---
library_name: peft
tags:
- generated_from_trainer
base_model: NousResearch/Llama-2-7b-hf
model-index:
- name: qlora-out
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

[<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
base_model: NousResearch/Llama-2-7b-hf
model_type: LlamaForCausalLM
tokenizer_type: LlamaTokenizer

load_in_8bit: false
load_in_4bit: true
strict: false

datasets:
  - path: caffeinatedcherrychic/cidds-agg-balanced
    type: alpaca
dataset_prepared_path: last_run_prepared
val_set_size: 0.1
output_dir: ./qlora-out

adapter: qlora
lora_model_dir:

sequence_len: 2048
sample_packing: false
pad_to_sequence_len: true

lora_r: 32
lora_alpha: 64
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:
lora_target_modules:
  - gate_proj
  - down_proj
  - up_proj
  - q_proj
  - v_proj
  - k_proj
  - o_proj

wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
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: true

loss_watchdog_threshold: 5.0
loss_watchdog_patience: 3

max_steps: 500
warmup_steps: 10
evals_per_epoch: 4
eval_table_size:
eval_max_new_tokens: 1
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.001
fsdp:
fsdp_config:
special_tokens:

```

</details><br>

# qlora-out

This model is a fine-tuned version of [NousResearch/Llama-2-7b-hf](https://huggingface.co/NousResearch/Llama-2-7b-hf) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1998

## 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
- training_steps: 62

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 6.6299        | 0.08  | 1    | 6.9320          |
| 5.9686        | 0.32  | 4    | 4.4463          |
| 0.5956        | 0.64  | 8    | 0.5577          |
| 0.4848        | 0.96  | 12   | 0.8370          |
| 0.4913        | 1.28  | 16   | 0.4896          |
| 0.671         | 1.6   | 20   | 0.5175          |
| 2.6136        | 1.92  | 24   | 2.3446          |
| 0.6383        | 2.24  | 28   | 0.5194          |
| 0.5776        | 2.56  | 32   | 0.5653          |
| 0.4913        | 2.88  | 36   | 0.4791          |
| 0.3486        | 3.2   | 40   | 0.4041          |
| 0.4944        | 3.52  | 44   | 0.3174          |
| 0.4788        | 3.84  | 48   | 0.3952          |
| 0.3321        | 4.16  | 52   | 0.2342          |
| 0.207         | 4.48  | 56   | 0.2058          |
| 0.4502        | 4.8   | 60   | 0.1998          |


### Framework versions

- PEFT 0.10.1.dev0
- Transformers 4.39.0.dev0
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.0