--- 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. [](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 )