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Model: daekeun-ml/phi-2-ko-v0.1
Source: Original Platform

.gitattributes

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README.md

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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
+inference: false
+---
+
+# 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.
+
+### Vocab Expansion
+
+| Model Name | Vocabulary Size | Description | 
+| --- | --- | --- |
+| Original phi-2 | 50,295 | BBPE (Byte-level BPE) |
+| **phi-2-ko** | 66,676 | BBPE. Added Korean vocab and merges |
+
+**Tokenizing "아마존 세이지메이커"**
+
+| Model | # of tokens | Tokens |
+| --- | --- | --- |
+| Original phi-2 | 25 | `[168, 243, 226, 167, 100, 230, 168, 94, 112, 23821, 226, 116, 35975, 112, 168, 100, 222, 167, 102, 242, 35975, 112, 168, 119, 97]` |
+| **phi-2-ko** |6| `[57974, 51299, 50617, 51005, 52027, 51446]` |
+
+### Continued pre-training
+
+The dataset used for training is as follows. To prevent catastrophic forgetting, I included some English corpus as training data.
+
+- 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
+```
+
+## How to Get Started with the Model
+```python
+import torch
+from transformers import PhiForCausalLM, AutoModelForCausalLM, AutoTokenizer
+
+torch.set_default_device("cuda")
+
+# Load model and tokenizer
+model = AutoModelForCausalLM.from_pretrained("daekeun-ml/phi-2-ko-v0.1", torch_dtype="auto")
+tokenizer = AutoTokenizer.from_pretrained("daekeun-ml/phi-2-ko-v0.1", trust_remote_code=True)
+
+# Korean 
+inputs = tokenizer("머신러닝은 ", return_tensors="pt", return_attention_mask=False)
+
+outputs = model.generate(**inputs, max_length=200)
+text = tokenizer.batch_decode(outputs)[0]
+print(text)
+
+# English 
+inputs = tokenizer('''def print_prime(n):
+   """
+   Print all primes between 1 and n
+   """''', return_tensors="pt", return_attention_mask=False)
+
+outputs = model.generate(**inputs, max_length=200)
+text = tokenizer.batch_decode(outputs)[0]
+print(text)
+```
+
+### 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)!

config.json

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+{
+  "_name_or_path": "/home/ec2-user/SageMaker/phi-2-ko",
+  "architectures": [
+    "PhiForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "auto_map": {
+    "AutoConfig": "configuration_phi.PhiConfig",
+    "AutoModelForCausalLM": "modeling_phi.PhiForCausalLM"
+  },
+  "bos_token_id": 50256,
+  "embd_pdrop": 0.0,
+  "eos_token_id": 50256,
+  "hidden_act": "gelu_new",
+  "hidden_size": 2560,
+  "initializer_range": 0.02,
+  "intermediate_size": 10240,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 2048,
+  "model_type": "phi",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 32,
+  "partial_rotary_factor": 0.4,
+  "qk_layernorm": false,
+  "resid_pdrop": 0.1,
+  "rope_scaling": null,
+  "rope_theta": 10000.0,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.37.2",
+  "use_cache": false,
+  "vocab_size": 66676
+}

