Update README.md

README.md

@@ -1,199 +1,106 @@
 ---
 library_name: transformers
-tags: []
+license: mit
+datasets:
+- heegyu/open-korean-instructions
+language:
+- ko
+tags:
+- Llama-2-7b-hf
+- LoRA
 ---
 
-# Model Card for Model ID
+# Llama-2-7b-hf 모델을 이용한 LoRA 파인 튜닝
 
 <!-- Provide a quick summary of what the model is/does. -->
 
-
-
-## Model Details
-
-### Model Description
+## 파인 튜닝 개요
+  - 대상 모델 : meta-llama/Llama-2-7b-hf
+  - 튜닝 방법 : PEFT(Parameter Efficient Fine-Tuning) - LoRA(Low-Rank Adaptation of Large Language Models)
+  - 관련 논문 : https://arxiv.org/abs/2106.09685
+  - Llama2 모델을 한국어 챗봇 데이터(open korean instructions)의 일부를 이용하여 파인 튜닝한 모델
+  - 대규모 언어 모델을 A30 GPU*1 에서 파인 튜닝이 가능한지 테스트
 
 <!-- Provide a longer summary of what this model is. -->
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
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-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
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-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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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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-### 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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-## Bias, Risks, and Limitations
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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. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
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-## Training Details
-
-### Training Data
-
-<!-- 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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-### 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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-
-#### Training Hyperparameters
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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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-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
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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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-#### Metrics
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-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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-### Results
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-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
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-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-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).
-
-- **Hardware Type:** [More Information Needed]
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-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
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-### Compute Infrastructure
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-#### Hardware
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-#### Software
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-## Citation [optional]
-
-<!-- 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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-**APA:**
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-## Glossary [optional]
-
-<!-- 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 [optional]
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-## Model Card Authors [optional]
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-
-## Model Card Contact
+- **Developed by:** [Lee Seung Man of Seoul Cyber University]
+- **Language(s) (NLP):** [Korean]
+- **Finetuned from model :** [meta-llama/Llama-2-7b-hf]
+
+## 파인 튜닝 상세
+
+- 알파값을 16으로 설정
+```
+peft_config = LoraConfig(
+    lora_alpha=16,
+    lora_dropout=0.1,
+    r=64,
+    bias='none',
+    task_type='CAUSAL_LM'
+)
+```
+
+- Mixed precision 설정 : 4bit 정밀도 (bnb_4bit_use_double_quant로 중복 양자 설정)
+```
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_use_double_quant=True,
+    bnb_4bit_quant_type='nf4',
+    bnb_4bit_compute_dtype='float16',
+)
+```
+
+- 3 epoch 수행
+```
+training_args = TrainingArguments(
+    output_dir=fine_tuning_model_name,
+    num_train_epochs=3,
+    per_device_train_batch_size=4,
+    gradient_accumulation_steps=2,
+    gradient_checkpointing=True,
+    optim='paged_adamw_32bit',
+    logging_steps=5,
+    save_strategy='epoch',
+    learning_rate=2e-4,
+    weight_decay=0.001,
+    max_grad_norm=0.3,
+    warmup_ratio=0.03,
+    group_by_length=False,
+    lr_scheduler_type='cosine',
+    disable_tqdm=True,
+    report_to='wandb',
+    seed=42
+)
+```
+
+- 기본 Trainer대신 SFT트레이너 사용 (https://huggingface.co/docs/trl/sft_trainer)
+```
+trainer = SFTTrainer(
+    model=peft_model,
+    train_dataset=dataset,
+    dataset_text_field='text',
+    max_seq_length=min(tokenizer.model_max_length, 2048),
+    tokenizer=tokenizer,
+    packing=True,
+    args=training_args
+)
+```
+
+### 학습 결과
+
+```
+소요시간 : executed in 2d 0h 17m
+```
+
+```
+TrainOutput(global_step=2001,
+            training_loss=0.6940358212922347,
+            metrics={
+               'train_runtime': 173852.2333,
+               'train_samples_per_second': 0.092,
+               'train_steps_per_second': 0.012,
+               'train_loss': 0.6940358212922347,
+               'epoch': 3.0})
+
+```
 
-[More Information Needed]