Synatra-7B-v0.3-Translation🐧
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Model Details
Base Model
mistralai/Mistral-7B-Instruct-v0.1
Datasets sharegpt_deepl_ko_translation
Filtered version of above dataset included.
Trained On
A100 80GB * 1
Instruction format
It follows ChatML format and Alpaca(No-Input) format.
<|im_start|>system
주어진 문장을 한국어로 번역해라.<|im_end|>
<|im_start|>user
{instruction}<|im_end|>
<|im_start|>assistant
<|im_start|>system
주어진 문장을 영어로 번역해라.<|im_end|>
<|im_start|>user
{instruction}<|im_end|>
<|im_start|>assistant
Ko-LLM-Leaderboard
On Benchmarking...
Implementation Code
Since, chat_template already contains insturction format above. You can use the code below.
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained("maywell/Synatra-7B-v0.3-Translation")
tokenizer = AutoTokenizer.from_pretrained("maywell/Synatra-7B-v0.3-Translation")
messages = [
{"role": "user", "content": "바나나는 원래 하얀색이야?"},
]
encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")
model_inputs = encodeds.to(device)
model.to(device)
generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True)
decoded = tokenizer.batch_decode(generated_ids)
print(decoded[0])