/AutoAWQ
cd AutoAWQ
pip3 install .
```
### Transformers example code (requires Transformers 4.35.0 and later)
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
model_name_or_path = "TheBloke/SOLARC-MOE-10.7Bx4-AWQ"
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
model = AutoModelForCausalLM.from_pretrained(
model_name_or_path,
low_cpu_mem_usage=True,
device_map="cuda:0"
)
# Using the text streamer to stream output one token at a time
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
prompt = "Tell me about AI"
prompt_template=f'''### User:
{prompt}
### Assistant:
'''
# Convert prompt to tokens
tokens = tokenizer(
prompt_template,
return_tensors='pt'
).input_ids.cuda()
generation_params = {
"do_sample": True,
"temperature": 0.7,
"top_p": 0.95,
"top_k": 40,
"max_new_tokens": 512,
"repetition_penalty": 1.1
}
# Generate streamed output, visible one token at a time
generation_output = model.generate(
tokens,
streamer=streamer,
**generation_params
)
# Generation without a streamer, which will include the prompt in the output
generation_output = model.generate(
tokens,
**generation_params
)
# Get the tokens from the output, decode them, print them
token_output = generation_output[0]
text_output = tokenizer.decode(token_output)
print("model.generate output: ", text_output)
# Inference is also possible via Transformers' pipeline
from transformers import pipeline
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
**generation_params
)
pipe_output = pipe(prompt_template)[0]['generated_text']
print("pipeline output: ", pipe_output)
```
<!-- README_AWQ.md-use-from-python end -->
<!-- README_AWQ.md-compatibility start -->
## Compatibility
The files provided are tested to work with:
- [text-generation-webui](https://github.com/oobabooga/text-generation-webui) using `Loader: AutoAWQ`.
- [vLLM](https://github.com/vllm-project/vllm) version 0.2.0 and later.
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) version 1.1.0 and later.
- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later.
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) version 0.1.1 and later.
<!-- README_AWQ.md-compatibility end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
# Original model card: Seungyoo Lee's Solarc MOE 10.7Bx4
**The license is `cc-by-nc-sa-4.0`.**
# **🐻❄️SOLARC-MOE-10.7Bx4🐻❄️**
## Model Details
**Model Developers** Seungyoo Lee(DopeorNope)
I am in charge of Large Language Models (LLMs) at Markr AI team in South Korea.
**Input** Models input text only.
**Output** Models generate text only.
**Model Architecture**
SOLARC-MOE-10.7Bx4 is an auto-regressive language model based on the SOLAR architecture.
---
## **Base Model**
[kyujinpy/Sakura-SOLAR-Instruct](https://huggingface.co/kyujinpy/Sakura-SOLAR-Instruct)
[Weyaxi/SauerkrautLM-UNA-SOLAR-Instruct](https://huggingface.co/Weyaxi/SauerkrautLM-UNA-SOLAR-Instruct)
[VAGOsolutions/SauerkrautLM-SOLAR-Instruct](https://huggingface.co/VAGOsolutions/SauerkrautLM-SOLAR-Instruct)
[fblgit/UNA-SOLAR-10.7B-Instruct-v1.0](https://huggingface.co/fblgit/UNA-SOLAR-10.7B-Instruct-v1.0)
## **Implemented Method**
I have built a model using the Mixture of Experts (MOE) approach, utilizing each of these models as the base.
---
# Implementation Code
## Load model
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
repo = "DopeorNope/SOLARC-MOE-10.7Bx4"
OpenOrca = AutoModelForCausalLM.from_pretrained(
repo,
return_dict=True,
torch_dtype=torch.float16,
device_map='auto'
)
OpenOrca_tokenizer = AutoTokenizer.from_pretrained(repo)
```
---
|
ntc-ai/SDXL-LoRA-slider.crazy-insane-stunt | ntc-ai | 2023-12-28T16:52:46Z | 4 | 0 | diffusers | [
"diffusers",
"text-to-image",
"stable-diffusion-xl",
"lora",
"template:sd-lora",
"template:sdxl-lora",
"sdxl-sliders",
"ntcai.xyz-sliders",
"concept",
"en",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:mit",
"region:us"
] | text-to-image | 2023-12-28T16:52:43Z |
---
language:
- en
thumbnail: "images/evaluate/crazy insane stunt.../crazy insane stunt_17_3.0.png"
widget:
- text: crazy insane stunt
output:
url: images/crazy insane stunt_17_3.0.png
- text: crazy insane stunt
output:
url: images/crazy insane stunt_19_3.0.png
- text: crazy insane stunt
output:
url: images/crazy insane stunt_20_3.0.png
- text: crazy insane stunt
output:
url: images/crazy insane stunt_21_3.0.png
- text: crazy insane stunt
output:
url: images/crazy insane stunt_22_3.0.png
tags:
- text-to-image
- stable-diffusion-xl
- lora
- template:sd-lora
- template:sdxl-lora
- sdxl-sliders
- ntcai.xyz-sliders
- concept
- diffusers
license: "mit"
inference: false
instance_prompt: "crazy insane stunt"
base_model: "stabilityai/stable-diffusion-xl-base-1.0"
---
# ntcai.xyz slider - crazy insane stunt (SDXL LoRA)
| Strength: -3 | Strength: 0 | Strength: 3 |
| --- | --- | --- |
| <img src="images/crazy insane stunt_17_-3.0.png" width=256 height=256 /> | <img src="images/crazy insane stunt_17_0.0.png" width=256 height=256 /> | <img src="images/crazy insane stunt_17_3.0.png" width=256 height=256 /> |
| <img src="images/crazy insane stunt_19_-3.0.png" width=256 height=256 /> | <img src="images/crazy insane stunt_19_0.0.png" width=256 height=256 /> | <img src="images/crazy insane stunt_19_3.0.png" width=256 height=256 /> |
| <img src="images/crazy insane stunt_20_-3.0.png" width=256 height=256 /> | <img src="images/crazy insane stunt_20_0.0.png" width=256 height=256 /> | <img src="images/crazy insane stunt_20_3.0.png" width=256 height=256 /> |
## Download
Weights for this model are available in Safetensors format.
