Model reference · open weights

sarashina2.2

LLMs sbintuitions Text gen 1 build Open weights 220k dl/mo

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.

  • sarashina2.2 is an autoregressive language model developed by sbintuitions for Japanese text generation.
  • The 0.5b instruct variant contains 793M parameters and supports a context length of 8192 tokens.
  • It is released under the MIT license.

Summary of the sbintuitions/sarashina2.2-0.5b-instruct-v0.1 model card, 2026-10-01

What it is

Released bysbintuitions
Released2025-02-26
Parameters793M
VRAM1.6 GB for the weights

What it runs on

Memory and cards for sarashina2.2-0.5b-instruct-v0.1 (BF16)

1.6 GBweights, file size
61 MBcache per 1K tokens
592 MBruntime overhead, at least
8,192 tokenscontext max
CardRequests at onceContext maxMemory
8K each
RTX 3060 12 GB18all 8K11.6 GB
RTX 4060 Ti 16 GB26all 8K15.4 GB
RTX 3090 24 GB42all 8K23.4 GB
RTX 4090 24 GB42all 8K23.4 GB
RTX 5090 32 GB57all 8K31.0 GB
L40S 48 GB83all 8K44.0 GB
A100 80 GB151all 8K78.2 GB
H100 80 GB144all 8K78.1 GB
RTX PRO 6000 Blackwell 96 GB175all 8K93.8 GB
DGX Spark (GB10) 128 GB unified202all 8K107 GB
H200 141 GB262all 8K138 GB
B200 180 GB338all 8K176 GB
Memory needed at each load
Requests at once8K tokens each
12.7 GB
54.7 GB
86.2 GB
1610.2 GB
3218.3 GB
6434.4 GB

One card, with vLLM's small-card settings.

From the model card

What sbintuitions says about sarashina2.2

Read the model card

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

ModelElyza-tasks-100Japanese MT BenchEnglish MT Bench
Qwen/Qwen2.5-0.5B-instruct1.532.954.98
sarashina2.2-0.5B-instruct-v0.12.384.555.09
Rakuten/RakutenAI-2.0-mini-instruct2.414.495.13
SakanaAI/TinySwallow-1.5B-Instruct2.815.246.31
Qwen/Qwen2.5-1.5B-instruct2.284.066.99
llm-jp/llm-jp-3-1.8b-instruct32.534.624.83
sarashina2.2-1B-instruct-v0.12.885.096.46
google/gemma-2-2b-jpn-it3.025.197.56
Qwen/Qwen2.5-3B-instruct2.995.687.88
llm-jp/llm-jp-3-3.7b-instruct32.794.985.44
sarashina2.2-3B-instruct-v0.13.756.517.71

How to Use

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline, set_seed

# モデルのロード
model_name = "sbintuitions/sarashina2.2-0.5b-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。'}]

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

Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.

© 2026 AxForge · EU-hosted AI infrastructure Pricing Docs Trust Privacy Terms