Update @ 2024.03.07
T3Q-platypus-SOLAR
This model is a fine-tuned version of upstage/SOLAR-10.7B-v1.0
Model Developers Chihoon Lee(chlee10), T3Q
Training hyperparameters
The following hyperparameters were used during training:
# ๋ฐ์ดํฐ์
๊ณผ ํ๋ จ ํ์์ ๊ด๋ จ๋ ํ์ดํผ ํ๋ผ๋ฏธํฐ
batch_size = 16
num_epochs = 1
micro_batch = 1
gradient_accumulation_steps = batch_size // micro_batch
# ํ๋ จ ๋ฐฉ๋ฒ์ ๋ํ ํ์ดํผ ํ๋ผ๋ฏธํฐ
cutoff_len = 4096
lr_scheduler = 'cosine'
warmup_ratio = 0.06 # warmup_steps = 100
learning_rate = 4e-4
optimizer = 'adamw_torch'
weight_decay = 0.01
max_grad_norm = 1.0
# LoRA config(QLoRA)
lora_r = 16
lora_alpha = 16
lora_dropout = 0.05
lora_target_modules = ["gate_proj", "down_proj", "up_proj"]
# Tokenizer์์ ๋์ค๋ input๊ฐ ์ค์ ์ต์
train_on_inputs = False
add_eos_token = False
# NEFTune params
noise_alpha: int = 5
Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu121
- Datasets 2.13.0
- Tokenizers 0.14.1
- Downloads last month
- 16
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.