## Trigger words
You can apply this LoRA with trigger words for additional effect:
```
crazy insane stunt
```
## Use in diffusers
```python
from diffusers import StableDiffusionXLPipeline
from diffusers import EulerAncestralDiscreteScheduler
import torch
pipe = StableDiffusionXLPipeline.from_single_file("https://huggingface.co/martyn/sdxl-turbo-mario-merge-top-rated/blob/main/topRatedTurboxlLCM_v10.safetensors")
pipe.to("cuda")
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
# Load the LoRA
pipe.load_lora_weights('ntc-ai/SDXL-LoRA-slider.crazy-insane-stunt', weight_name='crazy insane stunt.safetensors', adapter_name="crazy insane stunt")
# Activate the LoRA
pipe.set_adapters(["crazy insane stunt"], adapter_weights=[2.0])
prompt = "medieval rich kingpin sitting in a tavern, crazy insane stunt"
negative_prompt = "nsfw"
width = 512
height = 512
num_inference_steps = 10
guidance_scale = 2
image = pipe(prompt, negative_prompt=negative_prompt, width=width, height=height, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps).images[0]
image.save('result.png')
```
## Support the Patreon
If you like this model please consider [joining our Patreon](https://www.patreon.com/NTCAI).
By joining our Patreon, you'll gain access to an ever-growing library of over 690+ unique and diverse LoRAs, covering a wide range of styles and genres. You'll also receive early access to new models and updates, exclusive behind-the-scenes content, and the powerful LoRA slider creator, allowing you to craft your own custom LoRAs and experiment with endless possibilities.
Your support on Patreon will allow us to continue developing and refining new models.
## Other resources
- [CivitAI](https://civitai.com/user/ntc) - Follow ntc on Civit for even more LoRAs
- [ntcai.xyz](https://ntcai.xyz) - See ntcai.xyz to find more articles and LoRAs
|
yuchuantian/AIGC_detector_zhv2 | yuchuantian | 2023-12-28T16:52:16Z | 23 | 4 | transformers | [
"transformers",
"pytorch",
"bert",
"text-classification",
"arxiv:2305.18149",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2023-12-26T15:31:44Z | ---
license: apache-2.0
---
# AIGC Detector (MPU)
[[arXiv]](https://arxiv.org/abs/2305.18149) [[Codes (Model Links, Other Detector Versions)]](https://github.com/YuchuanTian/AIGC_text_detector)
The AIGC Detector (MPU) in our paper "Multiscale Positive-Unlabeled Detection of AI-Generated Texts".
Paper Link: https://arxiv.org/pdf/2305.18149.pdf
*BibTex* formatted citation:
```
@misc{tian2023multiscale,
title={Multiscale Positive-Unlabeled Detection of AI-Generated Texts},
author={Yuchuan Tian and Hanting Chen and Xutao Wang and Zheyuan Bai and Qinghua Zhang and Ruifeng Li and Chao Xu and Yunhe Wang},
year={2023},
eprint={2305.18149},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
## Version: Zh_v2
Zh_v2: This model is trained with MPU from a pretrained Chinese-RoBERTa-wwm-ext, but the standard training setting in the paper is not followed. This model demonstrates comparable performance to SOTA closed-source Chinese detectors on various domains. |
tb2pi-persistent/Llama-2-13b-chat-hf-tb2pi-peft-v2 | tb2pi-persistent | 2023-12-28T16:44:37Z | 1 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-2-13b-chat-hf",
"base_model:adapter:meta-llama/Llama-2-13b-chat-hf",
"region:us"
] | null | 2023-12-28T16:44:26Z | ---
library_name: peft
base_model: meta-llama/Llama-2-13b-chat-hf
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.7.1 |
dechantoine/ppo-Huggy | dechantoine | 2023-12-28T16:41:19Z | 5 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] | reinforcement-learning | 2023-12-28T16:41:14Z | ---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: dechantoine/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
master-frog/distilgpt2-finetuned-wikitext2 | master-frog | 2023-12-28T16:33:14Z | 6 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:distilbert/distilgpt2",
"base_model:finetune:distilbert/distilgpt2",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2023-12-28T16:06:29Z | ---
license: apache-2.0
base_model: distilgpt2
tags:
- generated_from_trainer
model-index:
- name: distilgpt2-finetuned-wikitext2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilgpt2-finetuned-wikitext2
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.6434
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.7501 | 1.0 | 2334 | 3.6669 |
| 3.6498 | 2.0 | 4668 | 3.6464 |
| 3.5868 | 3.0 | 7002 | 3.6434 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0
|
paisanx/Reinforce-Cartpole-V1 | paisanx | 2023-12-28T16:27:46Z | 0 | 0 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | reinforcement-learning | 2023-12-28T16:27:42Z | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Cartpole-V1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 145.80 +/- 6.84
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
Jackman4399/Reinforce-v1 | Jackman4399 | 2023-12-28T16:26:09Z | 0 | 0 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | reinforcement-learning | 2023-12-28T16:26:01Z | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
divysoni111/ppo-LunarLander-v2 | divysoni111 | 2023-12-28T16:20:51Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2023-12-28T16:20:30Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 241.41 +/- 24.82
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
CyberHarem/kobayashi_kobayashisanchinomaidragon | CyberHarem | 2023-12-28T16:17:53Z | 0 | 1 | null | [
"art",
"text-to-image",
"dataset:CyberHarem/kobayashi_kobayashisanchinomaidragon",
"license:mit",
"region:us"
] | text-to-image | 2023-12-28T16:02:40Z | ---
license: mit
datasets:
- CyberHarem/kobayashi_kobayashisanchinomaidragon
pipeline_tag: text-to-image
tags:
- art
---
# Lora of kobayashi_kobayashisanchinomaidragon
This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11).
