--- 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:** fajoie - **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) # 使用方法 ``` !pip install -U bitsandbytes !pip install -U transformers !pip install -U accelerate !pip install -U datasets !pip install -U peft !pip install ipywidgets --upgrade from transformers import ( AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, ) from peft import PeftModel import torch from tqdm import tqdm import json # Hugging Faceで取得したTokenをこちらに貼る。 HF_TOKEN = "xxx" # ベースとなるモデルと学習したLoRAのアダプタ。 model_id = "llm-jp/llm-jp-3-13b" adapter_id = "fajoie/llmjp3_lora" # QLoRA config bnb_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.bfloat16, ) # Load model model = AutoModelForCausalLM.from_pretrained( model_id, quantization_config=bnb_config, device_map="auto", token = HF_TOKEN ) # Load tokenizer tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True, token = HF_TOKEN) # 元のモデルにLoRAのアダプタを統合。 model = PeftModel.from_pretrained(model, adapter_id, token = HF_TOKEN) # データセットの読み込み。 datasets = [] with open("./elyza-tasks-100-TV_0.jsonl", "r") as f: item = "" for line in f: line = line.strip() item += line if item.endswith("}"): datasets.append(json.loads(item)) item = "" # llmjp results = [] for data in tqdm(datasets): input = data["input"] prompt = f"""### 指示 {input} ### 回答 """ tokenized_input = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt").to(model.device) attention_mask = torch.ones_like(tokenized_input) with torch.no_grad(): outputs = model.generate( tokenized_input, attention_mask=attention_mask, max_new_tokens=100, 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) results.append({"task_id": data["task_id"], "input": input, "output": output}) # ファイル保存 import re jsonl_id = re.sub(".*/", "", adapter_id) with open(f"./{jsonl_id}-outputs.jsonl", 'w', encoding='utf-8') as f: for result in results: json.dump(result, f, ensure_ascii=False) # ensure_ascii=False for handling non-ASCII characters f.write('\n') ``` # 学習手法 ``` !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 )