daekeun-ml
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phi-2-ko-v0.1

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 ---
 library_name: transformers
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 ---
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 ---
 library_name: transformers
+license: cc-by-sa-3.0
+datasets:
+- wikimedia/wikipedia
+- maywell/korean_textbooks
+- nampdn-ai/tiny-codes
+- Open-Orca/OpenOrca
+language:
+- ko
+- en
 ---
+# phi-2-ko-v0.1
 ## Model Details
+This model is a Korean-specific model trained in phi-2 by adding a Korean tokenizer and Korean data. (English is also available.)
+Although phi-2 performs very well, it does not support the Korean language and does not have a tokenizer trained on Korean corpous, so tokenizing Korean text will use many times more tokens than English tokens.
+To overcome these limitations, I trained the model using an open-license Korean corpus and some English corpus.
+The reasons for using the English corpus together are as follows:
+    1. The goal is to preserve the excellent performance of the existing model by preventing catastrophic forgetting.
+    2. Mixing English and Korean prompts usually produces better results than using all prompts in Korean.
+Since my role is not as a working developer, but as an solutions architect helping customers with quick PoCs/prototypes, and I was limited by AWS GPU resources available, I only trained with 5GB of data instead of hundreds of GB of massive data.
+### Continued pre-training
+The dataset used for training is as follows.
+- Wikipedia Korean dataset (https://huggingface.co/datasets/wikimedia/wikipedia)
+- Massive Korean synthetic dataset (https://huggingface.co/datasets/maywell/korean_textbooks)
+- Tiny code dataset (https://huggingface.co/datasets/nampdn-ai/tiny-codes)
+- OpenOrca dataset (https://huggingface.co/datasets/Open-Orca/OpenOrca)
+- Using some of the various sentences I wrote (personal blog, chat, etc.)
+Note that performance is not guaranteed since only a small number of datasets were used for the experiment. The number of samples for training set is just around 5 million after tokenization.
+For distributed training, all weights were trained without adapter techniques, and sharding parallelization was performed with ZeRO-2. The presets are as follows.
+Since this is a model that has not been fine-tuned, it is recommended to perform fine tuning such as instruction tuning/alignment tuning according to your use case.
+```json
+{
+    "fp16": {
+        "enabled": "auto",
+        "loss_scale": 0,
+        "loss_scale_window": 1000,
+        "initial_scale_power": 16,
+        "hysteresis": 2,
+        "min_loss_scale": 1
+    },
+    "bf16": {
+        "enabled": "auto"
+    },
+    "optimizer": {
+        "type": "AdamW",
+        "params": {
+            "lr": "auto",
+            "betas": "auto",
+            "eps": "auto",
+            "weight_decay": "auto"
+        }
+    },
+    "scheduler": {
+        "type": "WarmupLR",
+        "params": {
+            "warmup_min_lr": "auto",
+            "warmup_max_lr": "auto",
+            "warmup_num_steps": "auto"
+        }
+    },
+    "zero_optimization": {
+        "stage": 2,
+        "allgather_partitions": true,
+        "allgather_bucket_size": 2e8,
+        "overlap_comm": true,
+        "reduce_scatter": true,
+        "reduce_bucket_size": 2e8,
+        "contiguous_gradients": true,
+        "cpu_offload": true
+    },
+    "gradient_accumulation_steps": "auto",
+    "gradient_clipping": "auto",
+    "train_batch_size": "auto",
+    "train_micro_batch_size_per_gpu": "auto"
+}
+```
+Some hyperparameters are listed below.
+```
+batch_size: 2
+num_epochs: 1
+learning_rate: 3e-4
+gradient_accumulation_steps: 8
+lr_scheduler_type: "linear"
+group_by_length: False
+```
+### References
+- Base model: [microsoft/phi-2](https://huggingface.co/microsoft/phi-2)
+## Notes
+### License
+cc-by-sa 3.0; The license of phi-2 is MIT, but I considered the licensing of the dataset used for training.
+### Caution
+This model was created as a personal experiment, unrelated to the organization I work for. The model may not operate correctly because separate verification was not performed. Please be careful unless it is for personal experimentation or PoC (Proof of Concept)!