koOpenChat-sft🐧
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Model Details
Base Model
OpenChat3.5
Trained On
A100 80GB * 1
Instruction format
It follows ChatML format and Alpaca(No-Input) format.
Model Benchmark
None
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/koOpenChat-sft")
tokenizer = AutoTokenizer.from_pretrained("maywell/koOpenChat-sft")
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])
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 51.36 |
| ARC (25-shot) | 59.81 |
| HellaSwag (10-shot) | 78.73 |
| MMLU (5-shot) | 61.32 |
| TruthfulQA (0-shot) | 51.24 |
| Winogrande (5-shot) | 76.4 |
| GSM8K (5-shot) | 24.18 |
| DROP (3-shot) | 7.82 |