sbintuitions/sarashina2.2-1b-instruct-v0.1
Model Summary
This repository provides Japanese language models trained by SB Intuitions.
Model Details
- Model type: Autoregressive Language Model
- Language(s): Japanese
Evaluation in Japanese and English Tasks
| Model | Elyza-tasks-100 | Japanese MT Bench | English MT Bench |
|---|---|---|---|
| Qwen/Qwen2.5-0.5B-instruct | 1.53 | 2.95 | 4.98 |
| sarashina2.2-0.5B-instruct-v0.1 | 2.38 | 4.55 | 5.09 |
| Rakuten/RakutenAI-2.0-mini-instruct | 2.41 | 4.49 | 5.13 |
| SakanaAI/TinySwallow-1.5B-Instruct | 2.81 | 5.24 | 6.31 |
| Qwen/Qwen2.5-1.5B-instruct | 2.28 | 4.06 | 6.99 |
| llm-jp/llm-jp-3-1.8b-instruct3 | 2.53 | 4.62 | 4.83 |
| sarashina2.2-1B-instruct-v0.1 | 2.88 | 5.09 | 6.46 |
| google/gemma-2-2b-jpn-it | 3.02 | 5.19 | 7.56 |
| Qwen/Qwen2.5-3B-instruct | 2.99 | 5.68 | 7.88 |
| llm-jp/llm-jp-3-3.7b-instruct3 | 2.79 | 4.98 | 5.44 |
| sarashina2.2-3B-instruct-v0.1 | 3.75 | 6.51 | 7.71 |
How to Use
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline, set_seed
# モデルのロード
model_name = "sbintuitions/sarashina2.2-1b-instruct-v0.1"
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(model_name)
chat_pipeline = pipeline("text-generation", model=model, tokenizer=tokenizer)
set_seed(123)
# ユーザーの入力
user_input = [{"role": "user", "content": "こんにちは。あなたの名前を教えて"}]
# モデルによる応答生成
responses = chat_pipeline(
user_input,
max_length=50,
do_sample=True,
num_return_sequences=3,
)
# 応答を表示
for i, response in enumerate(responses, 1):
print(f"Response {i}: {response['generated_text']}")
# Response 1: [{'role': 'user', 'content': 'こんにちは。あなたの名前を教えて'}, {'role': 'assistant', 'content': 'Sarashina2と言います。本日のご要件を教えて下さい。'}]
# Response 2: [{'role': 'user', 'content': 'こんにちは。あなたの名前を教えて'}, {'role': 'assistant', 'content': 'こんにちは！私の名前はSarashina2です。今日はどうしましたか？'}]
# Response 3: [{'role': 'user', 'content': 'こんにちは。あなたの名前を教えて'}, {'role': 'assistant', 'content': 'Sarashina2と言います。本日のご要件を教えて下さい。'}]
Limitations
This model has limited safety training. Therefore, it might generate some meaningless sequences, some inaccurate instances, or biased/objectionable outputs. Before using it, we would like developers to tune models based on human preferences and safety considerations.
License
MIT License