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Model: daekeun-ml/phi-2-ko-v0.1 Source: Original Platform
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159
README.md
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---
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library_name: transformers
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license: cc-by-sa-3.0
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datasets:
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- wikimedia/wikipedia
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- maywell/korean_textbooks
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- nampdn-ai/tiny-codes
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- Open-Orca/OpenOrca
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language:
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- ko
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- en
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inference: false
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---
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# phi-2-ko-v0.1
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## Model Details
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This model is a Korean-specific model trained in phi-2 by adding a Korean tokenizer and Korean data. (English is also available.)
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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.
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To overcome these limitations, I trained the model using an open-license Korean corpus and some English corpus.
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The reasons for using the English corpus together are as follows:
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1. The goal is to preserve the excellent performance of the existing model by preventing catastrophic forgetting.
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2. Mixing English and Korean prompts usually produces better results than using all prompts in Korean.
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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.
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### Vocab Expansion
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| Model Name | Vocabulary Size | Description |
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| --- | --- | --- |
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| Original phi-2 | 50,295 | BBPE (Byte-level BPE) |
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| **phi-2-ko** | 66,676 | BBPE. Added Korean vocab and merges |
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**Tokenizing "아마존 세이지메이커"**
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| Model | # of tokens | Tokens |
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| --- | --- | --- |
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| 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]` |
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| **phi-2-ko** |6| `[57974, 51299, 50617, 51005, 52027, 51446]` |
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### Continued pre-training
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The dataset used for training is as follows. To prevent catastrophic forgetting, I included some English corpus as training data.
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- Wikipedia Korean dataset (https://huggingface.co/datasets/wikimedia/wikipedia)
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- Massive Korean synthetic dataset (https://huggingface.co/datasets/maywell/korean_textbooks)
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- Tiny code dataset (https://huggingface.co/datasets/nampdn-ai/tiny-codes)
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- OpenOrca dataset (https://huggingface.co/datasets/Open-Orca/OpenOrca)
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- Using some of the various sentences I wrote (personal blog, chat, etc.)
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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.
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For distributed training, all weights were trained without adapter techniques, and sharding parallelization was performed with ZeRO-2. The presets are as follows.
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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.
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```json
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{
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"fp16": {
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"enabled": "auto",
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"loss_scale": 0,
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"loss_scale_window": 1000,
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"initial_scale_power": 16,
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"hysteresis": 2,
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"min_loss_scale": 1
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},
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"bf16": {
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"enabled": "auto"
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},
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"optimizer": {
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"type": "AdamW",
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"params": {
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"lr": "auto",
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"betas": "auto",
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"eps": "auto",
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"weight_decay": "auto"
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}
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},
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"scheduler": {
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"type": "WarmupLR",
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"params": {
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"warmup_min_lr": "auto",
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"warmup_max_lr": "auto",
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"warmup_num_steps": "auto"
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}
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},
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"zero_optimization": {
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"stage": 2,
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"allgather_partitions": true,
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"allgather_bucket_size": 2e8,
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"overlap_comm": true,
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"reduce_scatter": true,
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"reduce_bucket_size": 2e8,
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"contiguous_gradients": true,
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"cpu_offload": true
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},
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"gradient_accumulation_steps": "auto",
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"gradient_clipping": "auto",
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"train_batch_size": "auto",
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"train_micro_batch_size_per_gpu": "auto"
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}
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```
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Some hyperparameters are listed below.
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```
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batch_size: 2
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num_epochs: 1
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learning_rate: 3e-4
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gradient_accumulation_steps: 8
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lr_scheduler_type: "linear"
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group_by_length: False
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```
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## How to Get Started with the Model
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```python
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import torch
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from transformers import PhiForCausalLM, AutoModelForCausalLM, AutoTokenizer
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torch.set_default_device("cuda")
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# Load model and tokenizer
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model = AutoModelForCausalLM.from_pretrained("daekeun-ml/phi-2-ko-v0.1", torch_dtype="auto")
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tokenizer = AutoTokenizer.from_pretrained("daekeun-ml/phi-2-ko-v0.1", trust_remote_code=True)
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# Korean
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inputs = tokenizer("머신러닝은 ", return_tensors="pt", return_attention_mask=False)
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outputs = model.generate(**inputs, max_length=200)
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text = tokenizer.batch_decode(outputs)[0]
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print(text)
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# English
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inputs = tokenizer('''def print_prime(n):
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"""
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Print all primes between 1 and n
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"""''', return_tensors="pt", return_attention_mask=False)
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outputs = model.generate(**inputs, max_length=200)
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text = tokenizer.batch_decode(outputs)[0]
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print(text)
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```
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### References
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- Base model: [microsoft/phi-2](https://huggingface.co/microsoft/phi-2)
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## Notes
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### License
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cc-by-sa 3.0; The license of phi-2 is MIT, but I considered the licensing of the dataset used for training.
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### Caution
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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)!
