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Evaluation

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How to use

Hugggingface

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("lightblue/karasu-7B")
model = AutoModelForCausalLM.from_pretrained("lightblue/karasu-7B", 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

from vllm import LLM, SamplingParams

sampling_params = SamplingParams(temperature=0.0, max_tokens=100)
llm = LLM(model="lightblue/karasu-7B")

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

augmxnt/shisa-7b-v1

• Mistral-7B base
• Pre-trained on 8B of MADLAD-Ja
• Finetuned on Japanese instructions
• Highest scoring 7B model on conversation benchmark (JA MT-Bench)

Training datasets (total ~7B)

• Aozora Bunko
• Japanese Law Precedent Dataset
• Japanese Wikipedia
• .lg.jp, .go.jp, .ac.jp domain webscrapes from CulturaX (Any documents with same first 25 characters were de-duplicated)
• English Ultrachat200K-gen (So that it doesn't forget English and chatting ability learned in the base checkpoint)

Developed by

👁 Lightblue technology logo

Engineers

Peter Devine

Sho Higuchi

Advisors

Yuuki Yamanaka

Atom Sonoda

Project manager

Shunichi Taniguchi

Dataset evaluator

Renju Aoki

The models trained under our Karasu and Qarasu project • 9 items • Updated Oct 10, 2024 • 1