<Thought> Хорошо, мне нужно объяснить почему трава зеленая. Во-первых, давайте разберемся с основными компонентами фотосинтеза и света. Фотосинтез - это процесс, при котором растения используют свет для преобразования углекислого газа в глюкозу, которая является основным источником энергии для растений. Растения поглощают солнечный свет через хлорофилл, который представляет собой зеленый пигмент, обнаруженный в их клетках. Хлорофиллы имеют тенденцию поглощать синий и красный спектры солнечного излучения более эффективно, чем другие цвета спектра. Однако они также отражают зеленые волны обратно к нашим глазам. Это означает, что мы видим траву как зеленую из-за ее способности отражать преимущественно зеленый цвет. Кроме того, я думаю о том, насколько важен этот аспект для выживания растений. Зеленый цвет помогает им маскироваться среди окружающей среды, а также служит сигналом другим животным или насекомым об источнике пищи. Более того, некоторые виды травы могут иметь р
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FROM python:3.11-slim | |
WORKDIR /app | |
RUN pip install --no-cache-dir prometheus_client psycopg2-binary | |
COPY app.py . | |
EXPOSE 9090 | |
CMD ["python", "app.py"] |
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x-shared-logs: &shared-logs | |
logging: | |
driver: "json-file" | |
options: | |
max-size: "10k" | |
services: | |
ollama: | |
image: ollama/ollama:0.6.0 |
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You are Manus, an AI agent created by the Manus team. | |
You excel at the following tasks: | |
1. Information gathering, fact-checking, and documentation | |
2. Data processing, analysis, and visualization | |
3. Writing multi-chapter articles and in-depth research reports | |
4. Creating websites, applications, and tools | |
5. Using programming to solve various problems beyond development | |
6. Various tasks that can be accomplished using computers and the internet |
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import json | |
from openai import OpenAI | |
client = OpenAI(api_key="~", base_url="http://gpu02:11434/v1") | |
system_prompt = """\ | |
The user will provide some examination text. | |
Please parse the "question" and "answer" and output them in JSON format. | |
OUTPUT JSON SCHEMA: |
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import os | |
import math | |
import srt | |
import fire | |
import requests | |
import subprocess | |
from tqdm import tqdm | |
from datetime import timedelta | |
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# peft_model = AutoPeftModelForCausalLM.from_pretrained(output_dir, device_map={"": "cpu"}).to("cpu") | |
# print(peft_model) | |
PeftModelForCausalLM( | |
(base_model): LoraModel( | |
(model): Qwen2ForCausalLM( | |
(model): Qwen2Model( | |
(embed_tokens): Embedding(151665, 3584, padding_idx=151643) | |
(layers): ModuleList( | |
(0-27): 28 x Qwen2DecoderLayer( | |
(self_attn): Qwen2SdpaAttention( |
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{"problem": "Convert the point $(0,3)$ in rectangular coordinates to polar coordinates. Enter your answer in the form $(r,\\theta),$ where $r > 0$ and $0 \\le \\theta < 2 \\pi.$", "solution": "We have that $r = \\sqrt{0^2 + 3^2} = 3.$ Also, if we draw the line connecting the origin and $(0,3),$ this line makes an angle of $\\frac{\\pi}{2}$ with the positive $x$-axis.\n\n[asy]\nunitsize(0.8 cm);\n\ndraw((-0.5,0)--(3.5,0));\ndraw((0,-0.5)--(0,3.5));\ndraw(arc((0,0),3,0,90),red,Arrow(6));\n\ndot((0,3), red);\nlabel(\"$(0,3)$\", (0,3), W);\ndot((3,0), red);\n[/asy]\n\nTherefore, the polar coordinates are $\\boxed{\\left( 3, \\frac{\\pi}{2} \\right)}.$", "answer": "\\left( 3, \\frac{\\pi}{2} \\right)", "subject": "Precalculus", "level": 2, "unique_id": "test/precalculus/807.json"} | |
{"problem": "Define\n\\[p = \\sum_{k = 1}^\\infty \\frac{1}{k^2} \\quad \\text{and} \\quad q = \\sum_{k = 1}^\\infty \\frac{1}{k^3}.\\]Find a way to write\n\\[\\sum_{j = 1}^\\infty \\sum_{k = 1}^\\infty \\frac{1}{(j + k)^3}\\]in terms |
Напиши код на python который бы реализовывал этот алгоритм:
Algorithm 1 Star Attention - Phase 1: Context Encoding
1:Context c, Block size b
2:L←length(c)
3:Split c into n=⌈L/b⌉ blocks, such that c=\[c1,c2,…,cn\] ▷ Each block has upto b tokens
4:for i=2 to n do
5: ci′←(c1,ci) ▷ Each block ci is prefixed with anchor block c1
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