--- base_model: kumapo/llm-jp-3-13b-jaster-dev-3k tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** kumapo - **License:** apache-2.0 - **Finetuned from model :** kumapo/llm-jp-3-13b-jaster-dev-3k This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [](https://github.com/unslothai/unsloth) # Usage ```bash # 必要なパッケージをインストール pip install pip3-autoremove pip-autoremove torch torchvision torchaudio -y pip install torch torchvision torchaudio xformers --index-url https://download.pytorch.org/whl/cu121 pip install unsloth ``` ```Python # 必要なライブラリを読み込み from unsloth import FastLanguageModel import json from tqdm import tqdm from datasets import load_dataset from google.colab import userdata model_id = "kumapo/llm-jp-3-13b-jaster-dev-3k-ichikara-003-3k-synthe-elyza-3k-4096" data_file = "./elyza-tasks-100-TV_0.jsonl" # Google Colabの場合 HF_TOKEN = userdata.get('HF_ACCESS_TOKEN') # unslothのFastLanguageModelで元のモデルをロード。 dtype = None # Noneにしておけば自動で設定 load_in_4bit = True # 今回は13Bモデルを扱うためTrue model, tokenizer = FastLanguageModel.from_pretrained( model_name=model_id, dtype=dtype, load_in_4bit=load_in_4bit, trust_remote_code=True, token=HF_TOKEN ) from datasets import load_dataset # タスクとなるデータの読み込み。 # 事前にデータをアップロードしてください。 datasets = load_dataset("json", data_files=data_file, split="train") # 推論するためにモデルのモードを変更 FastLanguageModel.for_inference(model) PROMPT = "以下は、タスクを説明する指示です。要求を適切に満たす応答を書きなさい。\n\n### 指示:\n{}\n\n### 応答:\n{}" MAX_SEQ_LEN = 4096 results = [] for dt in tqdm(datasets): input = dt["input"] prompt = PROMPT.format(input, "") # プロンプトの作成 inputs = tokenizer([prompt], return_tensors = "pt").to(model.device) max_new_tokens = MAX_SEQ_LEN - inputs["input_ids"].shape[-1] outputs = model.generate(**inputs, max_new_tokens = max_new_tokens, use_cache = True, do_sample=False, repetition_penalty=1.2) prediction = tokenizer.decode( outputs[0][inputs["input_ids"].shape[-1] :], skip_special_tokens=True, ) results.append({"task_id": dt["task_id"], "input": input, "output": prediction}) # 結果をjsonlで保存。 result_file = f"{model_id.replace('/', '-')}-outputs.jsonl" with open(result_file, 'w', encoding='utf-8') as f: for result in results: json.dump(result, f, ensure_ascii=False) f.write('\n') ```