--- license: apache-2.0 license_name: tongyi-qianwen-license-agreement license_link: >- https://github.com/QwenLM/Qwen/blob/main/Tongyi%20Qianwen%20LICENSE%20AGREEMENT datasets: - oscar-corpus/OSCAR-2301 - mc4 language: - ja ---

drawing

TinyLlama + Japanese pre-training (50,004 steps) # How to use ### Hugggingface ```python from transformers import AutoTokenizer, AutoModelForCausalLM import torch tokenizer = AutoTokenizer.from_pretrained("lightblue/karasu-1.1B") model = AutoModelForCausalLM.from_pretrained("lightblue/karasu-1.1B", torch_dtype=torch.bfloat16, 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/karasu-1.1B") 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.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}") ``` # Base checkpoint [TinyLlama/TinyLlama-1.1B-intermediate-step-715k-1.5T](TinyLlama/TinyLlama-1.1B-intermediate-step-715k-1.5T) # Training datasets (total ~3B) A filtered then sampled set from * OSCAR (Japanese) * mC4 (Japanese) # Developed by Lightblue technology logo ### Engineers Peter Devine Sho Higuchi ### Advisors Yuuki Yamanaka Atom Sonoda ### Project manager Shunichi Taniguchi Tomioka Wataru ### Dataset evaluator Renju Aoki