update readme

README.md

@@ -20,3 +20,135 @@ language:
 This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
 
 [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
+
+# 手順
+```
+!pip uninstall unsloth -y
+!pip install --upgrade --no-cache-dir "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"
+!pip install --upgrade torch
+!pip install --upgrade xformers
+
+# Install Flash Attention 2 for softcapping support
+import torch
+if torch.cuda.get_device_capability()[0] >= 8:
+    !pip install --no-deps packaging ninja einops "flash-attn>=2.6.3"
+
+# Hugging Face Token を指定
+HF_TOKEN = "xxx"
+
+# llm-jp/llm-jp-3-13bを4bit量子化のqLoRA設定でロード。
+from unsloth import FastLanguageModel
+import torch
+max_seq_length = 512
+dtype = None 
+load_in_4bit = True 
+
+model_id = "llm-jp/llm-jp-3-13b"
+new_model_id = "llm-jp-3-13b-it" 
+# FastLanguageModel 
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name=model_id,
+    dtype=dtype,
+    load_in_4bit=load_in_4bit,
+    trust_remote_code=True,
+)
+
+# SFT用のモデルを用意
+model = FastLanguageModel.get_peft_model(
+    model,
+    r = 32,
+    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
+                      "gate_proj", "up_proj", "down_proj",],
+    lora_alpha = 32,
+    lora_dropout = 0.05,
+    bias = "none",
+    use_gradient_checkpointing = "unsloth",
+    random_state = 3407,
+    use_rslora = False,
+    loftq_config = None,
+    max_seq_length = max_seq_length,
+)
+
+from datasets import Dataset, load_dataset, concatenate_datasets
+
+# 使用したいデータセットのパス　すべてのichikaraのデータセットを利用
+data_dir = "/content/"
+data_files = [
+    "ichikara-instruction-003-001-1.json",
+    "ichikara-instruction-003-001-2.1.json",
+    "ichikara-instruction-003-001-2.2.json",
+    "ichikara-instruction-003-001-5.1.json",
+    "ichikara-instruction-003-001-5.2.json",
+    "ichikara-instruction-003-003-1.json"
+    ]
+
+dataset = Dataset.from_dict({"ID": [], "text": [], "output":[]})
+for data_file in data_files:
+    tmp = load_dataset("json", data_files=f"{data_dir}{data_file}", split="train", streaming=False)
+    if len(dataset) == 0:
+        dataset = tmp
+    else:
+        dataset = concatenate_datasets([dataset,tmp])
+
+# 学習時のプロンプトフォーマットの定義
+prompt = """### 指示
+{}
+### 回答
+{}"""
+
+EOS_TOKEN = tokenizer.eos_token 
+def formatting_prompts_func(examples):
+    input = examples["text"] 
+    output = examples["output"] 
+    text = prompt.format(input, output) + EOS_TOKEN 
+    return { "formatted_text" : text, } 
+pass
+
+# # 各データにフォーマットを適用
+dataset = dataset.map(
+    formatting_prompts_func,
+    num_proc= 4, 
+)
+
+from trl import SFTTrainer
+from transformers import TrainingArguments
+from unsloth import is_bfloat16_supported
+
+# 学習の設定
+trainer = SFTTrainer(
+    model = model,
+    tokenizer = tokenizer,
+    train_dataset=dataset,
+    max_seq_length = max_seq_length,
+    dataset_text_field="formatted_text",
+    packing = False,
+    args = TrainingArguments(
+        per_device_train_batch_size = 16, #Google Colab Pro+を使ったのでバッチサイズを上げた
+        gradient_accumulation_steps = 1, #蓄積は逆になしに
+        num_train_epochs = 1, #上げたら過学習してさがったので、最終的に1回にした
+        logging_steps = 10,
+        warmup_steps = 100,
+        save_steps=100,
+        save_total_limit=2,
+        max_steps=-1,
+        learning_rate = 2e-4,
+        fp16 = not is_bfloat16_supported(),
+        bf16 = is_bfloat16_supported(),
+        group_by_length=True,
+        seed = 3407,
+        output_dir = "outputs",
+        report_to = "none",
+    ),
+)
+
+# 学習実行
+trainer_stats = trainer.train()
+
+# LoRAアダプタだけ保存
+model.push_to_hub_merged(
+    new_model_id+"_lora_4",
+    tokenizer=tokenizer,
+    save_method="lora",
+    token=HF_TOKEN,
+    private=True
+)