README.md

---
base_model: llm-jp/llm-jp-3-13b
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---

# Uploaded  model

- **Developed by:** Gamoooo
- **License:** apache-2.0
- **Finetuned from model :** llm-jp/llm-jp-3-13b

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)

!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"

from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
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-last"  

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,
)

# https://huggingface.co/settings/tokens
HF_TOKEN = "your-token"  # @param {type:"string"}

from datasets import load_dataset, concatenate_datasets

# データセットのロード
ichikara_dataset = load_dataset("json", data_files="/content/ichikara-instruction-003-001-1.json")
elyza_dataset = load_dataset("elyza/ELYZA-tasks-100")

EOS_TOKEN = tokenizer.eos_token  # 

# 学習時のプロンプトフォーマットの定義
prompt = """### 指示
{}
### 回答
{}"""

"""
formatting_prompts_func: 各データをプロンプトに合わせた形式に合わせる
"""
def formatting_prompts_func(examples):
    input = examples["text"]  
    output = examples["output"]  
    text = prompt.format(input, output) + EOS_TOKEN  
    return {"formatted_text": text} 

# ichikara-instruction のデータフォーマット
ichikara_dataset = ichikara_dataset.map(
    formatting_prompts_func,
    num_proc=4,  
)

# ELYZA-tasks-100 データセットのフォーマット関数
def elyza_formatting_prompts_func(examples):
    input = examples["input"]  
    output = examples["output"]  
    text = prompt.format(input, output) + EOS_TOKEN  
    return {"formatted_text": text}

# ELYZA-tasks-100 のデータフォーマット
elyza_dataset = elyza_dataset.map(
    elyza_formatting_prompts_func,
    num_proc=4  
)

from datasets import concatenate_datasets

# ichikara-instruction と ELYZA-tasks-100 を統合
combined_dataset = concatenate_datasets([
    ichikara_dataset["train"],  
    elyza_dataset["test"]      
])

# データ品質チェック
# 1. ランダムサンプルを確認
import random
sample_indices = random.sample(range(len(combined_dataset)), 10)
for idx in sample_indices:
    print(combined_dataset[idx]["formatted_text"])

# 2. 自動検査ルール
# 短すぎるデータをチェック（Noneチェックを追加）
short_data = combined_dataset.filter(
    lambda x: x["input"] is not None and x["output"] is not None and (len(x["input"]) < 5 or len(x["output"]) < 5)
)
print(f"\n短すぎるデータ数: {len(short_data)}")

# 指示と回答が同一のデータ（Noneチェックを追加）
duplicate_data = combined_dataset.filter(
    lambda x: x["input"] is not None and x["output"] is not None and x["input"].strip() == x["output"].strip()
)
print(f"\n指示と回答が同一のデータ数: {len(duplicate_data)}")

# 問題のあるデータをフィルタリング（Noneチェックを追加）
filtered_dataset = combined_dataset.filter(
    lambda x: x["input"] is not None and x["output"] is not None and len(x["input"]) > 5 and len(x["output"]) > 5 and x["input"].strip() != x["output"].strip()
)

print(f"元のデータ数: {len(combined_dataset)}")
print(f"フィルタリング後のデータ数: {len(filtered_dataset)}")
print(f"除外されたデータ数: {len(combined_dataset) - len(filtered_dataset)}")

# フィルタリング後のデータの例を確認
print(filtered_dataset[0])

"""
training_arguments: 学習の設定
"""
from trl import SFTTrainer
from transformers import TrainingArguments
from unsloth import is_bfloat16_supported

trainer = SFTTrainer(
    model=model,
    tokenizer=tokenizer,
    train_dataset=filtered_dataset,
    max_seq_length=max_seq_length,
    dataset_text_field="formatted_text",
    packing=False,
    args=TrainingArguments(
        per_device_train_batch_size=2,
        gradient_accumulation_steps=4,
        num_train_epochs=3,
        logging_steps=10,
        warmup_steps=10,
        save_steps=50,
        save_total_limit=2,
        max_steps=200,
        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",
    ),
)

#@title 学習実行
trainer_stats = trainer.train()

import json
from datasets import load_dataset

dataset = load_dataset("json", data_files="/content/elyza-tasks-100-TV_0.jsonl", split="train")

datasets = []  
with open("/content/elyza-tasks-100-TV_0.jsonl", "r", encoding="utf-8") as f:  
    item = ""
    for line in f:
        line = line.strip()
        item += line
        if item.endswith("}"):
            datasets.append(json.loads(item))
            item = ""

from tqdm import tqdm
import json

# 推論するためにモデルのモードを変更
FastLanguageModel.for_inference(model)

results = []
for dt in tqdm(datasets):
    try:
        input_text = dt["input"]

        # プロンプトを生成
        prompt = f"### 指示\n{input_text}\n次の要件を満たしてください：\n1. 簡潔に回答する。\n2. 必要なら箇条書きを使用して要点を整理する。\n3. 指示された内容に忠実に答える。\n### 回答\n"


        # トークナイズ
        inputs = tokenizer([prompt], return_tensors="pt").to(model.device)

        # 推論
        outputs = model.generate(
            **inputs,
            max_new_tokens=512,
            use_cache=True,
            do_sample=False,
            repetition_penalty=1.2,
        )
        prediction = tokenizer.decode(outputs[0], skip_special_tokens=True).split('\n### 回答')[-1]

        # 結果を保存
        results.append({"task_id": dt["task_id"], "input": input_text, "output": prediction})
    except Exception as e:
        print(f"Error processing task_id {dt.get('task_id', 'Unknown')}: {e}")
        results.append({"task_id": dt.get("task_id", "Unknown"), "input": dt.get("input", ""), "output": "Error"})


# 結果をJSONL形式で保存
output_file_jsonl = "/content/llm-jp-3-13b-last.jsonl"
with open(output_file_jsonl, "w", encoding="utf-8") as f:
    for result in results:
        f.write(json.dumps(result, ensure_ascii=False) + "\n")

model.push_to_hub_merged(
    new_model_id,
    tokenizer=tokenizer,
    save_method="lora",
    token=HF_TOKEN,
    private=True
)