Swallow-MS-7b-v0.1-ChatVector

Japanese "instruction tuned" model made by the technique of Chat Vector

The weights of this model are obtained not by any instruction tuning but by the following arithmetic:

Swallow-MS-7b-v0.1 + Mistral-7B-Instruct-v0.2 - Mistral-7B-v0.1

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Chat Vectorの手法を使って、学習済み重みの足し引きのみでSwallow-MS-7b-v0.1モデルにチャット形式の対話能力を与えたモデルです。

詳細はこちらの日本語記事で解説しています。

Instruction format

The promot format should be the same as Mistral-7B-Instruct-v0.2.

E.g.

text = "<s>[INST] What is your favourite condiment? [/INST]"
"Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!</s> "
"[INST] Do you have mayonnaise recipes? [/INST]"

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_name = "jovyan/Swallow-MS-7b-v0.1-ChatVector"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

prompt = "<s>[INST] 東京工業大学のキャンパスの特色を元気よく説明してください。 [/INST]"
input_ids = tokenizer.encode(
    prompt,
    add_special_tokens=False,
    return_tensors="pt"
)
tokens = model.generate(
    input_ids.to(device=model.device),
    max_new_tokens=128,
    temperature=0.99,
    top_p=0.95,
    do_sample=True,
)

out = tokenizer.decode(tokens[0], skip_special_tokens=True)
print(out)