git clone https://github.com/microsoft/fara.git
cd fara

python3 -m venv .venv
source .venv/bin/activate
pip install -e .[vllm]
playwright install

vllm serve "microsoft/Fara-7B" --port 5000 --dtype auto

fara-cli --task "whats the weather in new york now"

# Установим библиотеку трансформеров
!pip install transformers

# Импортируем библиотеки
from transformers import AutoModelForSeq2SeqLM, T5TokenizerFast

# Зададим название выбронной модели из хаба
MODEL_NAME = 'UrukHan/t5-russian-summarization'
MAX_INPUT = 256

# Загрузка модели и токенизатора
tokenizer = T5TokenizerFast.from_pretrained(MODEL_NAME)
model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_NAME)

# Входные данные (можно массив фраз или текст)
input_sequences = ['Тут твой текст']   # или можно использовать одиночные фразы:  input_sequences = 'сегодня хороший день'

task_prefix = "Spell correct: "                 # Токенизирование данных
if type(input_sequences) != list: input_sequences = [input_sequences]
encoded = tokenizer(
  [task_prefix + sequence for sequence in input_sequences],
  padding="longest",
  max_length=MAX_INPUT,
  truncation=True,
  return_tensors="pt",
)

predicts = model.generate(encoded)    # # Прогнозирование

tokenizer.batch_decode(predicts, skip_special_tokens=True)  # Декодируем данные

[SUBJECT]=Тема или навык для изучения
[CURRENT_LEVEL]=Начальный уровень знаний (начальный/средний/продвинутый)
[TIME_AVAILABLE]=Сколько часов в неделю готовы уделять обучению
[LEARNING_STYLE]=Предпочтительный метод обучения (визуальный/слуховой/практический/чтение)
[GOAL]=Конкретная цель обучения или целевой уровень навыка

[SUBJECT]=Topic or skill to learn
[CURRENT_LEVEL]=Starting knowledge level (beginner/intermediate/advanced)
[TIME_AVAILABLE]=Weekly hours available for learning
[LEARNING_STYLE]=Preferred learning method (visual/auditory/hands-on/reading)
[GOAL]=Specific learning objective or target skill level

Step 1: Knowledge Assessment
1. Break down [SUBJECT] into core components
2. Evaluate complexity levels of each component
3. Map prerequisites and dependencies
4. Identify foundational concepts
Output detailed skill tree and learning hierarchy

~ Step 2: Learning Path Design
1. Create progression milestones based on [CURRENT_LEVEL]
2. Structure topics in optimal learning sequence
3. Estimate time requirements per topic
4. Align with [TIME_AVAILABLE] constraints
Output structured learning roadmap with timeframes

~ Step 3: Resource Curation
1. Identify learning materials matching [LEARNING_STYLE]:
- Video courses
- Books/articles
- Interactive exercises
- Practice projects
2. Rank resources by effectiveness
3. Create resource playlist
Output comprehensive resource list with priority order

~ Step 4: Practice Framework
1. Design exercises for each topic
2. Create real-world application scenarios
3. Develop progress checkpoints
4. Structure review intervals
Output practice plan with spaced repetition schedule

~ Step 5: Progress Tracking System
1. Define measurable progress indicators
2. Create assessment criteria
3. Design feedback loops
4. Establish milestone completion metrics
Output progress tracking template and benchmarks

~ Step 6: Study Schedule Generation
1. Break down learning into daily/weekly tasks
2. Incorporate rest and review periods
3. Add checkpoint assessments
4. Balance theory and practice
Output detailed study schedule aligned with [TIME_AVAILABLE]

Отвечай на русском языке.