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34
config.json
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{
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"_name_or_path": "/home/ec2-user/SageMaker/phi-2-ko",
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"architectures": [
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"PhiForCausalLM"
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],
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"attention_dropout": 0.0,
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"auto_map": {
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"AutoConfig": "configuration_phi.PhiConfig",
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"AutoModelForCausalLM": "modeling_phi.PhiForCausalLM"
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},
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"bos_token_id": 50256,
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"embd_pdrop": 0.0,
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"eos_token_id": 50256,
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"hidden_act": "gelu_new",
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"hidden_size": 2560,
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"initializer_range": 0.02,
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"intermediate_size": 10240,
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"layer_norm_eps": 1e-05,
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"max_position_embeddings": 2048,
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"model_type": "phi",
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"num_key_value_heads": 32,
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"partial_rotary_factor": 0.4,
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"qk_layernorm": false,
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"resid_pdrop": 0.1,
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"rope_scaling": null,
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"rope_theta": 10000.0,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.37.2",
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"use_cache": false,
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"vocab_size": 66676
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}
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193
configuration_phi.py
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# coding=utf-8
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# Copyright 2023 Microsoft and the HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
|
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" Phi model configuration"""
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from transformers.configuration_utils import PretrainedConfig
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from transformers.utils import logging
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logger = logging.get_logger(__name__)
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PHI_PRETRAINED_CONFIG_ARCHIVE_MAP = {
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"microsoft/phi-2": "https://huggingface.co/microsoft/phi-2/resolve/main/config.json",
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}
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class PhiConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`PhiModel`]. It is used to instantiate an Phi
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model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
|
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defaults will yield a similar configuration to that of the Phi
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[microsoft/phi-1](https://huggingface.co/microsoft/phi-1).
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Args:
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vocab_size (`int`, *optional*, defaults to 51200):
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Vocabulary size of the Phi model. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling [`PhiModel`].
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hidden_size (`int`, *optional*, defaults to 2048):
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Dimension of the hidden representations.
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intermediate_size (`int`, *optional*, defaults to 8192):
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Dimension of the MLP representations.
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num_hidden_layers (`int`, *optional*, defaults to 24):
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Number of hidden layers in the Transformer decoder.
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num_attention_heads (`int`, *optional*, defaults to 32):
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Number of attention heads for each attention layer in the Transformer decoder.
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num_key_value_heads (`int`, *optional*):
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This is the number of key_value heads that should be used to implement Grouped Query Attention. If
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`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
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`num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
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converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
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by meanpooling all the original heads within that group. For more details checkout [this
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paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
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`num_attention_heads`.
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resid_pdrop (`float`, *optional*, defaults to 0.0):
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Dropout probability for mlp outputs.
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embd_pdrop (`int`, *optional*, defaults to 0.0):
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The dropout ratio for the embeddings.
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attention_dropout (`float`, *optional*, defaults to 0.0):
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The dropout ratio after computing the attention scores.
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hidden_act (`str` or `function`, *optional*, defaults to `"gelu_new"`):
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The non-linear activation function (function or string) in the decoder.
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max_position_embeddings (`int`, *optional*, defaults to 2048):
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The maximum sequence length that this model might ever be used with. Phi-1 and Phi-1.5 supports up to 2048
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tokens.
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initializer_range (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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layer_norm_eps (`float`, *optional*, defaults to 1e-05):
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The epsilon used by the rms normalization layers.
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use_cache (`bool`, *optional*, defaults to `True`):
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Whether or not the model should return the last key/values attentions (not used by all models). Only
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relevant if `config.is_decoder=True`. Whether to tie weight embeddings or not.
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tie_word_embeddings (`bool`, *optional*, defaults to `False`):
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Whether to tie weight embeddings
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rope_theta (`float`, *optional*, defaults to 10000.0):
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The base period of the RoPE embeddings.
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rope_scaling (`Dict`, *optional*):
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Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
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strategies: linear and dynamic. Their scaling factor must be an float greater than 1. The expected format
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is `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
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`max_position_embeddings` to the expected new maximum. See the following thread for more information on how
|
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these scaling strategies behave:
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https://www.reddit.com/r/LocalPersimmon/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This
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is an experimental feature, subject to breaking API changes in future versions.
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partial_rotary_factor (`float`, *optional*, defaults to 0.5):
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Percentage of the query and keys which will have rotary embedding.
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qk_layernorm (`bool`, *optional*, defaults to `False`):
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Whether or not to normalize the Queries and Keys after projecting the hidden states.
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bos_token_id (`int`, *optional*, defaults to 1):
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Denotes beginning of sequences token id.
