

<p align="center">
  <img src="https://cdn-uploads.huggingface.co/production/uploads/64c8a2e01c25d2c581a381c1/9CbN4lDGU42c-7DmK_mGM.png" alt="drawing" width="600"/>
</p>

# Evaluation

![image/png](https://cdn-uploads.huggingface.co/production/uploads/64b63f8ad57e02621dc93c8b/kICKvIbr6hpl0zT1-Y2Ut.png)

# How to use

### Hugggingface
```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)
```


### VLLM
```python
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

<a href="https://www.lightblue-tech.com">
<img src="https://www.lightblue-tech.com/wp-content/uploads/2023/08/color_%E6%A8%AA%E5%9E%8B-1536x469.png" alt="Lightblue technology logo" width="400"/>
</a>

### Engineers
Peter Devine

Sho Higuchi

### Advisors
Yuuki Yamanaka 

Atom Sonoda

### Project manager
Shunichi Taniguchi

### Dataset evaluator
Renju Aoki
{"language": ["ja", "en"], "license": "apache-2.0", "datasets": ["graelo/wikipedia", "uonlp/CulturaX", "HuggingFaceH4/ultrachat_200k"]}
transformers safetensors mistral text-generation conversational ja en dataset:graelo/wikipedia dataset:uonlp/CulturaX dataset:HuggingFaceH4/ultrachat_200k license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us