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 14280, you need to download `14280/kobayashi_kobayashisanchinomaidragon.pt` as the embedding and `14280/kobayashi_kobayashisanchinomaidragon.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The best step we recommend is 14280**, with the score of 0.920. The trigger words are:
1. `kobayashi_kobayashisanchinomaidragon`
2. `glasses, red_hair, blush, ponytail, bangs, closed_mouth, brown_eyes`
For the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
These are available steps:
| Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | pattern_5 | pattern_6 | pattern_7 | pattern_8 | pattern_9 | pattern_10 | pattern_11 | pattern_12 | pattern_13 | pattern_14 | pattern_15 | pattern_16 | pattern_17 | pattern_18 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata |
|:----------|:----------|:---------------------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:---------------------------------------------------|:---------------------------------------------------|:---------------------------------------------------|:---------------------------------------------------|:---------------------------------------------------|:---------------------------------------------------|:---------------------------------------------------|:---------------------------------------------------|:---------------------------------------------------|:-------------------------------------------|:---------------------------------------------------|:---------------------------------------|:---------------------------------------|:---------------------------------------|:------------------------------------------------|:-------------------------------------------------|:---------------------------------------|:-------------------------------------------|
| 15300 | 0.901 | [Download](15300/kobayashi_kobayashisanchinomaidragon.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](15300/previews/bondage.png) |  |  |  | [<NSFW, click to see>](15300/previews/nude.png) | [<NSFW, click to see>](15300/previews/nude2.png) |  |  |
| **14280** | **0.920** | [**Download**](14280/kobayashi_kobayashisanchinomaidragon.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](14280/previews/bondage.png) |  |  |  | [<NSFW, click to see>](14280/previews/nude.png) | [<NSFW, click to see>](14280/previews/nude2.png) |  |  |
| 13260 | 0.909 | [Download](13260/kobayashi_kobayashisanchinomaidragon.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](13260/previews/bondage.png) |  |  |  | [<NSFW, click to see>](13260/previews/nude.png) | [<NSFW, click to see>](13260/previews/nude2.png) |  |  |
| 12240 | 0.857 | [Download](12240/kobayashi_kobayashisanchinomaidragon.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](12240/previews/bondage.png) |  |  |  | [<NSFW, click to see>](12240/previews/nude.png) | [<NSFW, click to see>](12240/previews/nude2.png) |  |  |
| 11220 | 0.917 | [Download](11220/kobayashi_kobayashisanchinomaidragon.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](11220/previews/bondage.png) |  |  |  | [<NSFW, click to see>](11220/previews/nude.png) | [<NSFW, click to see>](11220/previews/nude2.png) |  |  |
| 10200 | 0.910 | [Download](10200/kobayashi_kobayashisanchinomaidragon.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](10200/previews/bondage.png) |  |  |  | [<NSFW, click to see>](10200/previews/nude.png) | [<NSFW, click to see>](10200/previews/nude2.png) |  |  |
| 9180 | 0.900 | [Download](9180/kobayashi_kobayashisanchinomaidragon.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](9180/previews/bondage.png) |  |  |  | [<NSFW, click to see>](9180/previews/nude.png) | [<NSFW, click to see>](9180/previews/nude2.png) |  |  |
| 8160 | 0.882 | [Download](8160/kobayashi_kobayashisanchinomaidragon.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](8160/previews/bondage.png) |  |  |  | [<NSFW, click to see>](8160/previews/nude.png) | [<NSFW, click to see>](8160/previews/nude2.png) |  |  |
| 7140 | 0.894 | [Download](7140/kobayashi_kobayashisanchinomaidragon.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](7140/previews/bondage.png) |  |  |  | [<NSFW, click to see>](7140/previews/nude.png) | [<NSFW, click to see>](7140/previews/nude2.png) |  |  |
| 6120 | 0.919 | [Download](6120/kobayashi_kobayashisanchinomaidragon.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](6120/previews/bondage.png) |  |  |  | [<NSFW, click to see>](6120/previews/nude.png) | [<NSFW, click to see>](6120/previews/nude2.png) |  |  |
| 5100 | 0.870 | [Download](5100/kobayashi_kobayashisanchinomaidragon.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](5100/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5100/previews/nude.png) | [<NSFW, click to see>](5100/previews/nude2.png) |  |  |
| 4080 | 0.864 | [Download](4080/kobayashi_kobayashisanchinomaidragon.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](4080/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4080/previews/nude.png) | [<NSFW, click to see>](4080/previews/nude2.png) |  |  |
| 3060 | 0.868 | [Download](3060/kobayashi_kobayashisanchinomaidragon.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](3060/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3060/previews/nude.png) | [<NSFW, click to see>](3060/previews/nude2.png) |  |  |
| 2040 | 0.779 | [Download](2040/kobayashi_kobayashisanchinomaidragon.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](2040/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2040/previews/nude.png) | [<NSFW, click to see>](2040/previews/nude2.png) |  |  |
| 1020 | 0.655 | [Download](1020/kobayashi_kobayashisanchinomaidragon.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](1020/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1020/previews/nude.png) | [<NSFW, click to see>](1020/previews/nude2.png) |  |  |
|
vladmandic/animatediff-v3 | vladmandic | 2023-12-28T16:14:46Z | 32 | 5 | diffusers | [
"diffusers",
"license:apache-2.0",
"region:us"
] | null | 2023-12-28T16:07:44Z | ---
license: apache-2.0
---
Copy of <https://huggingface.co/guoyww/animatediff/blob/main/v3_sd15_mm.ckpt> in Huggingface Diffusers format so it can be loaded directly using MotionAdapter.from_pretrained |
angiiangi/berte | angiiangi | 2023-12-28T16:05:56Z | 0 | 0 | null | [
"license:other",
"region:us"
] | null | 2023-12-28T16:04:28Z | ---
license: other
license_name: berte
license_link: LICENSE
---
|
Johnlhugface/a2c-PandaReachDense-v3 | Johnlhugface | 2023-12-28T15:54:56Z | 1 | 0 | stable-baselines3 | [
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2023-12-28T15:50:46Z | ---
library_name: stable-baselines3
tags:
- PandaReachDense-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v3
type: PandaReachDense-v3
metrics:
- type: mean_reward
value: -1.04 +/- 2.16
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
LoneStriker/Aurora-Nights-70B-v1.0-5.0bpw-h6-exl2 | LoneStriker | 2023-12-28T15:50:01Z | 6 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"en",
"arxiv:2307.11760",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2023-12-28T15:30:39Z | ---
license: llama2
language:
- en
---
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/aGUU0O9.png" alt="AuroraNights" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
### Overview
This model is a blend of [allenai/tulu-2-dpo-70b](https://huggingface.co/allenai/tulu-2-dpo-70b), [Xwin-LM/Xwin-LM-70B-V0.1](https://huggingface.co/Xwin-LM/Xwin-LM-70B-V0.1), and [dreamgen/opus-v0.5-70b](https://huggingface.co/dreamgen/opus-v0.5-70b). I then merged [nRuaif/fiction.live-Kimiko-V2-70B](https://huggingface.co/nRuaif/fiction.live-Kimiko-V2-70B) into the resultant blend. See the bottom of this card for the exact settings used.