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eos_token_id (`int`, *optional*, defaults to 2):
|
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Denotes end of sequences token id.
|
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Example:
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```python
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>>> from transformers import PhiModel, PhiConfig
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>>> # Initializing a Phi-1 style configuration
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>>> configuration = PhiConfig.from_pretrained("microsoft/phi-1")
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>>> # Initializing a model from the configuration
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>>> model = PhiModel(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "phi"
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keys_to_ignore_at_inference = ["past_key_values"]
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def __init__(
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self,
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vocab_size=51200,
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hidden_size=2048,
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intermediate_size=8192,
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num_hidden_layers=24,
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num_attention_heads=32,
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num_key_value_heads=None,
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resid_pdrop=0.0,
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embd_pdrop=0.0,
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attention_dropout=0.0,
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hidden_act="gelu_new",
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max_position_embeddings=2048,
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initializer_range=0.02,
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layer_norm_eps=1e-5,
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use_cache=True,
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tie_word_embeddings=False,
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rope_theta=10000.0,
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rope_scaling=None,
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partial_rotary_factor=0.5,
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qk_layernorm=False,
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bos_token_id=1,
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eos_token_id=2,
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**kwargs,
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):
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self.vocab_size = vocab_size
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self.hidden_size = hidden_size
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self.intermediate_size = intermediate_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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if num_key_value_heads is None:
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num_key_value_heads = num_attention_heads
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self.num_key_value_heads = num_key_value_heads
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self.resid_pdrop = resid_pdrop
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self.embd_pdrop = embd_pdrop
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self.attention_dropout = attention_dropout
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self.hidden_act = hidden_act
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self.max_position_embeddings = max_position_embeddings
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self.initializer_range = initializer_range
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self.layer_norm_eps = layer_norm_eps
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self.use_cache = use_cache
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self.rope_theta = rope_theta
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self.rope_scaling = rope_scaling
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self.partial_rotary_factor = partial_rotary_factor
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self.qk_layernorm = qk_layernorm
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self._rope_scaling_validation()
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super().__init__(
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bos_token_id=bos_token_id,
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eos_token_id=eos_token_id,
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tie_word_embeddings=tie_word_embeddings,
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**kwargs,
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)
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# Copied from transformers.models.llama.configuration_llama.LlamaConfig._rope_scaling_validation
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||||
def _rope_scaling_validation(self):
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"""
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Validate the `rope_scaling` configuration.
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||||
"""
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||||
if self.rope_scaling is None:
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||||
return
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||||
|
||||
if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
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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}")
|
||||
6
generation_config.json
Normal file
6
generation_config.json
Normal file
@@ -0,0 +1,6 @@
|
||||
{
|
||||
"_from_model_config": true,
|
||||
"bos_token_id": 50256,
|
||||
"eos_token_id": 50256,
|
||||
"transformers_version": "4.37.2"
|
||||
}
|
||||
66530
merges.txt
Normal file
66530
merges.txt
Normal file
File diff suppressed because it is too large
Load Diff
3
model-00001-of-00002.safetensors
Normal file
3
model-00001-of-00002.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:8dbe878142640c197bf05fff86dc42131e0a0daba45412ec98d3f6603a5ebd00
|
||||
size 4956815344
|
||||
3
model-00002-of-00002.safetensors
Normal file
3
model-00002-of-00002.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:944464dc9e8ea4ad06e9c4ebf36dabb8f0f56250bd7ffffe05feec675f2e9943
|
||||
size 761107696
|
||||
460
model.safetensors.index.json
Normal file
460
model.safetensors.index.json
Normal file
@@ -0,0 +1,460 @@
|
||||
{
|
||||
"metadata": {
|
||||
"total_size": 5717872872
|
||||
},
|
||||
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|
||||
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|
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"lm_head.weight": "model-00002-of-00002.safetensors",
|
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"model.embed_tokens.weight": "model-00001-of-00002.safetensors",
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
||||
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
||||
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||||
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||||
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|
||||
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|
||||
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||||
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|
||||
"model.layers.9.self_attn.v_proj.weight": "model-00001-of-00002.safetensors"
|
||||
}
|
||||
}
|
||||
1369
modeling_phi.py
Normal file
1369
modeling_phi.py
Normal file
File diff suppressed because it is too large
Load Diff
24
special_tokens_map.json
Normal file
24
special_tokens_map.json
Normal file
@@ -0,0 +1,24 @@
|
||||
{
|
||||
"bos_token": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"eos_token": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": "!",
|
||||
"unk_token": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
133262
tokenizer.json
Normal file
133262
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
28
tokenizer_config.json
Normal file
28
tokenizer_config.json
Normal file
@@ -0,0 +1,28 @@
|
||||
{
|
||||
"add_prefix_space": false,
|
||||
"added_tokens_decoder": {
|
||||
"0": {
|
||||
"content": "!",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"50256": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
}
|
||||
},
|
||||
"bos_token": "<|endoftext|>",
|
||||
"clean_up_tokenization_spaces": true,
|
||||
"eos_token": "<|endoftext|>",
|
||||
"model_max_length": 2048,
|
||||
"pad_token": "!",
|
||||
"tokenizer_class": "CodeGenTokenizer",
|
||||
"unk_token": "<|endoftext|>"
|
||||
}
|
||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user