---
library_name: peft
tags:
- generated_from_trainer
base_model: meta-llama/Llama-2-7b-hf
model-index:
- name: models/run2
  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.jsonl
    type: alpaca
dataset_prepared_path: ./resources/last_run_prepared
val_set_size: 0.05
output_dir: ./models/run2

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: run2
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/run2

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.
It achieves the following results on the evaluation set:
- Loss: 0.3248

## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 8.1402        | 0.02  | 1    | 8.4654          |
| 0.3619        | 0.3   | 20   | 0.3422          |
| 0.3432        | 0.6   | 40   | 0.3379          |
| 0.3227        | 0.9   | 60   | 0.3375          |
| 0.3315        | 1.18  | 80   | 0.3373          |
| 0.3204        | 1.48  | 100  | 0.3315          |
| 0.3291        | 1.79  | 120  | 0.3300          |
| 0.319         | 2.07  | 140  | 0.3277          |
| 0.3165        | 2.37  | 160  | 0.3280          |
| 0.3133        | 2.67  | 180  | 0.3388          |
| 0.3088        | 2.97  | 200  | 0.3263          |
| 0.3448        | 3.25  | 220  | 0.3252          |
| 0.3264        | 3.55  | 240  | 0.3273          |
| 0.2946        | 3.85  | 260  | 0.3310          |
| 0.3212        | 4.13  | 280  | 0.3244          |
| 0.3118        | 4.43  | 300  | 0.3245          |
| 0.3377        | 4.73  | 320  | 0.3248          |


### 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 results

| Model                   | Accuracy | Avg Time | Avg Cost            |
|-------------------------|----------|----------|---------------------|
| gpt-4                   | 0.874    | 0.624    | 0.00552             |
| gpt-3.5-turbo           | 0.824    | 0.530    | 0.0000916           |
| llama2-7b-ft-boolq-run2 | 0.856    | 0.0432   | 0.0000155           |

### ft vs gpt4

- Cost Improvement: 357x
- Latency Improvement: 12x

### ft vs gpt3.5-turbo

- Cost Improvement: 6x
- Latency Improvement: 14x