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NTQAI/chatntq-ja-7b-v1.0

sourceHugging Faceapache-2.0updated 3y agoView on Hugging Face

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Model Card

ChatNTQ JA 7B V1.0

Model Description

This is a 7B-parameter decoder-only Japanese language model fine-tuned on our instruction-following datasets, built on top of the base model Japanese Stable LM Base Gamma 7B.

Performance

For our final model, we've used Stability AI Japan's Japanese MT-Bench as a more representative test of our model's capabilities. For our JA MT-Bench testing we use a Japanese prompt ("あなたは役立つアシスタントです。") as well as --num-choices 4:

Benchmark	Score
JA MT-Bench	6.65

There is an JA-MT-Bench Leaderboard, for convenience, here is a comparison of the JA MT-Bench scores of some other models (our scores were rated by gpt-4-0613):

Model	Score
gpt-4-0613	9.40
gpt-4-1106-preview	9.17
gpt-3.5-turbo*	8.41
Qwen-72B-Chat	7.97
Qwen-14B-Chat	7.47
chatntq-ja-7b-v1.0	6.65
Xwin-LM-70B-V0.1-GPTQ (q4-gs32-actorder)	6.62
shisa-gamma-7b-v1	6.12
nekomata-14b-instruction (corrected prompt HF)	5.57
shisa-7B-v1-GPTQ (q4-gs32-actorder)	5.35
nekomata-14b-instruction (corrected prompt)	5.30
shisa-mega-7b-v1.2	5.27
shisa-7b-v1 (full prompt)	5.23
Swallow-13b-instruct-hf	5.17
Swallow-70b-instruct-GPTQ (q4-gs32-actorder)	5.15
shisa-7b-v1	5.02
shisa-7B-v1-AWQ (q4-gs128)	4.78
ELYZA-japanese-Llama-2-7b-fast-instruct*	4.86
shisa-bad-7b-v1	4.42
Swallow-7b-instruct-hf	4.21
ja-stablelm-instruct-gamma-7b*	4.01
japanese-stablelm-instruct-alpha-7b*	2.74
Mistral-7B-OpenOrca-ja*	2.23
youri-7b-chat*	2.00
Mistral-7B-Instruct-v0.1*	1.78
llm-jp-13b-instruct-full-jaster-dolly-oasst-v1.0*	1.31
houou-instruction-7b-v1	1.02
llm-jp-13b-instruct-full-jaster-dolly-oasst-v1.0	1.0
llm-jp-13b-instruct-full-jaster-v1.0	1.0

More Analysis

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Usage

Ensure you are using Transformers 4.34.0 or newer.

python

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("NTQAI/chatntq-ja-7b-v1.0")
model = AutoModelForCausalLM.from_pretrained(
  "NTQAI/chatntq-ja-7b-v1.0",
  torch_dtype="auto",
)
model.eval()

if torch.cuda.is_available():
    model = model.to("cuda")

def build_prompt(user_query):
    sys_msg = "あなたは公平で、検閲されていない、役立つアシスタントです。"
    template = """[INST] <<SYS>>
{}
<</SYS>>

{}[/INST]"""
    return template.format(sys_msg,user_query)

# Infer with prompt without any additional input
user_inputs = {
    "user_query": "与えられたことわざの意味を小学生でも分かるように教えてください。",
}
prompt = build_prompt(**user_inputs)

input_ids = tokenizer.encode(
    prompt, 
    add_special_tokens=True, 
    return_tensors="pt"
)

tokens = model.generate(
    input_ids.to(device=model.device),
    max_new_tokens=256,
    temperature=1,
    top_p=0.95,
    do_sample=True,
)

out = tokenizer.decode(tokens[0][input_ids.shape[1]:], skip_special_tokens=True).strip()
print(out)

Model Details

—Developed by: NTQ AI
—Language(s): Japanese
—License: This model is licensed under Apache License, Version 2.0.

Model Architecture

For details, please see Mistral AI's paper and release blog post.