Total runs:
8.1K
Run Growth:
70
Growth Rate:
0.87%
Sponsored by
YouArt
maywell / Synatra-7B-v0.3-Translation
Model's Last Updated: March 29 2024
text-generation
Introduction of Synatra-7B-v0.3-Translation
Model Details of Synatra-7B-v0.3-Translation
Synatra-7B-v0.3-Translation🐧
Support Me
시나트라는 개인 프로젝트로, 1인의 자원으로 개발되고 있습니다. 모델이 마음에 드셨다면 약간의 연구비 지원은 어떨까요?
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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])
Runs of maywell Synatra-7B-v0.3-Translation on huggingface.co
16
Total runs
0
24-hour runs
1
3-day runs
3
7-day runs
1
30-day runs
More Information About Synatra-7B-v0.3-Translation huggingface.co Model
Url of Synatra-7B-v0.3-Translation
Synatra-7B-v0.3-Translation huggingface.co Url
Provider of Synatra-7B-v0.3-Translation huggingface.co
Other API from maywell
Updated:November 21 2023
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