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llama2-7b-auto-improving-boolq

PEFT
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llama
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llama2-7b-auto-improving-boolq
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---
library_name: peft
tags:
- generated_from_trainer
base_model: meta-llama/Llama-2-7b-hf
model-index:
- name: models/auto-improving-run
  results: []
---

[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
<details><summary>See axolotl config</summary>

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: "<s>"
  eos_token: "</s>"
  unk_token: "<unk>"
```

</details><br>

# 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