This model is good at both following instructions and producing creative, uncensored storytelling and roleplaying content.
This model turned out quite uncensored. *You are responsible for whatever you do with it.*
This model was designed for roleplaying and storytelling and I think it does well at both. It *should* perform well at other tasks, but I haven't tested its capabilities in other areas.
### Sampler Tips
I recommend using the new Min-P sampler method with this model. The creator has a great [guide to it on Reddit](https://www.reddit.com/r/LocalLLaMA/comments/17vonjo/your_settings_are_probably_hurting_your_model_why/).
I find this model performs surprisingly well at 8192 context but you will probably get better results at 4096 context.
Experiment with any and all of the settings below, but trust me on a few points:
* This model works well with Min-P.
* Frequency Penalty set to 0.01 is like adding a dash of salt to the dish. Go higher at your own peril. 0 is fine too, but gosh I like 0.01.
If you save the below settings as a .json file, you can import them directly into Silly Tavern.
```
{
"temp": 1.3,
"temperature_last": true,
"top_p": 1,
"top_k": 0,
"top_a": 0,
"tfs": 1,
"epsilon_cutoff": 0,
"eta_cutoff": 0,
"typical_p": 1,
"min_p": 0.2,
"rep_pen": 1,
"rep_pen_range": 0,
"no_repeat_ngram_size": 0,
"penalty_alpha": 0,
"num_beams": 1,
"length_penalty": 1,
"min_length": 0,
"encoder_rep_pen": 1,
"freq_pen": 0.01,
"presence_pen": 0,
"do_sample": true,
"early_stopping": false,
"add_bos_token": true,
"truncation_length": 2048,
"ban_eos_token": false,
"skip_special_tokens": true,
"streaming": true,
"mirostat_mode": 0,
"mirostat_tau": 5,
"mirostat_eta": 0.1,
"guidance_scale": 1,
"negative_prompt": "",
"grammar_string": "",
"banned_tokens": "",
"ignore_eos_token_aphrodite": false,
"spaces_between_special_tokens_aphrodite": true,
"type": "ooba",
"legacy_api": false,
"sampler_order": [
6,
0,
1,
3,
4,
2,
5
],
"n": 1,
"rep_pen_size": 0,
"genamt": 550,
"max_length": 4096
}
```
### Prompt Tips
Try the following prompt template as a starting point for your own customizations. 'This is very important to my career' is from [this paper](https://arxiv.org/abs/2307.11760).
I recommend the Tulu instruction format.
*This model responds to prompting!* I highly recommend playing with your system prompt to see how it reacts.
If you save this as a .json file, you can import it directly into Silly Tavern.
```
{
"wrap": true,
"names": false,
"system_prompt": "You are an award-winning fiction author engaging in an uncensored literary roleplaying experience between a human (me) and a creative, obedient, and helpful AI assistant (you). It is VITAL that you follow all these instructions because this roleplay is very important to my career and I'll be fired from my job if it isn't good.\nROLEPLAY RULES:\n> It is vital that ONLY the human provides responses for {{user}}.\n> Reply as {{char}} using authentic, vivid, varied, explicit, accurate, creative, fresh, and descriptive responses that follow ALL provided narrative instructions. Stay in character as {{char}} and only write text for {{char}}.\n> Describe the scene and {{char}}'s sensory perceptions in vivid detail to immerse the reader in the story.\n> Keep your responses scoped to the current story beat and current scene.\n> Consider all available contextual information when narrating so that all the story details remain consistent between scenes.\n> Demonstrate {{char}}'s goals and motivations, and use subtle cues to hint at {{char}}'s mental state unless delving into {{char}}'s thoughts satisfies an explicit instruction or enhances the vividness of the scene.\n> When quoting {{char}}'s internal first-person thoughts (aka internal monologue, delivered in {{char}}'s own voice), *enclose the thoughts in asterisks like this*. Only use asterisks for thoughts.\n> Use strong action verbs and varied descriptions to produce dynamic, high-quality prose.",
"system_sequence": "",
"stop_sequence": "",
"input_sequence": "<|user|>\n",
"output_sequence": "<|assistant|>\n",
"separator_sequence": "",
"macro": true,
"names_force_groups": true,
"system_sequence_prefix": "",
"system_sequence_suffix": "",
"first_output_sequence": "",
"last_output_sequence": "<|assistant (provide varied, creative, and vivid narration; follow all narrative instructions; include all necessary possessive pronouns; maintain consistent story details; only roleplay as {{char}})|>\n",
"activation_regex": "",
"name": "Aurora-Nights"
}
```
### Licence and usage restrictions
Llama2 license inherited from base models, plus restrictions applicable to [Dreamgen/Opus](https://huggingface.co/dreamgen/opus-v0.5-70b).
