youjunhyeok
/

llama3-8b-ko-sft-v1

@@ -1,199 +1,162 @@
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 ---
 library_name: transformers
+tags:
+- llama-factory
+license: apache-2.0
 ---
+## Model
+- base model: [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct)
+## 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)
+```
+## Chat
+```python3
+def chat(message):
+    messages = [
+        {"role": "system", "content": "당신은 인공지능 어시트턴트입니다. 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=0.6,
+        top_p=0.9,
+    )
+    response = outputs[0][input_ids.shape[-1]:]
+    print(tokenizer.decode(response, skip_special_tokens=True))
+chat('근로자가 부당해고를 당했을 때 구제받기 위한 절차와 요건은 무엇인가?')
+```
+## Output
+```
+근로자가 부당해고를 당했을 때 구제받기 위한 절차와 요건은 다음과 같습니다:
+1. 근로자: 근로자는 부당해고를 당한 경우, 근로기준법 등 관련 법률에 따라 구제를 받을 수 있습니다.
+2. 부당해고: 부당해고란, 근로자에게 적법한 절차를 거치지 않고 해고된 경우를 말합니다. 근로기준법 등 관련 법률에 따라 부당해고의 기준과 구제절차가 정해져 있습니다.
+3. 구제절차: 근로자가 부당해고를 당한 경우, 다음과 같은 구제절차를 거칠 수 있습니다:
+   - 근로기준법 제28조에 따라 근로자에게 부당해고를 당한 경우, 근로기준법상 부당해고를 받은 근로자는 근로감독관에게 신고할 수 있습니다. 근로감독관은 근로자에게 구제를 제공하고, 근로자에게 부당해고를 당한 경우에 대한 적법한 절차를 수행합니다.
+   - 근로기준법 제30조에 따라 근로자는 근로감독관에게 구제를 요청할 수 있습니다. 근로감독관은 근로자의 요청에 따라 구제를 제공하고, 근로자에게 부당해고를 당한 경우에 대한 적법한 절차를 수행합니다.
+4. 요건: 근로자가 구제를 받기 위해서는 다음과 같은 요건을 충족해야 합니다:
+   - 근로자: 근로자는 근로기준법 등 관련 법률에 따라 근로자로 정의되는 경우에 구제를 받을 수 있습니다.
+   - 부당해고: 근로자는 부당해고를 당한 경우에 구제를 받을 수 있습니다. 부당해고란, 근로기준법 등 관련 법률에 따라 정의된 경우에 해당합니다.
+   - 구제절차: 근로자는 구제절차를 거쳐야 합니다. 구제절차는 근로기준법 등 관련 법률에 따라 정의된 경우에 해당합니다.
+요약하면, 근로자가 부당해고를 당했을 때 구제받기 위해서는 근로기준법 등 관련 법률에 따라 구제절차를 거치고, 근로
+```
+## BenchMark (KOR)
+```
+# alias
+A = youjunhyeok/llama3-8b-ko-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)     | 84.7 | 33.5 | 38.2 | 34.1 |
+| kobest_boolq (5-shot)     | 85.4 | 68.8 | 83.8 | 93.1 |
+| kobest_copa (0-shot)      | 60.6 | 58.5 | 63.1 | 81.0 |
+| kobest_copa (5-shot)      | 67.2 | 61.7 | 69.1 | 91.0 |
+| kobest_hellaswag (0-shot) | 40.0 | 43.2 | 42.1 | 55.1 |
+| kobest_hellaswag (5-shot) | 42.4 | 45.3 | 44.2 | 55.2 |
+| kobest_sentineg (0-shot)  | 52.1 | 34.8 | 51.5 | 82.7 |
+| kobest_sentineg (5-shot)  | 89.4 | 85.8 | 94.7 | 91.4 |
+## BenchMark (ENG)
+```
+# alias
+A = youjunhyeok/llama3-8b-ko-sft-v1
+B = DavidAhn/Llama-3-8B-slerp-262k
+C = meta-llama/Meta-Llama-3-8B
+```
+|               |     A |     B |     C |
+|:--------------|------:|------:|------:|
+| openbookqa    | 0.342 | 0.312 | 0.338 |
+| hellaswag     | 0.555 | 0.587 | 0.576 |
+| boolq         | 0.824 | 0.832 | 0.831 |
+| arc_easy      | 0.758 | 0.808 | 0.815 |
+| arc_challenge | 0.464 | 0.518 | 0.529 |
+## Llama_factory Train Command
+{data_dir}, {dataset_name}, {output_dir} is variable
+```
+llamafactory-cli train \
+    --stage sft \
+    --do_train True \
+    --model_name_or_path meta-llama/Meta-Llama-3-8B-Instruct \
+    --preprocessing_num_workers 16 \
+    --finetuning_type lora \
+    --template llama3 \
+    --flash_attn auto \
+    --dataset_dir {data_dir} \
+    --dataset {dataset_name} \
+    --cutoff_len 1024 \
+    --learning_rate 5e-05 \
+    --num_train_epochs 2.0 \
+    --max_samples 400000 \
+    --per_device_train_batch_size 8 \
+    --gradient_accumulation_steps 8 \
+    --lr_scheduler_type cosine \
+    --max_grad_norm 1.0 \
+    --logging_steps 50 \
+    --save_steps 1000 \
+    --warmup_steps 1000 \
+    --optim adamw_torch \
+    --resize_vocab True \
+    --packing False \
+    --report_to none \
+    --output_dir {output_dir} \
+    --fp16 True \
+    --plot_loss True \
+    --ddp_timeout 180000000 \
+    --include_num_input_tokens_seen True \
+    --lora_rank 8 \
+    --lora_alpha 16 \
+    --lora_dropout 0.05 \
+    --lora_target all
+```