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  library_name: transformers
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- tags: []
 
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  ---
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-
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- # Model Card for Model ID
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-
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- <!-- Provide a quick summary of what the model is/does. -->
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-
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- ## Model Details
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-
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- ### Model Description
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-
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- <!-- Provide a longer summary of what this model is. -->
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- This is the model card of a ๐Ÿค— transformers model that has been pushed on the Hub. This model card has been automatically generated.
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-
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- - **Developed by:** [More Information Needed]
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- - **Funded by [optional]:** [More Information Needed]
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- - **Shared by [optional]:** [More Information Needed]
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- - **Model type:** [More Information Needed]
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- - **Language(s) (NLP):** [More Information Needed]
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- - **License:** [More Information Needed]
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- - **Finetuned from model [optional]:** [More Information Needed]
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-
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- ### Model Sources [optional]
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- <!-- Provide the basic links for the model. -->
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- - **Repository:** [More Information Needed]
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- - **Paper [optional]:** [More Information Needed]
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- - **Demo [optional]:** [More Information Needed]
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-
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- ## Uses
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-
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- <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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- ### Direct Use
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- <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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- [More Information Needed]
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- ### Downstream Use [optional]
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- <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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- [More Information Needed]
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- ### Out-of-Scope Use
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- <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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- [More Information Needed]
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- ## Bias, Risks, and Limitations
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- <!-- This section is meant to convey both technical and sociotechnical limitations. -->
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- [More Information Needed]
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- ### Recommendations
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- <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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- Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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- ## How to Get Started with the Model
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- Use the code below to get started with the model.
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- [More Information Needed]
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- ## Training Details
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-
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- ### Training Data
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- <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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- [More Information Needed]
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- ### Training Procedure
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- <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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- #### Preprocessing [optional]
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- [More Information Needed]
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- #### Training Hyperparameters
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- - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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- #### Speeds, Sizes, Times [optional]
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- <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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- [More Information Needed]
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- ## Evaluation
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- <!-- This section describes the evaluation protocols and provides the results. -->
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- ### Testing Data, Factors & Metrics
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- #### Testing Data
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- <!-- This should link to a Dataset Card if possible. -->
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- [More Information Needed]
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- #### Factors
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- <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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- [More Information Needed]
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- #### Metrics
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- <!-- These are the evaluation metrics being used, ideally with a description of why. -->
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- [More Information Needed]
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- ### Results
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- [More Information Needed]
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- #### Summary
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- ## Model Examination [optional]
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- <!-- Relevant interpretability work for the model goes here -->
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- [More Information Needed]
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- ## Environmental Impact
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- <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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- Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- - **Hardware Type:** [More Information Needed]
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- - **Hours used:** [More Information Needed]
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- - **Cloud Provider:** [More Information Needed]
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- - **Compute Region:** [More Information Needed]
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- - **Carbon Emitted:** [More Information Needed]
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- ## Technical Specifications [optional]
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- ### Model Architecture and Objective
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- [More Information Needed]
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- ### Compute Infrastructure
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- [More Information Needed]
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- #### Hardware
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- [More Information Needed]
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- #### Software
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- [More Information Needed]
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- ## Citation [optional]
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- <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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- **BibTeX:**
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- [More Information Needed]
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- **APA:**
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- [More Information Needed]
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- ## Glossary [optional]
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- <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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- [More Information Needed]
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- ## More Information [optional]
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- [More Information Needed]
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- ## Model Card Authors [optional]
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- [More Information Needed]
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- ## Model Card Contact
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- [More Information Needed]
 
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  ---
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+ license: apache-2.0
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+ language:
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+ - ko
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+ base_model:
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+ - meta-llama/Llama-3.2-1B-Instruct
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+ pipeline_tag: text-generation
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  library_name: transformers
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+ datasets:
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+ - nayohan/CodeFeedback-Filtered-Instruction-ko
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  ---
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+ - base_model : [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct)
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+ - data_set : [nayohan/CodeFeedback-Filtered-Instruction-ko](https://huggingface.co/datasets/nayohan/CodeFeedback-Filtered-Instruction-ko)
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+ - ํ•ด๋‹น ๋ฐ์ดํ„ฐ์…‹์„ ์ „๋ถ€ ์‚ฌ์šฉํ•œ๊ฑด ์•„๋‹ˆ๋ฉฐ Python์–ธ์–ด๋ฅผ ์šฐ์„  ์ถ”์ถœํ•œ๋‹ค์Œ ๋ฐ์ดํ„ฐ์…‹๋“ค์˜ ์ƒ๊น€์ƒˆ๋ฅผ ํŒŒ์•…, ๊ทธ ๋‹ค์Œ ์ „์ฒ˜๋ฆฌ๊ฐ€ ๊ณตํ†ต์ ์œผ๋กœ ๋“ค์–ด๊ฐˆ๋งŒํ•œ ๋ฐ์ดํ„ฐ๋ฅผ ๋‹ค์‹œ ์ถ”์ถœํ•˜์—ฌ ํ•™์Šต์— ์‚ฌ์šฉํ–ˆ์Šต๋‹ˆ๋‹ค.
