Model reference · open weights
sarashina2.2 is an open-weight language model from sbintuitions. sarashina2.2-0.5b-instruct-v0.1 (BF16) weighs 1.6 GB; the smallest configuration that runs it is RTX 3060 12 GB.
For self-hosting, compare this build's requirements below with the GPU you plan to rent.
Summary of the sbintuitions/sarashina2.2-0.5b-instruct-v0.1 model card, 2026-10-01
What it is
| Released by | sbintuitions |
|---|---|
| Released | 2025-02-26 |
| Parameters | 793M |
| VRAM | 1.6 GB for the weights |
What it runs on
| Card | Requests at once | Context max | Memory |
|---|---|---|---|
| 8K each | |||
| RTX 3060 12 GB | 18 | all 8K | 11.6 GB |
| RTX 4060 Ti 16 GB | 26 | all 8K | 15.4 GB |
| RTX 3090 24 GB | 42 | all 8K | 23.4 GB |
| RTX 4090 24 GB | 42 | all 8K | 23.4 GB |
| RTX 5090 32 GB | 57 | all 8K | 31.0 GB |
| L40S 48 GB | 83 | all 8K | 44.0 GB |
| A100 80 GB | 151 | all 8K | 78.2 GB |
| H100 80 GB | 144 | all 8K | 78.1 GB |
| RTX PRO 6000 Blackwell 96 GB | 175 | all 8K | 93.8 GB |
| DGX Spark (GB10) 128 GB unified | 202 | all 8K | 107 GB |
| H200 141 GB | 262 | all 8K | 138 GB |
| B200 180 GB | 338 | all 8K | 176 GB |
| Requests at once | 8K tokens each |
|---|---|
| 1 | 2.7 GB |
| 5 | 4.7 GB |
| 8 | 6.2 GB |
| 16 | 10.2 GB |
| 32 | 18.3 GB |
| 64 | 34.4 GB |
One card, with vLLM's small-card settings.
From the model card
This repository provides Japanese language models trained by SB Intuitions.
| 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 |
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline, set_seed
# モデルのロード
model_name = "sbintuitions/sarashina2.2-0.5b-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。'}]
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.
MIT License
Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.