---
license: apache-2.0
datasets:
- graelo/wikipedia
- uonlp/CulturaX
- HuggingFaceH4/ultrachat_200k
language:
- ja
- en
---
# How to use
```python
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)
```
# 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
### Engineers
Peter Devine
Sho Higuchi
### Advisors
Yuuki Yamanaka
Atom Sonoda
### Dataset evaluator
Renju Aoki