Introduction of japanese-stablelm-instruct-beta-70b
Model Details of japanese-stablelm-instruct-beta-70b
Japanese-StableLM-Instruct-Beta-70B
A cute robot wearing a kimono writes calligraphy with one single brush —
Stable Diffusion XL
Model Description
japanese-stablelm-instruct-beta-70b
is a 70B-parameter decoder-only language model based on
japanese-stablelm-base-beta-70b
and further fine tuned on Databricks Dolly-15k, Anthropic HH, and other public data.
Then start generating text with
japanese-stablelm-instruct-beta-70b
by using the following code snippet:
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
model_name = "stabilityai/japanese-stablelm-instruct-beta-70b"
tokenizer = AutoTokenizer.from_pretrained(model_name)
# The next line may need to be modified depending on the environment
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, low_cpu_mem_usage=True, device_map="auto")
defbuild_prompt(user_query, inputs):
sys_msg = "<s>[INST] <<SYS>>\nあなたは役立つアシスタントです。\n<<SYS>>\n\n"
p = sys_msg + user_query + "\n\n" + inputs + " [/INST] "return p
# 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。",
"inputs": "情けは人のためならず"
}
prompt = build_prompt(**user_inputs)
input_ids = tokenizer.encode(
prompt,
add_special_tokens=True,
return_tensors="pt"
)
# this is for reproducibility.# feel free to change to get different result
seed = 23
torch.manual_seed(seed)
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)
We suggest playing with different generation config (
top_p
,
repetition_penalty
etc) to find the best setup for your tasks. For example, use higher temperature for roleplay task, lower temperature for reasoning.
Model Details
Model type
:
japanese-stablelm-instruct-beta-70b
model is an auto-regressive language model based on the Llama2 transformer architecture.
The following datasets were used for the instruction training. Note these are Japanese translated versions of the original datasets, shared by
kunishou
.
The model is intended to be used by all individuals as a foundation for application-specific fine-tuning without strict limitations on commercial use.
Limitations and bias
The pre-training dataset may have contained offensive or inappropriate content even after applying data cleansing filters which can be reflected in the model generated text. We recommend users exercise reasonable caution when using these models in production systems. Do not use the model for any applications that may cause harm or distress to individuals or groups.
Authors
This model was developed by the Research & Development team at Stability AI Japan, and the development was co-led by
Takuya Akiba
and
Meng Lee
. The members of the team are as follows:
We thank Meta Research for releasing Llama 2 under an open license for others to build on.
We are grateful for the contributions of the EleutherAI Polyglot-JA team in helping us to collect a large amount of pre-training data in Japanese. Polyglot-JA members includes Hyunwoong Ko (Project Lead), Fujiki Nakamura (originally started this project when he commited to the Polyglot team), Yunho Mo, Minji Jung, KeunSeok Im, and Su-Kyeong Jang.
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