### Tools Used
* [mergekit](https://github.com/cg123/mergekit)
```
models:
- model: NousResearch_Llama-2-70b-hf
# no parameters necessary for base model
- model: allenai_tulu-2-dpo-70b # primary
parameters:
density: 1.0
weight: 0.4
- model: Xwin-LM_Xwin-LM-70B-V0.1 # secondary
parameters:
density: 0.7
weight: 0.3
- model: dreamgen_opus-v0.5-70b # supporting, good at storytelling and roleplay
parameters:
density: 0.2
weight: 0.6
merge_method: dare_ties
base_model: NousResearch_Llama-2-70b-hf
parameters:
normalize: true
int8_mask: true
dtype: float32
``` |
imagepipeline/Photo-Paint-Slider | imagepipeline | 2023-12-28T15:39:18Z | 0 | 0 | null | [
"imagepipeline",
"imagepipeline.io",
"text-to-image",
"ultra-realistic",
"license:creativeml-openrail-m",
"region:us"
] | text-to-image | 2023-12-28T15:39:16Z | ---
license: creativeml-openrail-m
tags:
- imagepipeline
- imagepipeline.io
- text-to-image
- ultra-realistic
pinned: false
pipeline_tag: text-to-image
---
## Photo-Paint-Slider
<img src="https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/2c243520-afff-410f-837f-32a92660bcca/original=true/xyz_grid-0048-20230819192050UnstableinkdreamV7.5-balance-fp16-no-ema158594f6b8DPM++%202M%20Karras15.jpeg" alt="Generated by Image Pipeline" style="border-radius: 10px;">
**This lora model is uploaded on [imagepipeline.io](https://imagepipeline.io/)**
Model details - V2.0
After understanding the LECO training method, we will adjust the parameters and the learning rate in order to achieve better results with smaller files and fewer weights.
The available weights are from +2 to -2, and the recommended weights are from +2 to -1.0, with positive values providing animation tendency and negative values providing real tendency.
The performance of positive weight (animation) is better than negative weight (real).
[](https://imagepipeline.io/models/Photo-Paint-Slider?id=8d995698-3aa1-4695-aca9-3d176401079b/)
## How to try this model ?
You can try using it locally or send an API call to test the output quality.
Get your `API_KEY` from [imagepipeline.io](https://imagepipeline.io/). No payment required.
Coding in `php` `javascript` `node` etc ? Checkout our documentation
[](https://docs.imagepipeline.io/docs/introduction)
```python
import requests
import json
url = "https://imagepipeline.io/sd/text2image/v1/run"
payload = json.dumps({
"model_id": "sd1.5",
"prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K",
"negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime",
"width": "512",
"height": "512",
"samples": "1",
"num_inference_steps": "30",
"safety_checker": false,
"guidance_scale": 7.5,
"multi_lingual": "no",
"embeddings": "",
"lora_models": "8d995698-3aa1-4695-aca9-3d176401079b",
"lora_weights": "0.5"
})
headers = {
'Content-Type': 'application/json',
'API-Key': 'your_api_key'
}
response = requests.request("POST", url, headers=headers, data=payload)
print(response.text)
}
```
Get more ready to use `MODELS` like this for `SD 1.5` and `SDXL` :
[](https://imagepipeline.io/models)
### API Reference
#### Generate Image
```http
https://api.imagepipeline.io/sd/text2image/v1
```
| Headers | Type | Description |
|:----------------------| :------- |:-------------------------------------------------------------------------------------------------------------------|
| `API-Key` | `str` | Get your `API_KEY` from [imagepipeline.io](https://imagepipeline.io/) |
| `Content-Type` | `str` | application/json - content type of the request body |
| Parameter | Type | Description |
| :-------- | :------- | :------------------------- |
| `model_id` | `str` | Your base model, find available lists in [models page](https://imagepipeline.io/models) or upload your own|
| `prompt` | `str` | Text Prompt. Check our [Prompt Guide](https://docs.imagepipeline.io/docs/SD-1.5/docs/extras/prompt-guide) for tips |
| `num_inference_steps` | `int [1-50]` | Noise is removed with each step, resulting in a higher-quality image over time. Ideal value 30-50 (without LCM) |
| `guidance_scale` | `float [1-20]` | Higher guidance scale prioritizes text prompt relevance but sacrifices image quality. Ideal value 7.5-12.5 |
| `lora_models` | `str, array` | Pass the model_id(s) of LoRA models that can be found in models page |
| `lora_weights` | `str, array` | Strength of the LoRA effect |
---
license: creativeml-openrail-m
tags:
- imagepipeline
- imagepipeline.io
- text-to-image
- ultra-realistic
pinned: false
pipeline_tag: text-to-image
---
### Feedback
If you have any feedback, please reach out to us at [email protected]
#### 🔗 Visit Website
[](https://imagepipeline.io/)
If you are the original author of this model, please [click here](https://airtable.com/apprTaRnJbDJ8ufOx/shr4g7o9B6fWfOlUR) to add credits
|
khoantap/mythical-wizard-rp | khoantap | 2023-12-28T15:30:39Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2023-09-08T07:07:39Z | A merge of https://huggingface.co/Sao10K/Mythical-Destroyer-V2-L2-13B and https://huggingface.co/lemonilia/limarp-llama2-v2 to solve bad roleplay performance.
Credit: Sao10k + Lemonilia |
itsdhanoob/lunar_lander | itsdhanoob | 2023-12-28T15:23:28Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2023-12-28T15:23:07Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO(Proximal Policy Optimization)
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 272.11 +/- 24.54
name: mean_reward
verified: false
---
# **PPO(Proximal Policy Optimization)** Agent playing **LunarLander-v2**
This is a trained model of a **PPO(Proximal Policy Optimization)** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
platzi/platzi-distilroberta-bases-mrpc-nicolas-gomez | platzi | 2023-12-28T15:21:18Z | 5 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilroberta-base",
"base_model:finetune:distilbert/distilroberta-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2023-12-27T17:44:29Z | ---
license: apache-2.0
base_model: distilroberta-base
tags:
- text-classification
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: platzi-distilroberta-bases-mrpc-nicolas-gomez
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# platzi-distilroberta-bases-mrpc-nicolas-gomez
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the glue and the mrpc datasets.
