EO Community | Crypto, Airdrop and NFT
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EO Community | Crypto, Airdrop and NFT
buat yang bingung tutor task ke 2 ane mau buatin tutornya
Claim faucet dulu : https://www.sfuelstation.com/claim-sfuel/0x82300ecfcB92999D4382CB172ca3419d9fc7Fee6?testnet=true
Lalu mulai step2nya
1. Install dulu visualcode, python, dan pip di pc kalian
https://code.visualstudio.com/ https://www.python.org/downloads/
https://pip.pypa.io/en/stable/installation/
Kalo pusing cara installnya silahkan cari tutor di yt
2. Setelah python dan pip terinstall buka notepad kalian, lalu buat file pertama dengan isi private key wallet kalian dan namakan filenya private_key.txt
3. lalu buat file kedua di notepad lalu isi dengan public key atau address 0x wallet testnet kalian dan namakan filenya dengan nama public_key.txt
4. buka notepad lagi dan buat file ketiga dengan isi (codingan akan dishare dibawah), simpan file dengan nama run_model.py
5. jika semua file sudah dibuat maka cari di file manager kalian letak file run_model.py ini contohnya : C:\Users\NamaAnda\Projects
6. jika semua step diatas sudah dijalankan maka buka CMD kalian dan beri perintah :
cd C:\Users\NamaAnda\Projects
python run_model.py


Note : Gunakan New Wallet

Jika codingan masih error, install Microsoft Visual C++
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import time
from ModelMarketSDK.ModelMarket import Mixtral8x7BModelMarketTestnet

# Membaca private key dan public key dari file teks
with open('private_key.txt', 'r') as file:
PRIVATE_KEY = file.read().strip()

with open('public_key.txt', 'r') as file:
PUBLIC_KEY = file.read().strip()

model_market = Mixtral8x7BModelMarketTestnet(PRIVATE_KEY, PUBLIC_KEY)

# Fungsi untuk mengirim permintaan dan mendapatkan respons
def get_response():
chat = [{"role": "system", "content": "You are a helpful assistant!"}, {"role":"user", "content": "What is 2+2?"}]

# the generate_self_requesting method takes the same input
# but does not return the response but rather the url of the node that is processing
# the request and the result_code (unique code for each request)
node_url, result_code = model_market.generate_self_requesting(3000, chat)

full_resp = ""
done = False
while not done:
# the get_next_output function takes the url, the result_code and what has been
# generated so far and returns the next piece of content as a str and a bool
# indicating whether the AI generation is done
resp, done = model_market.get_next_output(node_url, result_code, full_resp)
full_resp += resp
print(resp, end="")
time.sleep(0.1)
print("\n") # Tambahkan baris baru setelah setiap respons

# Mengulang proses sebanyak 20 kali
for i in range(20):
print(f"Iteration {i + 1}:")
get_response()
codingan untuk task 2 ini udah diubah ya jadi cukup pakai sekali di cmd udah otomatis 20x pertanyaan dan 400 point msk
Forwarded from Time Farm News
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