youjunhyeok
/

llama3-koen-8b-sft-v1

@@ -2,199 +2,158 @@
 library_name: transformers
 tags:
 - llama-factory
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 library_name: transformers
 tags:
 - llama-factory
+license: apache-2.0
 ---
+## Model
+- base model: [beomi/Llama-3-KoEn-8B-Instruct-preview](https://huggingface.co/beomi/Llama-3-KoEn-8B-Instruct-preview)
+## Dataset
+- [youjunhyeok/llama3_train](https://huggingface.co/datasets/youjunhyeok/llama3_train)
+## Load Model
+Use the following Python code to load the model:
+```python3
+from transformers import AutoTokenizer, AutoModelForCausalLM
+path = 'youjunhyeok/llama3-8b-ko-sft-v1'
+model = AutoModelForCausalLM.from_pretrained(path)
+tokenizer = AutoTokenizer.from_pretrained(path)
+model.to('cuda')
+```
+## Chat
+```python3
+def chat(message):
+    messages = [
+        {"role": "system", "content": "당신은 인공지능 어시스턴트입니다. 친절하고 정확한 답변을 해주세요."},
+        {"role": "user", "content": message},
+    ]
+    input_ids = tokenizer.apply_chat_template(
+        messages,
+        add_generation_prompt=True,
+        return_tensors="pt"
+    ).to(model.device)
+    terminators = [
+        tokenizer.eos_token_id,
+        tokenizer.convert_tokens_to_ids("<|eot_id|>")
+    ]
+    outputs = model.generate(
+        input_ids,
+        max_new_tokens=512,
+        eos_token_id=terminators,
+        do_sample=True,
+        temperature=0.9,
+        top_p=0.95,
+    )
+    response = outputs[0][input_ids.shape[-1]:]
+    print(tokenizer.decode(response, skip_special_tokens=True))
+chat('ETL 파이프라인을 어떻게 구현해야할까?')
+```
+## Output
+```
+ETL(데이터 추출, 변환 및 로드) 파이프라인을 구현하려면 먼저 데이터 소스, 목표, 중간 결과물과 같은 ETL 파이프라인의 각 단계에 대한 명확한 이해와 구현 계획이 필요합니다. 다음은 일반적인 ETL 파이프라인 구현 프로세스입니다:
+1. 데이터 소스 이해: ETL 파이프라인의 첫 번째 단계는 데이터 소스를 결정하는 것입니다. ETL 파이프라인에 사용할 데이터 소스 유형을 식별하고 데이터 소스에서 추출할 데이터에 대한 구체적인 계획을 세웁니다.
+2. ETL 도구 선택: ETL을 구현하는 데 사용할 도구를 결정합니다. ETL 프로세스의 특정 요구 사항에 따라 다양한 도구를 사용할 수 있습니다. 인기 있는 도구로는 Oracle의 Informatica, Microsoft의 Power BI, 데이터웨어하우스 툴인 Talend 등이 있습니다.
+3. 추출 계획 수립: 데이터 소스에서 데이터를 추출할 계획을 세웁니다. 이 단계에서는 데이터 소스에서 필요로 하는 데이터 유형, 구조 및 형식을 파악합니다. 추출 프로세스에 대한 로드맵을 만들고, 데이터 변환 및 로드에 대한 필요성을 식별하는 것이 중요합니다.
+4. 변환 계획 수립: 데이터를 변환하기 위해 실행할 프로세스를 결정합니다. 데이터 포맷을 변환하고, 데이터 유형을 변환하며, 필요한 경우 데이터 값을 정규화하거나 결합합니다.
+5. 데이터 로드 계획 수립: 변환된 데이터를 데이터베이스나 기타 목적지에 로드할 계획을 세웁니다. 이 단계에서는 데이터베이스 디자인, 저장 프로시저 또는 파이프라인 구성을 결정합니다.
+6. ETL 파이프라인 구현: ETL 도구를 사용하여 ETL 파이프라인을 구현합니다. 소스, 변환, 로드 단계를 순서대로 처리하여 필요한 데이터 변환을 수행합니다.
+7. 데이터 품질 관리: ETL 파이프라인이 올바르게 작동하고 데이터 품질이 유지되도록 테스트하고 모니터링합니다. 데이터 오류 및 누락 항목을 검출하고 데이터 품질을 유지하기 위한 프로세스를 구현합니다.
+```
+## BenchMark (KOR)
+```
+# alias
+A = youjunhyeok/llama3-koen-8b-sft-v1
+B = DavidAhn/Llama-3-8B-slerp-262k
+C = meta-llama/Meta-Llama-3-8B
+D = chihoonlee10/T3Q-ko-solar-dpo-v7.0 (24.05.24 ko 리더보드 1등)
+```
+| Benchmark (macro_f1)      |   A  |   B  |   C  |   D  |
+|---------------------------|:----:|:----:|:----:|:----:|
+| kobest_boolq (0-shot)     | 78.1 | 33.5 | 38.2 | 34.1 |
+| kobest_boolq (5-shot)     | 85.0 | 68.8 | 83.8 | 93.1 |
+| kobest_copa (0-shot)      | 80.4 | 58.5 | 63.1 | 81.0 |
+| kobest_copa (5-shot)      | 84.0 | 61.7 | 69.1 | 91.0 |
+| kobest_hellaswag (0-shot) | 51.7 | 43.2 | 42.1 | 55.1 |
+| kobest_hellaswag (5-shot) | 51.7 | 45.3 | 44.2 | 55.2 |
+| kobest_sentineg (0-shot)  | 81.5 | 34.8 | 51.5 | 82.7 |
+| kobest_sentineg (5-shot)  | 97.7 | 85.8 | 94.7 | 91.4 |
+## BenchMark (ENG)
+```
+# alias
+A = youjunhyeok/llama3-koen-8b-sft-v1
+B = DavidAhn/Llama-3-8B-slerp-262k
+C = meta-llama/Meta-Llama-3-8B
+```
+|               |     A |     B |     C |
+|:--------------|------:|------:|------:|
+| openbookqa    | 0.310 | 0.312 | 0.338 |
+| hellaswag     | 0.544 | 0.587 | 0.576 |
+| boolq         | 0.807 | 0.832 | 0.831 |
+| arc_easy      | 0.753 | 0.808 | 0.815 |
+| arc_challenge | 0.421 | 0.518 | 0.529 |
+## Llama_factory Train Config
+{data_dir}, {dataset_name}, {output_dir} is variable
+```
+cutoff_len: 1024
+dataset: {dataset_name}
+dataset_dir: {data_dir}
+ddp_timeout: 180000000
+do_train: true
+eval_steps: 500
+eval_strategy: steps
+finetuning_type: lora
+flash_attn: auto
+fp16: true
+gradient_accumulation_steps: 8
+include_num_input_tokens_seen: true
+learning_rate: 5.0e-05
+logging_steps: 5
+lora_alpha: 16
+lora_dropout: 0.05
+lora_rank: 16
+lora_target: all
+lr_scheduler_type: cosine
+max_grad_norm: 1.0
+max_samples: 300000
+model_name_or_path: beomi/Llama-3-KoEn-8B-Instruct-preview
+num_train_epochs: 1.0
+optim: adamw_torch
+output_dir: {output_dir}
+packing: false
+per_device_eval_batch_size: 16
+per_device_train_batch_size: 16
+plot_loss: true
+preprocessing_num_workers: 16
+report_to: all
+save_steps: 1000
+stage: sft
+template: llama3
+val_size: 0.01
+warmup_steps: 1000
+```