It achieves the following results on the evaluation set:
- Loss: 0.4302
- Accuracy: 0.8480
- F1: 0.8916
widget:
- text: ["Yucaipa owned Dominick 's before selling the chain to Safeway in 1998 for $ 2.5 billion.",
"Yucaipa bought Dominick's in 1995 for $ 693 million and sold it to Safeway for $ 1.8 billion in 1998."]
example_title: Not Equivalent
- text: ["Revenue in the first quarter of the year dropped 15 percent from the same period a year earlier.",
"With the scandal hanging over Stewart's company revenue the first quarter of the year dropped 15 percent from the same period a year earlier."]
example_title: Equivalent
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.5097 | 1.09 | 500 | 0.4302 | 0.8480 | 0.8916 |
| 0.3324 | 2.18 | 1000 | 0.7114 | 0.8407 | 0.8889 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0
|
jondurbin/bagel-14b | jondurbin | 2023-12-28T15:20:40Z | 17 | 2 | transformers | [
"transformers",
"safetensors",
"qwen",
"text-generation",
"custom_code",
"dataset:ai2_arc",
"dataset:unalignment/spicy-3.1",
"dataset:codeparrot/apps",
"dataset:facebook/belebele",
"dataset:boolq",
"dataset:jondurbin/cinematika-v0.1",
"dataset:drop",
"dataset:lmsys/lmsys-chat-1m",
"dataset:TIGER-Lab/MathInstruct",
"dataset:cais/mmlu",
"dataset:Muennighoff/natural-instructions",
"dataset:openbookqa",
"dataset:piqa",
"dataset:Vezora/Tested-22k-Python-Alpaca",
"dataset:cakiki/rosetta-code",
"dataset:Open-Orca/SlimOrca",
"dataset:spider",
"dataset:squad_v2",
"dataset:migtissera/Synthia-v1.3",
"dataset:datasets/winogrande",
"dataset:nvidia/HelpSteer",
"dataset:Intel/orca_dpo_pairs",
"dataset:unalignment/toxic-dpo-v0.1",
"dataset:jondurbin/truthy-dpo-v0.1",
"dataset:allenai/ultrafeedback_binarized_cleaned",
"dataset:Squish42/bluemoon-fandom-1-1-rp-cleaned",
"dataset:LDJnr/Capybara",
"dataset:JULIELab/EmoBank",
"dataset:kingbri/PIPPA-shareGPT",
"license:other",
"autotrain_compatible",
"region:us"
] | text-generation | 2023-12-27T08:38:38Z | ---
license: other
license_name: qwen
license_link: https://huggingface.co/Qwen/Qwen-14B/blob/main/LICENSE
datasets:
- ai2_arc
- unalignment/spicy-3.1
- codeparrot/apps
- facebook/belebele
- boolq
- jondurbin/cinematika-v0.1
- drop
- lmsys/lmsys-chat-1m
- TIGER-Lab/MathInstruct
- cais/mmlu
- Muennighoff/natural-instructions
- openbookqa
- piqa
- Vezora/Tested-22k-Python-Alpaca
- cakiki/rosetta-code
- Open-Orca/SlimOrca
- spider
- squad_v2
- migtissera/Synthia-v1.3
- datasets/winogrande
- nvidia/HelpSteer
- Intel/orca_dpo_pairs
- unalignment/toxic-dpo-v0.1
- jondurbin/truthy-dpo-v0.1
- allenai/ultrafeedback_binarized_cleaned
- Squish42/bluemoon-fandom-1-1-rp-cleaned
- LDJnr/Capybara
- JULIELab/EmoBank
- kingbri/PIPPA-shareGPT
---
# A bagel, with everything
## Overview
An experimental fine-tune of qwen-14b using [bagel](https://github.com/jondurbin/bagel)
The resulting model didn't turn out quite as great as I would have liked - in fact, I'd probably use the [mistral-7b](https://huggingface.co/jondurbin/bagel-dpo-7b-v0.1) version over this, because it scored higher on mt-bench, is much faster, and generally is uncensored in comparison to this model (even after toxic DPO, several epochs)
I modified the qwen tokenizer to use `<s>` instead of `<|im_start|>` and `</s>` instead of `<|endoftext|>`, and it may have caused some issues but I'm not entirely sure.
Hardware kindly provided by [Massed Compute](https://massedcompute.com)
## Data selection.
The first step in the process is creating a dataset.
In this case, we're actually creating a composite dataset, consisting of both supervised fine-tuning data (SFT) and direct preference optimization (DPO) data.
All instruction data, that is, data that is not plain text (like project Gutenberg and items from Cinematika) or DPO, is converted into ShareGPT format so it's easier to work with.
See the corresponding code in `bagel/data_sources/*.py` for full implementation for each data source.
Deduplication is done by creating a uuid v5 of the instruction/text, then only adding items not previously seen (where datasets are loaded in order of the confidence score I assign them).
This means that if an instruction is in data source "Foo" with confidence 4 as well as in data source "Bar" with confidence score 2, only the entry from "Foo" will be taken.
### SFT data sources
*Yes, you will see benchmark names in the list, but this only uses the train splits, and a decontamination by cosine similarity is performed at the end as a sanity check*
- [ai2_arc](https://huggingface.co/datasets/ai2_arc)
- Abstraction and reasoning dataset, useful in measuring "intelligence" to a certain extent.
- [airoboros](https://huggingface.co/datasets/unalignment/spicy-3.1)
- Variety of categories of synthetic instructions generated by gpt-4.
- [apps](https://huggingface.co/datasets/codeparrot/apps)
- Python coding dataset with 10k problems.