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+ - ์ด ํ•™์Šต ๋ฐ์ดํ„ฐ ๊ฑด : 49,859 ๊ฑด
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+
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+ ```python
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+ from transformers import AutoTokenizer, AutoModelForCausalLM
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+
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+ model_id = 'MDDDDR/llama3.2-1B-Instruct-FFT-code-python'
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+ tokenizer = AutoTokenizer.from_pretrained(model_id)
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+ model = AutoModelForCausalLM.from_pretrained(model_id,
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+ device_map="cuda:0",
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+ torch_dtype=torch.bfloat16)
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+
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+
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+ instruction = '''LCS(Longest Common Subsequence, ์ตœ์žฅ ๊ณตํ†ต ๋ถ€๋ถ„ ์ˆ˜์—ด)๋ฌธ์ œ๋Š” ๋‘ ์ˆ˜์—ด์ด ์ฃผ์–ด์กŒ์„ ๋•Œ, ๋ชจ๋‘์˜ ๋ถ€๋ถ„ ์ˆ˜์—ด์ด ๋˜๋Š” ์ˆ˜์—ด ์ค‘ ๊ฐ€์žฅ ๊ธด ๊ฒƒ์„ ์ฐพ๋Š” ๋ฌธ์ œ์ด๋‹ค.
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+
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+ ์˜ˆ๋ฅผ ๋“ค์–ด, ACAYKP์™€ CAPCAK์˜ LCS๋Š” ACAK๊ฐ€ ๋œ๋‹ค.
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+
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+ ###์ž…๋ ฅ : ์ฒซ์งธ ์ค„๊ณผ ๋‘˜์งธ ์ค„์— ๋‘ ๋ฌธ์ž์—ด์ด ์ฃผ์–ด์ง„๋‹ค. ๋ฌธ์ž์—ด์€ ์•ŒํŒŒ๋ฒณ ๋Œ€๋ฌธ์ž๋กœ๋งŒ ์ด๋ฃจ์–ด์ ธ ์žˆ์œผ๋ฉฐ, ์ตœ๋Œ€ 1000๊ธ€์ž๋กœ ์ด๋ฃจ์–ด์ ธ ์žˆ๋‹ค.
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+ ###์ถœ๋ ฅ : ์ฒซ์งธ ์ค„์— ์ž…๋ ฅ์œผ๋กœ ์ฃผ์–ด์ง„ ๋‘ ๋ฌธ์ž์—ด์˜ LCS์˜ ๊ธธ์ด๋ฅผ ์ถœ๋ ฅํ•œ๋‹ค.
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+
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+ ###์ž…๋ ฅ ์˜ˆ์ œ :
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+ ACAYKP
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+ CAPCAK
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+ ###์ถœ๋ ฅ ์˜ˆ์ œ : 4
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+ '''
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+
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+ messages = [
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+ {
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+ "role":"user",
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+ "content":"์•„๋ž˜๋Š” ๋ฌธ์ œ๋ฅผ ์„ค๋ช…ํ•˜๋Š” ์ง€์‹œ์‚ฌํ•ญ์ž…๋‹ˆ๋‹ค. ์ด ์š”์ฒญ์— ๋Œ€ํ•ด ์ ์ ˆํ•˜๊ฒŒ ๋‹ต๋ณ€ํ•ด์ฃผ์„ธ์š”.\n###์ง€์‹œ์‚ฌํ•ญ:{instruction}\n###๋‹ต๋ณ€:".format(instruction=instruction)
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+ }
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+ ]
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+
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+ with torch.no_grad():
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+ prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=False)
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+ inputs = tokenizer(prompt, return_tensors="pt", padding=False).to('cuda')
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+ outputs = model.generate(**inputs,
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+ use_cache=False,
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+ max_length=256,
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+ top_p=0.9,
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+ temperature=0.7,
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+ repetition_penalty=1.0,
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+ pad_token_id=tokenizer.pad_token_id)
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+
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+ output_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
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+ final_output = output_text.split('assistant')[-1].strip()
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+ print(final_output)
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+ # ###๋‹ต๋ณ€:```python
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+ # def longest_common_subsequence(str1, str2):
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+ # m = len(str1)
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+ # n = len(str2)
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+ # dp = [[0] * (n+1) for _ in range(m+1)]
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+ #
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+ # for i in range(m+1):
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+ # for j in range(n+1):
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+ # if i == 0 or j == 0:
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+ # dp[i][j] = 0
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+ # elif str1[i-1] == str2[j-1]:
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+ # dp[i][j] = dp[i-1][j-1] + 1
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+ # else:
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+ # dp[i][j] = max(dp[i-1][j], dp[i][j-1])
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+ #
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+ # return dp[m][n]
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+ #
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+ # print(longest_common_subsequence("ACAYKP", "CAPCAK")) # Output: 4
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+ ```
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+ ```
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+
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+ Hardware
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+ - A100 40GB x 1
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+ - Training Time : 1 hour 45 minutes