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newline or spaceIf you're interested in building large-scale language models to solve a wide variety of problems in a wide variety of domains, you should consider joining Allganize. For a coffee chat or if you have any questions, please do not hesitate to contact me as well! - kuotient.dev@gmail.com
1<|im_start|>system
2주어진 문장을 한국어로 번역하세요.<|im_end|>
3<|im_start|>user
4{instruction}<|im_end|>
5<|im_start|>assistant
6# Don't miss newline here1<|im_start|>system
2주어진 문장을 영어로 번역하세요.<|im_end|>
3<|im_start|>user
4{instruction}<|im_end|>
5<|im_start|>assistant
6# Don't miss newline hereA particle's wave function, $\psi(x)$, is given by $$\psi(x)=\begin{cases} 3x & \text{if } -1 \leq x \leq 0 \ 3(1-x) & \text{if } 0 < x \leq 1 \ 0 & \text{otherwise} \end{cases}$$ Compute the Fourier transform, $\tilde{\psi}(k)$, of the wave function $\psi(x)$ and show that it satisfies the Fourier inversion theorem, i.e., $\psi(x) = \frac{1}{\sqrt{2\pi}} \int_{-\infty}^{\infty} \tilde{\psi}(k) e^{ikx} \mathrm{d}k$.
입자의 파동 함수 $\psi(x)$는 다음과 같이 주어집니다. $$\psi(x)=\begin{cases} 3x & \text{if } -1 \leq x \leq 0 \ 3(1-x) & \text{if } 0 < x \leq 1 \ 0 & \text{otherwise} \end{cases}$$ 파동 함수 $\psi(x)$의 푸리에 변환 $\tilde{\psi}(k)$를 계산하고 푸리에 반전 정리, 즉 $\psi(x) = \frac{1}{\sqrt{2\pi}} \int_{-\infty}^{\infty} \tilde{\psi}(k) e^{ikx} \mathrm{d}k$를 만족합니다.
입자의 파동 함수 $\psi(x)$는 $$\psi(x)=\begin{cases}로 주어집니다. 3x & \text{if } -1 \leq x \leq 0 \ 3(1-x) & \text{if } 0 < x \leq 1 \ 0 & \text{기타} \end{cases}$$ 파동 함수 $\psi(x)$의 푸리에 변환인 $\tilde{\psi}(k)$를 계산하고 푸리에 반전 정리, 즉 $\psi(x) = \frac{1}{\sqrt{2\pi}}를 만족함을 증명합니다. \int_{-\infty}^{\infty} \물결표{\psi}(k) e^{ikx} \mathrm{d}k$.
1from transformers import AutoModelForCausalLM, AutoTokenizer
2device = "cuda" # the device to load the model onto
3model = AutoModelForCausalLM.from_pretrained("kuotient/Seagull-13B-translation")
4tokenizer = AutoTokenizer.from_pretrained("kuotient/Seagull-13B-translation")
5messages = [
6 {"role": "system", "content", "주어진 문장을 한국어로 번역하세요."}
7 {"role": "user", "content": "Here are five examples of nutritious foods to serve your kids."},
8]
9encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")
10
11model_inputs = encodeds.to(device)
12model.to(device)
13generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True)
14decoded = tokenizer.batch_decode(generated_ids)
15print(decoded[0])