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