--- library_name: transformers tags: [] --- # How to use ・ 使い方 We recommend on running with at least 4 A100 cards A100の4枚の環境がおすすめです ### Huggingface ```python from transformers import AutoTokenizer, AutoModelForCausalLM import torch tokenizer = AutoTokenizer.from_pretrained("lightblue/ao-karasu-72B") model = AutoModelForCausalLM.from_pretrained("lightblue/ao-karasu-72B", device_map="auto") pipe = pipeline("text-generation", model=model, tokenizer=tokenizer) messages = [{"role": "system", "content": "あなたはAIアシスタントです。"}] messages.append({"role": "user", "content": "イギリスの首相は誰ですか?"}) prompt = tokenizer.apply_chat_template(conversation=messages, add_generation_prompt=True, tokenize=False) pipe(prompt, max_new_tokens=100, do_sample=False, temperature=0.0, return_full_text=False) ``` ### vLLM ```python from vllm import LLM, SamplingParams sampling_params = SamplingParams(temperature=0.0, max_tokens=100) llm = LLM(model="lightblue/aokarasu-72B", tensor_parallel_size=4) messages = [{"role": "system", "content": "あなたはAIアシスタントです。"}] messages.append({"role": "user", "content": "イギリスの首相は誰ですか?"}) prompt = llm.llm_engine.tokenizer.tokenizer.apply_chat_template(conversation=messages, add_generation_prompt=True, tokenize=False) prompts = [prompt] outputs = llm.generate(prompts, sampling_params) for output in outputs: prompt = output.prompt generated_text = output.outputs[0].text print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}") ``` # Training details 学習詳細 [ブログ](https://editor.note.com/notes/n483d194d3614) # Training data 学習データ Roughly 20 million characters samples from a dataset of more than 1.1 billion characters, which was made up of: ~450 million characters from Wikipedia-based QA (same as Qarasu) ~200 million characters from technical blogs (new) ~200 million characters from Japanese QA site answers (new) ~100 million characters from LLM generated prompts and responses (same as Qarasu) ~70 million characters from news articles (new) # Training schedule Training for ~1 day on a A100 (80GB) GPU