llm-jp-3-1.8B-finetune-ver3

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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

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How to Get Started with the Model

Use the code below to get started with the model.

from google.colab import drive
drive.mount('/content/drive')

from google.colab import userdata
submit_key = userdata.get('submit_key') # "submit用key"

# python 3.10.12
!pip install -U pip
!pip install -U transformers
!pip install -U bitsandbytes
!pip install -U accelerate
!pip install -U datasets
!pip install -U peft
!pip install -U trl
!pip install -U wandb
!pip install ipywidgets --upgrade

from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
    BitsAndBytesConfig,
    TrainingArguments,
    logging,
)
from peft import (
    LoraConfig,
    PeftModel,
    get_peft_model,
)
import os, torch, gc
import bitsandbytes as bnb
from trl import SFTTrainer
from datasets import Dataset, DatasetDict, concatenate_datasets, load_dataset
from tqdm import tqdm

from google.colab import output
output.enable_custom_widget_manager()


new_model_id = "ymatsuda-97/llm-jp-3-1.8B-finetune-ver3"

"""
bnb_config: 量子化の設定

  - load_in_4bit:
      - 4bit量子化形式でモデルをロード

  - bnb_4bit_quant_type:
      - 量子化の形式を指定

  - bnb_4bit_compute_dtype:
      - 量子化された重みを用いて計算する際のデータ型

"""

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4", # nf4は通常のINT4より精度が高く、ニューラルネットワークの分布に最適です
    bnb_4bit_compute_dtype=torch.bfloat16,
)

"""
model: モデル

  - base_model:
      - 読み込むベースモデル (事前に定義したもの)

  - quantization_config:
      - bnb_configで設定した量子化設定

  - device_map:
      - モデルを割り当てるデバイス (CPU/GPU) "auto"で自動に割り当てられます。

tokenizer: トークナイザー

  - base_model:
      - 読み込むベースモデル (事前に定義したもの)

  - trust_remote_code:
      - リモートコードの実行を許可 (カスタムモデルなど)
"""
model = AutoModelForCausalLM.from_pretrained(
    new_model_id,
    quantization_config=bnb_config,
    device_map="auto",
    token=submit_key,
)

tokenizer = AutoTokenizer.from_pretrained(
    new_model_id,
    trust_remote_code=True,
    token=submit_key
)

# # モデルによるタスクの推論。
# prompt = """### 指示
# {}
# ### 回答
# """
#
# def evaluate_task(task, filename, prompt):
#     results = []
#     for data in tqdm(task):
#         input = data["input"]
#         input_prompt = prompt.format(input)
#
#         tokenized_input = tokenizer.encode(input_prompt, add_special_tokens=False, return_tensors="pt").to(model.device)
#         attention_mask = tokenized_input.ne(tokenizer.pad_token_id) # torch.ones_like(tokenized_input)
#
#         with torch.no_grad():
#             outputs = model.generate(
#                 tokenized_input,
#                 attention_mask=attention_mask,
#                 max_new_tokens=1024,
#                 do_sample=False,
#                 repetition_penalty=1.2,
#                 pad_token_id=tokenizer.eos_token_id
#             )[0]
#         output = tokenizer.decode(outputs[tokenized_input.size(1):], skip_special_tokens=True)

Training Details

Training Data

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Training Procedure

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Preprocessing [optional]

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Training Hyperparameters

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Evaluation

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Testing Data, Factors & Metrics

Testing Data

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Summary

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Environmental Impact

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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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