Base Model
model_name = "mistralai/Mistral-7B-v0.1"
The instruction dataset to use
dataset_name = "KasparZ/HITL-2"
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QLoRA parameters
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LoRA attention dimension
lora_r = 64
Alpha parameter for LoRA scaling
lora_alpha = 16
Dropout probability for LoRA layers
lora_dropout = 0.05 #0.1
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bitsandbytes parameters
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Activate 4-bit precision base model loading
use_4bit = True
Compute dtype for 4-bit base models
bnb_4bit_compute_dtype = "float16"
Quantization type (fp4 or nf4)
bnb_4bit_quant_type = "nf4"
Activate nested quantization for 4-bit base models (double quantization)
use_nested_quant = False
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TrainingArguments parameters
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Output directory where the model predictions and checkpoints will be stored
output_dir = "./results"
Number of training epochs
#num_train_epochs = 1
num_train_epochs = 4
Enable fp16/bf16 training (set bf16 to True with an A100)
fp16 = False
bf16 = False
Batch size per GPU for training
per_device_train_batch_size = 4
Batch size per GPU for evaluation
per_device_eval_batch_size = 4
Number of update steps to accumulate the gradients for
gradient_accumulation_steps = 4 #1
Enable gradient checkpointing
gradient_checkpointing = True
Maximum gradient normal (gradient clipping)
max_grad_norm = 0.3
Initial learning rate (AdamW optimizer)
learning_rate = 2e-4
Weight decay to apply to all layers except bias/LayerNorm weights
weight_decay = 0.001
Optimizer to use
optim = "paged_adamw_32bit"
Learning rate schedule
lr_scheduler_type = "cosine"
Number of training steps (overrides num_train_epochs)
max_steps = -1
Ratio of steps for a linear warmup (from 0 to learning rate)
warmup_ratio = 0.03
Group sequences into batches with same length
Saves memory and speeds up training considerably
group_by_length = True
Save checkpoint every X updates steps
save_steps = 0
Log every X updates steps
logging_steps = 25
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SFT parameters
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Maximum sequence length to use
max_seq_length = None
Pack multiple short examples in the same input sequence to increase efficiency
packing = False
Load the entire model on the GPU 0
device_map = {"": 0}