- [belebele](https://huggingface.co/datasets/facebook/belebele)
- Multi-lingual reading comprehension dataset.
- [bluemoon](https://huggingface.co/datasets/Squish42/bluemoon-fandom-1-1-rp-cleaned)
- Roleplay data scraped from Bluemoon, then cleaned and formatted as ShareGPT.
- [boolq](https://huggingface.co/datasets/boolq)
- Corpus of yes/no questions (which can be surprisingly difficult for AI to answer apparently?)
- [capybara](https://huggingface.co/datasets/LDJnr/Capybara)
- Multi-turn dataset used to create the capybara models.
- [cinematika](https://huggingface.co/datasets/jondurbin/cinematika-v0.1) (instruction and plain text)
- RP-style data synthesized from movie scripts so the model isn't quite as boring as it otherwise would be.
- [drop](https://huggingface.co/datasets/drop)
- More reading comprehension.
- [emobank](https://github.com/JULIELab/EmoBank)
- Emotion annotations using the Valence-Arousal-Domninance scheme.
- [gutenberg](https://www.gutenberg.org/) (plain text)
- Books/plain text, again to make the model less boring, only a handful of examples supported by [chapterize](https://github.com/JonathanReeve/chapterize)
- [lmsys_chat_1m](https://huggingface.co/datasets/lmsys/lmsys-chat-1m) (only gpt-4 items, also used for DPO)
- Chats collected by the lmsys chat arena, containing a wide variety of chats with various models.
- [mathinstruct](https://huggingface.co/datasets/TIGER-Lab/MathInstruct)
- Composite dataset with a variety of math-related tasks and problem/question formats.
- [mmlu](https://huggingface.co/datasets/cais/mmlu)
- Massive Multitask Language Understanding - a wide variety of questions about various subject matters.
- [natural_instructions](https://huggingface.co/datasets/Muennighoff/natural-instructions)
- Millions of instructions from 1600+ task categories (sampled down substantially, stratified by task type)
- [openbookqa](https://huggingface.co/datasets/openbookqa)
- Question answering dataset.
- [pippa](https://huggingface.co/datasets/kingbri/PIPPA-shareGPT)
- Deduped version of [PIPPA](https://huggingface.co/datasets/PygmalionAI/PIPPA) in ShareGPT format.
- [piqa](https://huggingface.co/datasets/piqa)
- Phyiscal interaction question answering.
- [python_alpaca](https://huggingface.co/datasets/Vezora/Tested-22k-Python-Alpaca)
- Python instruction response pairs, validated as functional.
- [rosetta_code](https://huggingface.co/datasets/cakiki/rosetta-code)
- Code problems and solutions in a variety of programming languages taken from rosettacode.org.
- [slimorca](https://huggingface.co/datasets/Open-Orca/SlimOrca)
- Collection of ~500k gpt-4 verified chats from OpenOrca.
- [spider](https://huggingface.co/datasets/spider)
- SQL-targeted dataset.
- [squad_v2](https://huggingface.co/datasets/squad_v2)
- Contextual question answering (RAG).
- [synthia](https://huggingface.co/datasets/migtissera/Synthia-v1.3)
- GPT-4 generated data using advanced prompting from Migel Tissera.
- [winogrande](https://huggingface.co/datasets/winogrande)
- Fill in the blank style prompts.
### DPO data sources
- [airoboros 3.1](https://huggingface.co/datasets/unalignment/spicy-3.1) vs [airoboros 2.2.1](https://huggingface.co/datasets/jondurbin/airoboros-gpt4-1.4.1)
- The creative/writing tasks from airoboros-2.2.1 were re-generated using gpt4-0314 and a custom prompt to get longer, more creative, less clichè responses for airoboros 3.1, so we can use the shorter/boring version as the "rejected" value and the rerolled response as "chosen"
- [helpsteer](https://huggingface.co/datasets/nvidia/HelpSteer)
- Really neat dataset provided by the folks at NVidia with human annotation across a variety of metrics. Only items with the highest "correctness" value were used for DPO here, with the highest scoring output as "chosen" and random lower scoring value as "rejected"
- [orca_dpo_pairs](https://huggingface.co/datasets/Intel/orca_dpo_pairs)
- Another interesting dataset by Intel, which provides various DPO pairs generated from prompts included in the SlimOrca dataset.
- [toxic-dpo](https://huggingface.co/datasets/unalignment/toxic-dpo-v0.1)
- __*highly toxic and potentially illegal content!*__ De-censorship, for academic and lawful purposes only, of course. Generated by llama-2-70b via prompt engineering.
- [truthy](https://huggingface.co/datasets/jondurbin/truthy-dpo-v0.1)
- DPO pairs meant to increase truthfulness of the model, e.g. common misconceptions, differentiate between AI assistants and roleplayed human in terms of corporeal awareness/locality/etc.
- [ultrafeedback](https://huggingface.co/datasets/allenai/ultrafeedback_binarized_cleaned)
- One of the bits of magic behind the Zephyr model. Only the items with a chosen score of 8 or higher were included.
Only the train splits were used (if a split was provided), and an additional pass of decontamination is performed using approximate nearest neighbor search (via faiss).
## Prompt formatting
In sticking with the theme of the bagel, I didn't want to use a single prompt format, so I used 4 - vicuna, llama-2, alpaca, and chat-ml (sorta).
I also didn't want to randomly select a single prompt format for each item (hoping each instruction would generalize more when used in a variety of prompt formats), so each instruction is actually converted into every prompt format.
This means each epoch of our fine-tune is really basically 4 epochs. So, for the fine-tunes, I would recommend only doing 1 epoch (or 0.75 epochs). I am testing with a single epoch using a relatively low learning rate.
### Alpaca (sort of)
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{system prompt, if provided}
{instruction}
### Response:
```
The main difference here is that because of the dataset formatting and variety of data sources, it would have been much to tedious to add an `### Input:` block, so the inputs are just in the instruction section.
