metadata
license: bigcode-openrail-m
base_model: bigcode/starcoder
tags:
- generated_from_trainer
model-index:
- name: peft-lora-starcoder-personal-copilot-A100-40GB-colab
results: []
library_name: peft
peft-lora-starcoder-personal-copilot-A100-40GB-colab
This model is a fine-tuned version of bigcode/starcoder on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.3627
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
The following bitsandbytes quantization config was used during training:
- 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
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_steps: 30
- training_steps: 2000
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.66 | 0.05 | 100 | 0.5844 |
| 0.6223 | 0.1 | 200 | 0.5280 |
| 0.6601 | 0.15 | 300 | 0.4819 |
| 0.5526 | 0.2 | 400 | 0.4617 |
| 0.485 | 0.25 | 500 | 0.4593 |
| 0.5239 | 0.3 | 600 | 0.4492 |
| 0.489 | 0.35 | 700 | 0.4371 |
| 0.5582 | 0.4 | 800 | 0.4362 |
| 0.4688 | 0.45 | 900 | 0.4314 |
| 0.5415 | 0.5 | 1000 | 0.4227 |
| 0.5152 | 0.55 | 1100 | 0.4121 |
| 0.5243 | 0.6 | 1200 | 0.3967 |
| 0.414 | 0.65 | 1300 | 0.3954 |
| 0.557 | 0.7 | 1400 | 0.3926 |
| 0.4144 | 0.75 | 1500 | 0.3911 |
| 0.7935 | 0.8 | 1600 | 0.3896 |
| 0.4129 | 0.85 | 1700 | 0.3866 |
| 0.4549 | 0.9 | 1800 | 0.3877 |
| 0.3903 | 0.95 | 1900 | 0.3781 |
| 0.4945 | 1.0 | 2000 | 0.3627 |
Framework versions
- PEFT 0.4.0
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3