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# Model: Llama-3.1-70B-Instruct
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Model ini adalah model instruksi yang telah difinetuned dan dievaluasi untuk menghasilkan catatan, flashcards, dan kuis berdasarkan konten yang diberikan. Model ini menggunakan Hugging Face API dengan fine-grained token untuk mengakses model.
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## Cara Menggunakan Model
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### Python
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Berikut adalah contoh kode Python untuk menggunakan model ini:
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```python
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from openai import OpenAI
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from IPython.display import display, Markdown
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client = OpenAI(
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api_key="hf_xxxxxxxxxxxxxxx", # isi dengan token "FINE GRAINED" huggingface anda!
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base_url="https://huggingface.co/api/integrations/dgx/v1",
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)
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# System prompts - pastikan ini sudah didefinisikan
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notes_prompt = "..." // wajib isi dengan prompt notes yang sudah ditentukan (notes_prompt.txt)
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flashcards_prompt = "..." // wajib isi dengan prompt flashcards yang sudah ditentukan (flashcards_prompt.txt)
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quiz_prompt = "..." // wajib isi dengan prompt quiz yang sudah ditentukan (quiz_prompt.txt)
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content = "..." // isi dengan dokumen yang sudah di ekstrak (PDF, TXT, DOCX)
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# 1. Generate notes
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notes_response = client.chat.completions.create(
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model="meta-llama/Llama-3.1-70B-Instruct",
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messages=[
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{"role": "system", "content": notes_prompt},
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{"role": "user", "content": content},
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],
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temperature=0.3,
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max_tokens=10_000
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)
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# Notes response
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notes_md = notes_response.choices[0].message.content
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# 2. Generate flashcards
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flashcards_response = client.chat.completions.create(
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model="meta-llama/Llama-3.1-70B-Instruct",
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messages=[
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{"role": "system", "content": flashcards_prompt},
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{"role": "user", "content": notes_md},
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],
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temperature=0.3,
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max_tokens=1024
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)
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# flashcards response
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flashcards_md = flashcards_response.choices[0].message.content
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# 3. Generate quiz
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quiz_response = client.chat.completions.create(
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model="meta-llama/Llama-3.1-70B-Instruct",
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messages=[
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{"role": "system", "content": quiz_prompt},
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{"role": "user", "content": notes_md},
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],
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temperature=0.3,
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max_tokens=5048
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)
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# Quiz response
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quiz_md = quiz_response.choices[0].message.content
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print(notes_md)
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print(flashcards_md)
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print(quiz_md)
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```
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### Javascript
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```javascript
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import { HfInference } from "@huggingface/inference";
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const client = new HfInference("hf_xxxxxxxxxxxxxxx"); // isi dengan token "FINE GRAINED" huggingface anda!
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// System prompts - pastikan ini sudah didefinisikan
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const notes_prompt = "..." // wajib isi dengan prompt notes yang sudah ditentukan (notes_prompt.txt)
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const flashcards_prompt = "..." // wajib isi dengan prompt flashcards yang sudah ditentukan (flashcards_prompt.txt)
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const quiz_prompt = "..." // wajib isi dengan prompt quiz yang sudah ditentukan (quiz_prompt.txt)
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const content = "..." // isi dengan dokumen yang sudah di ekstrak (PDF, TXT, DOCX)
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async function generateEducationalContent() {
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try {
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// 1. Generate Notes
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const notesResponse = await client.chatCompletion({
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model: "meta-llama/Llama-3.1-70B-Instruct",
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messages: [
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{ role: "system", content: notes_prompt },
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{ role: "user", content: content }
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],
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temperature: 0.3,
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max_tokens: 10_000
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});
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const notesMd = notesResponse.choices[0].message.content;
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console.log("=== CATATAN ===");
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console.log(notesMd);
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// 2. Generate Flashcards
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const flashcardsResponse = await client.chatCompletion({
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model: "meta-llama/Llama-3.1-70B-Instruct",
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messages: [
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{ role: "system", content: flashcards_prompt },
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{ role: "user", content: notesMd }
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],
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temperature: 0.3,
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max_tokens: 1024
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});
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const flashcardsMd = flashcardsResponse.choices[0].message.content;
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console.log("\n=== FLASHCARDS ===");
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console.log(flashcardsMd);
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// 3. Generate Quiz
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const quizResponse = await client.chatCompletion({
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model: "meta-llama/Llama-3.1-70B-Instruct",
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messages: [
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{ role: "system", content: quiz_prompt },
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{ role: "user", content: notesMd }
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],
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temperature: 0.3,
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max_tokens: 5048
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});
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const quizMd = quizResponse.choices[0].message.content;
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console.log("\n=== KUIS ===");
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console.log(quizMd);
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} catch (error) {
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console.error("Error:", error);
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}
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}
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// Eksekusi fungsi utama
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generateEducationalContent();
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```
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Pastikan anda menginstall dependency yang diperlukan:
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```node.js
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npm install @huggingface/inference
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```
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