---
library_name: peft
tags:
- generated_from_trainer
base_model: meta-llama/Llama-2-7b-hf
model-index:
- name: models/auto-improving-run
results: []
---
[
](https://github.com/OpenAccess-AI-Collective/axolotl)
See axolotl config
axolotl version: `0.4.0`
```yaml
# This file is used by the training script in train.ipynb. You can read more about
# the format and see more examples at https://github.com/OpenAccess-AI-Collective/axolotl.
# One of the parameters you might want to play around with is `num_epochs`: if you have a
# smaller dataset size, making that large can have good results.
base_model: meta-llama/Llama-2-7b-hf
base_model_config: meta-llama/Llama-2-7b-hf
model_type: LlamaForCausalLM
tokenizer_type: LlamaTokenizer
is_llama_derived_model: true
load_in_8bit: true
load_in_4bit: false
strict: false
datasets:
- path: ./resources/train_aug.jsonl
type: alpaca
dataset_prepared_path: ./resources/last_run_prepared
val_set_size: 0.05
output_dir: ./models/auto-improving-run
sequence_len: 4096
sample_packing: true
adapter: lora
lora_model_dir:
lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:
# This will report stats from your training run to https://wandb.ai/. If you don't want to create a wandb account you can comment this section out.
wandb_project: google-boolq
wandb_entity:
wandb_watch:
wandb_run_id: auto-improving-run
wandb_log_model:
gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 5
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0002
train_on_inputs: false
group_by_length: false
bf16: true
fp16: false
tf32: false
gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: false
warmup_steps: 10
eval_steps: 20
save_steps: 60
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
bos_token: ""
eos_token: ""
unk_token: ""
```
# models/auto-improving-run
This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the google/boolq dataset with a research platform that iterates on the model inaccuracies, gets refined by expert, and re-performs training.
It achieves the following results on the evaluation set:
- Loss: 0.3435
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 7.9638 | 0.01 | 1 | 8.3163 |
| 0.3508 | 0.28 | 20 | 0.3923 |
| 0.3166 | 0.55 | 40 | 0.3505 |
| 0.3363 | 0.83 | 60 | 0.3775 |
| 0.3295 | 1.09 | 80 | 0.3478 |
| 0.3232 | 1.36 | 100 | 0.3514 |
| 0.3569 | 1.64 | 120 | 0.3504 |
| 0.3379 | 1.92 | 140 | 0.3475 |
| 0.3234 | 2.17 | 160 | 0.3623 |
| 0.3442 | 2.45 | 180 | 0.3580 |
| 0.3103 | 2.73 | 200 | 0.3426 |
| 0.3253 | 3.0 | 220 | 0.3415 |
| 0.3291 | 3.26 | 240 | 0.3457 |
| 0.3248 | 3.54 | 260 | 0.3427 |
| 0.3463 | 3.81 | 280 | 0.3486 |
| 0.3273 | 4.07 | 300 | 0.3431 |
| 0.3071 | 4.35 | 320 | 0.3416 |
| 0.3227 | 4.62 | 340 | 0.3433 |
| 0.3333 | 4.9 | 360 | 0.3435 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.40.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.0
### Evaluation
These are the metrics reported on the test data (10% of boolq)
model='auto-improving-llama' accuracy=0.8629969418960245 avg_time=0.044935779816513415 avg_cost=1.6101987767584347e-05