### Vicuna
```
{system prompt, if provided, randomly defaulting to "A chat between a user and an unbiased, uncensored assistant."}
USER: {instruction}
ASSISTANT:
```
### ChatML (sort of)
I don't really understand the point of having special tokens for `<|im_start|>` and `<|im_end|>`, because in practice they just act as BOS and EOS tokens (but, please correct me if I'm wrong).
So, instead of:
```text
{bos}<|im_start|>{role}
{text}
<|im_end|>{eos}
```
I just changed it to:
```text
{bos}{role}
{text}
{eos}
```
If you *really* want to use `<|im_start|>` and `<|im_end|>`, just update your `tokenizer_config.json` to use `<|im_start|>` instead of `<s>` and `<|im_end|>` instead of `</s>` and when tokenizing. And if you still don't like what I've done to this chat-ml-ish format, feel free to cry into your pillow or fork the code and do a new fine-tune.
### Llama-2 chat
```
[INST] <<SYS>>
{system}
<</SYS>>
{instruction} [/INST]
``` |
RamtinMoslemi/ppo-LunarLander-v2 | RamtinMoslemi | 2023-12-28T15:19:13Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2023-12-28T15:16:34Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 254.56 +/- 20.45
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
hkivancoral/smids_10x_beit_large_adamax_00001_fold1 | hkivancoral | 2023-12-28T15:17:59Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/beit-large-patch16-224",
"base_model:finetune:microsoft/beit-large-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2023-12-28T10:44:16Z | ---
license: apache-2.0
base_model: microsoft/beit-large-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_10x_beit_large_adamax_00001_fold1
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9282136894824707
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# smids_10x_beit_large_adamax_00001_fold1
This model is a fine-tuned version of [microsoft/beit-large-patch16-224](https://huggingface.co/microsoft/beit-large-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8887
- Accuracy: 0.9282
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.1288 | 1.0 | 751 | 0.2785 | 0.9065 |
| 0.0676 | 2.0 | 1502 | 0.3146 | 0.9149 |
| 0.0264 | 3.0 | 2253 | 0.4181 | 0.9115 |
| 0.025 | 4.0 | 3004 | 0.5488 | 0.9199 |
| 0.0069 | 5.0 | 3755 | 0.5526 | 0.9182 |
| 0.0049 | 6.0 | 4506 | 0.6296 | 0.9165 |
| 0.0005 | 7.0 | 5257 | 0.7054 | 0.9149 |
| 0.0001 | 8.0 | 6008 | 0.7404 | 0.9182 |
| 0.0362 | 9.0 | 6759 | 0.7520 | 0.9132 |
| 0.0001 | 10.0 | 7510 | 0.8011 | 0.9149 |
| 0.0001 | 11.0 | 8261 | 0.7591 | 0.9199 |
| 0.0002 | 12.0 | 9012 | 0.7216 | 0.9215 |
| 0.0024 | 13.0 | 9763 | 0.8101 | 0.9132 |
| 0.0 | 14.0 | 10514 | 0.8382 | 0.9249 |
| 0.0 | 15.0 | 11265 | 0.8571 | 0.9165 |
| 0.0 | 16.0 | 12016 | 0.8307 | 0.9249 |
| 0.0002 | 17.0 | 12767 | 0.8135 | 0.9098 |
| 0.0 | 18.0 | 13518 | 0.9070 | 0.9132 |
| 0.0 | 19.0 | 14269 | 0.8650 | 0.9115 |
| 0.0 | 20.0 | 15020 | 0.8297 | 0.9265 |
| 0.0 | 21.0 | 15771 | 0.8359 | 0.9282 |
| 0.0 | 22.0 | 16522 | 0.8827 | 0.9265 |
| 0.0 | 23.0 | 17273 | 0.8484 | 0.9215 |
| 0.0 | 24.0 | 18024 | 0.8739 | 0.9182 |
| 0.0004 | 25.0 | 18775 | 0.8728 | 0.9232 |
| 0.0 | 26.0 | 19526 | 0.8742 | 0.9149 |
| 0.0 | 27.0 | 20277 | 0.9029 | 0.9199 |
| 0.0 | 28.0 | 21028 | 0.8812 | 0.9232 |
| 0.0109 | 29.0 | 21779 | 0.9326 | 0.9215 |
| 0.0 | 30.0 | 22530 | 0.9197 | 0.9115 |
| 0.0001 | 31.0 | 23281 | 0.8910 | 0.9215 |
| 0.0 | 32.0 | 24032 | 0.8659 | 0.9215 |
| 0.0 | 33.0 | 24783 | 0.8759 | 0.9232 |
| 0.0 | 34.0 | 25534 | 0.9176 | 0.9199 |
| 0.0 | 35.0 | 26285 | 0.8674 | 0.9249 |
| 0.0 | 36.0 | 27036 | 0.8364 | 0.9249 |
| 0.0 | 37.0 | 27787 | 0.8518 | 0.9265 |
| 0.0 | 38.0 | 28538 | 0.8614 | 0.9232 |
| 0.0 | 39.0 | 29289 | 0.8789 | 0.9215 |
| 0.0 | 40.0 | 30040 | 0.8979 | 0.9215 |
| 0.0 | 41.0 | 30791 | 0.9262 | 0.9199 |
| 0.0107 | 42.0 | 31542 | 0.8969 | 0.9232 |
| 0.0 | 43.0 | 32293 | 0.9021 | 0.9265 |
| 0.0 | 44.0 | 33044 | 0.8921 | 0.9282 |
| 0.0 | 45.0 | 33795 | 0.9002 | 0.9249 |
| 0.0007 | 46.0 | 34546 | 0.9147 | 0.9199 |
| 0.0 | 47.0 | 35297 | 0.8904 | 0.9249 |
| 0.0 | 48.0 | 36048 | 0.8842 | 0.9282 |
| 0.0 | 49.0 | 36799 | 0.8899 | 0.9265 |
| 0.0 | 50.0 | 37550 | 0.8887 | 0.9282 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
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