peft pytorch mistral pt region:us
---
library_name: peft
language:
- pt
---
## Training procedure


The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions


- PEFT 0.4.0

This model was trained with this parameters:
```
max_seq_length = 2048

training_arguments_mistral = {
    'num_train_epochs':10,
    'per_device_train_batch_size':2,
    'gradient_accumulation_steps':2,
    'gradient_checkpointing':True,
    'optim':'adamw_torch',
    'lr_scheduler_type':'constant_with_warmup',
    'logging_steps':10,
    'evaluation_strategy':'epoch',
    'save_strategy':"epoch",
    'load_best_model_at_end':True,
    'learning_rate':4e-4,
    'save_total_limit':3,
    'fp16':True,
    'tf32': True, 
    'max_steps':8000,
    'max_grad_norm':0.3,
    'warmup_ratio':0.03,
    'disable_tqdm':False,
    'weight_decay':0.001,
    'hub_model_id':'Weni/WeniGPT-Mistral-7B-instructBase-4bit',
    'push_to_hub':True,
    'hub_strategy':'every_save',
    'hub_token':token,
    'hub_private_repo':True, 
```