configuration_phi.py

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+# coding=utf-8
+# Copyright 2023 Microsoft and the HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+""" Phi model configuration"""
+
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+PHI_PRETRAINED_CONFIG_ARCHIVE_MAP = {
+    "microsoft/phi-2": "https://huggingface.co/microsoft/phi-2/resolve/main/config.json",
+}
+
+
+class PhiConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`PhiModel`]. It is used to instantiate an Phi
+    model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
+    defaults will yield a similar configuration to that of the Phi
+    [microsoft/phi-1](https://huggingface.co/microsoft/phi-1).
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 51200):
+            Vocabulary size of the Phi model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`PhiModel`].
+        hidden_size (`int`, *optional*, defaults to 2048):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 8192):
+            Dimension of the MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 24):
+            Number of hidden layers in the Transformer decoder.
+        num_attention_heads (`int`, *optional*, defaults to 32):
+            Number of attention heads for each attention layer in the Transformer decoder.
+        num_key_value_heads (`int`, *optional*):
+            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+            `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+            by meanpooling all the original heads within that group. For more details checkout [this
+            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
+            `num_attention_heads`.
+        resid_pdrop (`float`, *optional*, defaults to 0.0):
+            Dropout probability for mlp outputs.
+        embd_pdrop (`int`, *optional*, defaults to 0.0):
+            The dropout ratio for the embeddings.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio after computing the attention scores.
+        hidden_act (`str` or `function`, *optional*, defaults to `"gelu_new"`):
+            The non-linear activation function (function or string) in the decoder.
+        max_position_embeddings (`int`, *optional*, defaults to 2048):
+            The maximum sequence length that this model might ever be used with. Phi-1 and Phi-1.5 supports up to 2048
+            tokens.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        layer_norm_eps (`float`, *optional*, defaults to 1e-05):
+            The epsilon used by the rms normalization layers.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models). Only
+            relevant if `config.is_decoder=True`. Whether to tie weight embeddings or not.
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether to tie weight embeddings
+        rope_theta (`float`, *optional*, defaults to 10000.0):
+            The base period of the RoPE embeddings.
+        rope_scaling (`Dict`, *optional*):
+            Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
+            strategies: linear and dynamic. Their scaling factor must be an float greater than 1. The expected format
+            is `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
+            `max_position_embeddings` to the expected new maximum. See the following thread for more information on how
+            these scaling strategies behave:
+            https://www.reddit.com/r/LocalPersimmon/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This
+            is an experimental feature, subject to breaking API changes in future versions.
+        partial_rotary_factor (`float`, *optional*, defaults to 0.5):
+            Percentage of the query and keys which will have rotary embedding.
+        qk_layernorm (`bool`, *optional*, defaults to `False`):
+            Whether or not to normalize the Queries and Keys after projecting the hidden states.
+        bos_token_id (`int`, *optional*, defaults to 1):
+            Denotes beginning of sequences token id.
+        eos_token_id (`int`, *optional*, defaults to 2):
+            Denotes end of sequences token id.
+
+    Example:
+
+    ```python
+    >>> from transformers import PhiModel, PhiConfig
+
+    >>> # Initializing a Phi-1 style configuration
+    >>> configuration = PhiConfig.from_pretrained("microsoft/phi-1")
+
+    >>> # Initializing a model from the configuration
+    >>> model = PhiModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "phi"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=51200,
+        hidden_size=2048,
+        intermediate_size=8192,
+        num_hidden_layers=24,
+        num_attention_heads=32,
+        num_key_value_heads=None,
+        resid_pdrop=0.0,
+        embd_pdrop=0.0,
+        attention_dropout=0.0,
+        hidden_act="gelu_new",
+        max_position_embeddings=2048,
+        initializer_range=0.02,
+        layer_norm_eps=1e-5,
+        use_cache=True,
+        tie_word_embeddings=False,
+        rope_theta=10000.0,
+        rope_scaling=None,
+        partial_rotary_factor=0.5,
+        qk_layernorm=False,
+        bos_token_id=1,
+        eos_token_id=2,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+
+        self.num_key_value_heads = num_key_value_heads
+        self.resid_pdrop = resid_pdrop
+        self.embd_pdrop = embd_pdrop
+        self.attention_dropout = attention_dropout
+        self.hidden_act = hidden_act
+        self.max_position_embeddings = max_position_embeddings
+        self.initializer_range = initializer_range
+        self.layer_norm_eps = layer_norm_eps
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
+        self.partial_rotary_factor = partial_rotary_factor
+        self.qk_layernorm = qk_layernorm
+        self._rope_scaling_validation()
+
+        super().__init__(
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
+
+    # Copied from transformers.models.llama.configuration_llama.LlamaConfig._rope_scaling_validation
+    def _rope_scaling_validation(self):
+        """
+        Validate the `rope_scaling` configuration.
+        """
+        if self.rope_scaling is None:
+            return
+
+        if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
+            raise ValueError(
+                "`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, "
+                f"got {self.rope_scaling}"
+            )
+        rope_scaling_type = self.rope_scaling.get("type", None)
+        rope_scaling_factor = self.rope_scaling.get("factor", None)
+        if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
+            raise ValueError(
+                f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
+            )
+        if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
+            raise ValueError(f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}")

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 50256,
+  "eos_token_id": 50256,
+  "transformers_version": "4.37.2"
+}

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special_tokens_map.json

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tokenizer_config.json

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