2.2 KiB
language, library_name, pipeline_tag, license
| language | library_name | pipeline_tag | license | |
|---|---|---|---|---|
|
transformers | text-generation | cc-by-nc-4.0 |
This is VERY Ealry Model of Development!
이 모델은 Synatra-Zephyr-7B의 극초기 버전입니다.
Synatra-Zephyr-7B-v0.01🐧
Support Me
시나트라는 개인 프로젝트로, 1인의 자원으로 개발되고 있습니다. 모델이 마음에 드셨다면 약간의 연구비 지원은 어떨까요?
Wanna be a sponser? Contact me on Telegram AlzarTakkarsen
License
This model is strictly non-commercial (cc-by-nc-4.0) use only. The "Model" is completely free (ie. base model, derivates, merges/mixes) to use for non-commercial purposes as long as the the included cc-by-nc-4.0 license in any parent repository, and the non-commercial use statute remains, regardless of other models' licences. The licence can be changed after new model released. If you are to use this model for commercial purpose, Contact me.
Model Details
Base Model
mistralai/Mistral-7B-Instruct-v0.1
Trained On
A100 80G * 4
Model Benchmark
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-Zephyr-7B-v0.01")
tokenizer = AutoTokenizer.from_pretrained("maywell/Synatra-Zephyr-7B-v0.01")
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])